DataPreprocessing3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "886b932c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ast\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(action = 'ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8856b840",
   "metadata": {},
   "source": [
    "# USER ACTIVITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8ba1079",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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>317287</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317288</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317289</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317290</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317291</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317292 rows × 6 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",
       "317287  ID_Z759BCS3   job_ID_5ONI      07:14:47              1   \n",
       "317288  ID_WNJMADF8   job_ID_5ONI      06:24:46              1   \n",
       "317289  ID_7NBY9MV2   job_ID_5ONI      11:56:35              1   \n",
       "317290  ID_LHPJJSOK   job_ID_5ONI      01:27:13              1   \n",
       "317291  ID_LCFNGARD   job_ID_5ONI      08:40:29              1   \n",
       "\n",
       "        datetime Month  datetime Day_of_month  \n",
       "0                   11                     22  \n",
       "1                   11                     23  \n",
       "2                   11                     23  \n",
       "3                   11                     23  \n",
       "4                   11                     23  \n",
       "...                ...                    ...  \n",
       "317287              11                     14  \n",
       "317288              11                     14  \n",
       "317289              11                     20  \n",
       "317290              11                     14  \n",
       "317291              11                     17  \n",
       "\n",
       "[317292 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userAct = pd.read_csv(\"UserActivity.csv\").drop_duplicates()\n",
    "df_userAct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f5d483e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 317292 entries, 0 to 317291\n",
      "Data columns (total 6 columns):\n",
      " #   Column                 Non-Null Count   Dtype \n",
      "---  ------                 --------------   ----- \n",
      " 0   User_ID                317292 non-null  object\n",
      " 1   Title                  317292 non-null  object\n",
      " 2   datetime time          317292 non-null  object\n",
      " 3   datetime Year          317292 non-null  int64 \n",
      " 4   datetime Month         317292 non-null  int64 \n",
      " 5   datetime Day_of_month  317292 non-null  int64 \n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 16.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_userAct.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a118b129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10400"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_userAct['User_ID']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b097fae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_userAct['datetime Year']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4f72dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct.drop('datetime Year', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e727b3a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     61181\n",
       "1     58292\n",
       "5     57258\n",
       "3     56323\n",
       "12    42047\n",
       "4     26962\n",
       "11    15229\n",
       "Name: datetime Month, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userAct[\"datetime Month\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "996f5e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2203"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_userAct[df_userAct['datetime Month'] == 5].User_ID.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53ef3337",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct[\"User_ID\"] = df_userAct[\"User_ID\"].astype(\"string\")\n",
    "df_userAct[\"datetime Month\"] = df_userAct[\"datetime Month\"].astype(\"string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1dd236b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct.drop(\"datetime time\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a42cbb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Viewed All Competitions            58176\n",
       "Viewed All Discussions             47172\n",
       "Viewed All Learning Pages          22930\n",
       "Downloaded Competition Datafile    19578\n",
       "Updated Profile                    14748\n",
       "                                   ...  \n",
       "comp_ID_TJK9                           1\n",
       "comp_ID_TBXU                           1\n",
       "blog_ID_NVX3                           1\n",
       "blog_ID_R97T                           1\n",
       "blog_ID_22WI                           1\n",
       "Name: Title, Length: 397, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userAct[\"Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09ee6f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df_userAct[\"datetime Month\"].copy()\n",
    "df_userAct[\"User_ID\"] = df_userAct[\"User_ID\"].str.cat(new, sep =\"_Month_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6cf63d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct.drop('datetime Month', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "93a2c424",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct[\"Title\"] = df_userAct[\"Title\"].astype(\"string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a363f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<StringArray>\n",
       "[                     'comp_ID_IV5D',                      'comp_ID_M3ST',\n",
       "                      'comp_ID_ZN92',                      'comp_ID_DD5S',\n",
       "                      'comp_ID_1261',                      'comp_ID_X0PD',\n",
       "                      'comp_ID_L3EB',                      'comp_ID_JPR9',\n",
       "                      'comp_ID_TDAE',                      'comp_ID_Q8XJ',\n",
       " ...\n",
       "                   'Votes (Up/Down)',       'Transferred Team Leadership',\n",
       "                        'badge_PLDS',                  'Changed Password',\n",
       "                      'Deleted Team', 'Accepted Team Leadership Transfer',\n",
       "  'Revoked Team Leadership Transfer',                  'Report Something',\n",
       "           'Kicked Member From Team',                         'Left Team']\n",
       "Length: 397, dtype: string"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userAct[\"Title\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46fb9c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c9862de",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c90ab218",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_id(x):\n",
    "    x = x.replace('$', '')\n",
    "    if 'ID' in x:\n",
    "        return \"_\".join(x.split('_')[:-1])\n",
    "    \n",
    "    return x.replace(' ', '_')\n",
    "\n",
    "df_userAct.Title.apply(remove_id).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b27061ec",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userAct['main_activities'] = df_userAct.Title.apply(remove_id)\n",
    "df_userAct.main_activities.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9504bf5",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "act_vc = df_userAct.main_activities.value_counts()\n",
    "selected_activities = act_vc[act_vc > 1000].keys()\n",
    "selected_activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9fa2c617",
   "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 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_Month_11</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>22</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_RT43AK77_Month_11</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>23</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_RT43AK77_Month_11</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>23</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_RT43AK77_Month_11</td>\n",
       "      <td>comp_ID_IV5D</td>\n",
       "      <td>23</td>\n",
       "      <td>comp_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_RT43AK77_Month_11</td>\n",
       "      <td>comp_ID_IV5D</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314600</th>\n",
       "      <td>ID_Z759BCS3_Month_11</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>14</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314601</th>\n",
       "      <td>ID_WNJMADF8_Month_11</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>14</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314602</th>\n",
       "      <td>ID_7NBY9MV2_Month_11</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>20</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314603</th>\n",
       "      <td>ID_LHPJJSOK_Month_11</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>14</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314604</th>\n",
       "      <td>ID_LCFNGARD_Month_11</td>\n",
       "      <td>job_ID_5ONI</td>\n",
       "      <td>17</td>\n",
       "      <td>job_ID</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314605 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     User_ID         Title  datetime Day_of_month  \\\n",
       "0       ID_RT43AK77_Month_11  comp_ID_IV5D                     22   \n",
       "1       ID_RT43AK77_Month_11  comp_ID_IV5D                     23   \n",
       "2       ID_RT43AK77_Month_11  comp_ID_IV5D                     23   \n",
       "3       ID_RT43AK77_Month_11  comp_ID_IV5D                     23   \n",
       "4       ID_RT43AK77_Month_11  comp_ID_IV5D                     23   \n",
       "...                      ...           ...                    ...   \n",
       "314600  ID_Z759BCS3_Month_11   job_ID_5ONI                     14   \n",
       "314601  ID_WNJMADF8_Month_11   job_ID_5ONI                     14   \n",
       "314602  ID_7NBY9MV2_Month_11   job_ID_5ONI                     20   \n",
       "314603  ID_LHPJJSOK_Month_11   job_ID_5ONI                     14   \n",
       "314604  ID_LCFNGARD_Month_11   job_ID_5ONI                     17   \n",
       "\n",
       "       main_activities  \n",
       "0              comp_ID  \n",
       "1              comp_ID  \n",
       "2              comp_ID  \n",
       "3              comp_ID  \n",
       "4              comp_ID  \n",
       "...                ...  \n",
       "314600          job_ID  \n",
       "314601          job_ID  \n",
       "314602          job_ID  \n",
       "314603          job_ID  \n",
       "314604          job_ID  \n",
       "\n",
       "[314605 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userAct = df_userAct[df_userAct.main_activities.isin(selected_activities)].reset_index(drop=True)\n",
    "df_userAct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b0c0512",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>User_ID</th>\n",
       "      <th>main_activities</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111314</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>Downloaded_Competition_Datafile</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111318</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>Viewed_All_Discussions</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111313</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>Confirmed_Email</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111315</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>Joined_Competition</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>111316</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>Signed_Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>Viewed_All_Competitions</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>comp_ID</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>create_alias</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>identify</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111323 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID                  main_activities  count\n",
       "111314  ID_ZZVUJ45W_Month_3  Downloaded_Competition_Datafile      3\n",
       "111318  ID_ZZVUJ45W_Month_3           Viewed_All_Discussions      2\n",
       "111313  ID_ZZVUJ45W_Month_3                  Confirmed_Email      1\n",
       "111315  ID_ZZVUJ45W_Month_3               Joined_Competition      1\n",
       "111316  ID_ZZVUJ45W_Month_3                        Signed_Up      1\n",
       "...                     ...                              ...    ...\n",
       "1       ID_000H9XOP_Month_5          Viewed_All_Competitions      1\n",
       "2       ID_000H9XOP_Month_5           Viewed_All_Discussions      1\n",
       "3       ID_000H9XOP_Month_5                          comp_ID      1\n",
       "4       ID_000H9XOP_Month_5                     create_alias      1\n",
       "5       ID_000H9XOP_Month_5                         identify      1\n",
       "\n",
       "[111323 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = df_userAct.groupby(['User_ID', 'main_activities']).size().reset_index().\\\n",
    "    rename(columns={0: 'count'}).\\\n",
    "        sort_values(['User_ID', 'count'], ascending=False)\n",
    "\n",
    "dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6db17e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.main_activities.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d248410",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_activities = dataset.main_activities.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6968f049",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZZQK0LU6_Month_3</td>\n",
       "      <td>35</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ZZFZI3DK_Month_5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_ZZ852GLE_Month_12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7411</th>\n",
       "      <td>ID_01BEG5HQ_Month_3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7412</th>\n",
       "      <td>ID_015M2NE1_Month_12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7413</th>\n",
       "      <td>ID_011YM9VH_Month_3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7414</th>\n",
       "      <td>ID_011YM9VH_Month_2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7415</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7416 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   User_ID  count\n",
       "0      ID_ZZQK0LU6_Month_3     35\n",
       "1      ID_ZZJKFBR5_Month_2      2\n",
       "2     ID_ZZJKFBR5_Month_12     24\n",
       "3      ID_ZZFZI3DK_Month_5      2\n",
       "4     ID_ZZ852GLE_Month_12      7\n",
       "...                    ...    ...\n",
       "7411   ID_01BEG5HQ_Month_3      8\n",
       "7412  ID_015M2NE1_Month_12      1\n",
       "7413   ID_011YM9VH_Month_3      4\n",
       "7414   ID_011YM9VH_Month_2      1\n",
       "7415   ID_000H9XOP_Month_5      1\n",
       "\n",
       "[7416 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.main_activities == \"comp_ID\"].drop(columns='main_activities').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc0e775d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for activity in all_activities:\n",
    "    dataset[activity] = np.zeros((dataset.shape[0], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aeb34fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>111314</th>\n",
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       "      <th>111318</th>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>111316</th>\n",
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       "      <td>1</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>...</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>2</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>Viewed_All_Discussions</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>comp_ID</td>\n",
       "      <td>1</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>...</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>4</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>create_alias</td>\n",
       "      <td>1</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>...</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>5</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>identify</td>\n",
       "      <td>1</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>...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>111323 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID                  main_activities  count  \\\n",
       "111314  ID_ZZVUJ45W_Month_3  Downloaded_Competition_Datafile      3   \n",
       "111318  ID_ZZVUJ45W_Month_3           Viewed_All_Discussions      2   \n",
       "111313  ID_ZZVUJ45W_Month_3                  Confirmed_Email      1   \n",
       "111315  ID_ZZVUJ45W_Month_3               Joined_Competition      1   \n",
       "111316  ID_ZZVUJ45W_Month_3                        Signed_Up      1   \n",
       "...                     ...                              ...    ...   \n",
       "1       ID_000H9XOP_Month_5          Viewed_All_Competitions      1   \n",
       "2       ID_000H9XOP_Month_5           Viewed_All_Discussions      1   \n",
       "3       ID_000H9XOP_Month_5                          comp_ID      1   \n",
       "4       ID_000H9XOP_Month_5                     create_alias      1   \n",
       "5       ID_000H9XOP_Month_5                         identify      1   \n",
       "\n",
       "        Downloaded_Competition_Datafile  Viewed_All_Discussions  \\\n",
       "111314                              0.0                     0.0   \n",
       "111318                              0.0                     0.0   \n",
       "111313                              0.0                     0.0   \n",
       "111315                              0.0                     0.0   \n",
       "111316                              0.0                     0.0   \n",
       "...                                 ...                     ...   \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",
       "5                                   0.0                     0.0   \n",
       "\n",
       "        Confirmed_Email  Joined_Competition  Signed_Up  \\\n",
       "111314              0.0                 0.0        0.0   \n",
       "111318              0.0                 0.0        0.0   \n",
       "111313              0.0                 0.0        0.0   \n",
       "111315              0.0                 0.0        0.0   \n",
       "111316              0.0                 0.0        0.0   \n",
       "...                 ...                 ...        ...   \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",
       "5                   0.0                 0.0        0.0   \n",
       "\n",
       "        Viewed_All_Competitions  Viewed_All_Learning_Pages  ...  job_ID  \\\n",
       "111314                      0.0                        0.0  ...     0.0   \n",
       "111318                      0.0                        0.0  ...     0.0   \n",
       "111313                      0.0                        0.0  ...     0.0   \n",
       "111315                      0.0                        0.0  ...     0.0   \n",
       "111316                      0.0                        0.0  ...     0.0   \n",
       "...                         ...                        ...  ...     ...   \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",
       "5                           0.0                        0.0  ...     0.0   \n",
       "\n",
       "        Updated_Discussion  Viewed_All_Jobs  Created_Submission  Signed_Out  \\\n",
       "111314                 0.0              0.0                 0.0         0.0   \n",
       "111318                 0.0              0.0                 0.0         0.0   \n",
       "111313                 0.0              0.0                 0.0         0.0   \n",
       "111315                 0.0              0.0                 0.0         0.0   \n",
       "111316                 0.0              0.0                 0.0         0.0   \n",
       "...                    ...              ...                 ...         ...   \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                 0.0         0.0   \n",
       "5                      0.0              0.0                 0.0         0.0   \n",
       "\n",
       "        Updated_Profile  blog_ID  Viewed_Discussion  Updated_Submission  \\\n",
       "111314              0.0      0.0                0.0                 0.0   \n",
       "111318              0.0      0.0                0.0                 0.0   \n",
       "111313              0.0      0.0                0.0                 0.0   \n",
       "111315              0.0      0.0                0.0                 0.0   \n",
       "111316              0.0      0.0                0.0                 0.0   \n",
       "...                 ...      ...                ...                 ...   \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                0.0                 0.0   \n",
       "5                   0.0      0.0                0.0                 0.0   \n",
       "\n",
       "        badge_HYIO  \n",
       "111314         0.0  \n",
       "111318         0.0  \n",
       "111313         0.0  \n",
       "111315         0.0  \n",
       "111316         0.0  \n",
       "...            ...  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              0.0  \n",
       "5              0.0  \n",
       "\n",
       "[111323 rows x 25 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a62c733e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(dataset)):\n",
    "    for activity in all_activities:\n",
    "        if dataset.loc[i, \"main_activities\"] == activity:\n",
    "            dataset.loc[i, activity] = dataset.loc[i, \"count\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3cb59242",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.groupby(\"User_ID\").sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "298b7bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(\"count\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c3f9523f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15139 entries, 0 to 15138\n",
      "Data columns (total 23 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   User_ID                          15139 non-null  object \n",
      " 1   Downloaded_Competition_Datafile  15139 non-null  float64\n",
      " 2   Viewed_All_Discussions           15139 non-null  float64\n",
      " 3   Confirmed_Email                  15139 non-null  float64\n",
      " 4   Joined_Competition               15139 non-null  float64\n",
      " 5   Signed_Up                        15139 non-null  float64\n",
      " 6   Viewed_All_Competitions          15139 non-null  float64\n",
      " 7   Viewed_All_Learning_Pages        15139 non-null  float64\n",
      " 8   badge_OCZE                       15139 non-null  float64\n",
      " 9   create_alias                     15139 non-null  float64\n",
      " 10  identify                         15139 non-null  float64\n",
      " 11  comp_ID                          15139 non-null  float64\n",
      " 12  Signed_In                        15139 non-null  float64\n",
      " 13  job_ID                           15139 non-null  float64\n",
      " 14  Updated_Discussion               15139 non-null  float64\n",
      " 15  Viewed_All_Jobs                  15139 non-null  float64\n",
      " 16  Created_Submission               15139 non-null  float64\n",
      " 17  Signed_Out                       15139 non-null  float64\n",
      " 18  Updated_Profile                  15139 non-null  float64\n",
      " 19  blog_ID                          15139 non-null  float64\n",
      " 20  Viewed_Discussion                15139 non-null  float64\n",
      " 21  Updated_Submission               15139 non-null  float64\n",
      " 22  badge_HYIO                       15139 non-null  float64\n",
      "dtypes: float64(22), object(1)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d566f32d",
   "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",
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       "      <th>create_alias</th>\n",
       "      <th>...</th>\n",
       "      <th>job_ID</th>\n",
       "      <th>Updated_Discussion</th>\n",
       "      <th>Viewed_All_Jobs</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_000H9XOP_Month_5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_003YJHQA_Month_4</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",
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       "      <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>2</th>\n",
       "      <td>ID_004UKSJV_Month_1</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>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>3</th>\n",
       "      <td>ID_004UKSJV_Month_12</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>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>4.0</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_008D2J6C_Month_1</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",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15134</th>\n",
       "      <td>ID_ZZJKFBR5_Month_2</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",
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       "      <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>15135</th>\n",
       "      <td>ID_ZZMHNLNT_Month_4</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>15136</th>\n",
       "      <td>ID_ZZQK0LU6_Month_3</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",
       "      <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>15137</th>\n",
       "      <td>ID_ZZUNZJ2U_Month_2</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>3.0</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>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>15138</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>15139 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID  Downloaded_Competition_Datafile  \\\n",
       "0       ID_000H9XOP_Month_5                              0.0   \n",
       "1       ID_003YJHQA_Month_4                              0.0   \n",
       "2       ID_004UKSJV_Month_1                              0.0   \n",
       "3      ID_004UKSJV_Month_12                              0.0   \n",
       "4       ID_008D2J6C_Month_1                              0.0   \n",
       "...                     ...                              ...   \n",
       "15134   ID_ZZJKFBR5_Month_2                              0.0   \n",
       "15135   ID_ZZMHNLNT_Month_4                              0.0   \n",
       "15136   ID_ZZQK0LU6_Month_3                              7.0   \n",
       "15137   ID_ZZUNZJ2U_Month_2                              0.0   \n",
       "15138   ID_ZZVUJ45W_Month_3                              3.0   \n",
       "\n",
       "       Viewed_All_Discussions  Confirmed_Email  Joined_Competition  Signed_Up  \\\n",
       "0                         1.0              0.0                 0.0        1.0   \n",
       "1                         2.0              1.0                 0.0        1.0   \n",
       "2                         0.0              0.0                 0.0        0.0   \n",
       "3                         1.0              1.0                 0.0        1.0   \n",
       "4                         1.0              1.0                 0.0        1.0   \n",
       "...                       ...              ...                 ...        ...   \n",
       "15134                     0.0              0.0                 0.0        0.0   \n",
       "15135                     1.0              1.0                 0.0        1.0   \n",
       "15136                     4.0              1.0                 2.0        1.0   \n",
       "15137                     1.0              1.0                 0.0        1.0   \n",
       "15138                     2.0              1.0                 1.0        1.0   \n",
       "\n",
       "       Viewed_All_Competitions  Viewed_All_Learning_Pages  badge_OCZE  \\\n",
       "0                          1.0                        0.0         0.0   \n",
       "1                          1.0                        1.0         1.0   \n",
       "2                          0.0                        0.0         1.0   \n",
       "3                          1.0                        1.0         0.0   \n",
       "4                          1.0                        1.0         1.0   \n",
       "...                        ...                        ...         ...   \n",
       "15134                      0.0                        0.0         0.0   \n",
       "15135                      1.0                        1.0         1.0   \n",
       "15136                     14.0                        1.0         1.0   \n",
       "15137                      3.0                        3.0         1.0   \n",
       "15138                      1.0                        1.0         1.0   \n",
       "\n",
       "       create_alias  ...  job_ID  Updated_Discussion  Viewed_All_Jobs  \\\n",
       "0               1.0  ...     0.0                 0.0              0.0   \n",
       "1               1.0  ...     0.0                 0.0              0.0   \n",
       "2               0.0  ...     0.0                 0.0              0.0   \n",
       "3               1.0  ...     0.0                 0.0              0.0   \n",
       "4               1.0  ...     0.0                 0.0              0.0   \n",
       "...             ...  ...     ...                 ...              ...   \n",
       "15134           0.0  ...     0.0                 0.0              0.0   \n",
       "15135           1.0  ...     0.0                 0.0              0.0   \n",
       "15136           0.0  ...     0.0                 0.0              0.0   \n",
       "15137           1.0  ...     0.0                 0.0              0.0   \n",
       "15138           1.0  ...     0.0                 0.0              0.0   \n",
       "\n",
       "       Created_Submission  Signed_Out  Updated_Profile  blog_ID  \\\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         0.0              4.0      0.0   \n",
       "4                     0.0         0.0              2.0      0.0   \n",
       "...                   ...         ...              ...      ...   \n",
       "15134                 0.0         0.0              0.0      0.0   \n",
       "15135                 0.0         0.0              0.0      0.0   \n",
       "15136                 0.0         0.0              0.0      0.0   \n",
       "15137                 0.0         0.0              0.0      0.0   \n",
       "15138                 0.0         0.0              0.0      0.0   \n",
       "\n",
       "       Viewed_Discussion  Updated_Submission  badge_HYIO  \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",
       "15134                0.0                 0.0         0.0  \n",
       "15135                0.0                 0.0         0.0  \n",
       "15136                0.0                 0.0         0.0  \n",
       "15137                0.0                 0.0         0.0  \n",
       "15138                0.0                 0.0         0.0  \n",
       "\n",
       "[15139 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "08ce9dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct = pd.merge(df_userAct, dataset, how=\"left\", on=['User_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04b89deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct.drop(\"Title\", inplace=True, axis=1)\n",
    "df_userAct.drop(\"main_activities\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dfed0338",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct.rename(columns={'Theme': 'Blog Theme'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4d0aedd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 314605 entries, 0 to 314604\n",
      "Data columns (total 24 columns):\n",
      " #   Column                           Non-Null Count   Dtype  \n",
      "---  ------                           --------------   -----  \n",
      " 0   User_ID                          314605 non-null  object \n",
      " 1   datetime Day_of_month            314605 non-null  int64  \n",
      " 2   Downloaded_Competition_Datafile  314605 non-null  float64\n",
      " 3   Viewed_All_Discussions           314605 non-null  float64\n",
      " 4   Confirmed_Email                  314605 non-null  float64\n",
      " 5   Joined_Competition               314605 non-null  float64\n",
      " 6   Signed_Up                        314605 non-null  float64\n",
      " 7   Viewed_All_Competitions          314605 non-null  float64\n",
      " 8   Viewed_All_Learning_Pages        314605 non-null  float64\n",
      " 9   badge_OCZE                       314605 non-null  float64\n",
      " 10  create_alias                     314605 non-null  float64\n",
      " 11  identify                         314605 non-null  float64\n",
      " 12  comp_ID                          314605 non-null  float64\n",
      " 13  Signed_In                        314605 non-null  float64\n",
      " 14  job_ID                           314605 non-null  float64\n",
      " 15  Updated_Discussion               314605 non-null  float64\n",
      " 16  Viewed_All_Jobs                  314605 non-null  float64\n",
      " 17  Created_Submission               314605 non-null  float64\n",
      " 18  Signed_Out                       314605 non-null  float64\n",
      " 19  Updated_Profile                  314605 non-null  float64\n",
      " 20  blog_ID                          314605 non-null  float64\n",
      " 21  Viewed_Discussion                314605 non-null  float64\n",
      " 22  Updated_Submission               314605 non-null  float64\n",
      " 23  badge_HYIO                       314605 non-null  float64\n",
      "dtypes: float64(22), int64(1), object(1)\n",
      "memory usage: 60.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_userAct.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ee896afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp1 = df_userAct.groupby('User_ID').max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f89f49fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp2 = df_userAct.groupby('User_ID').mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "78b0c80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp3 = df_userAct.groupby('User_ID').count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3889682c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp4 = df_userAct.groupby('User_ID').min() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b7596df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = df_temp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "458de006",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Last day active\n",
    "df_temp[\"datetime Day_of_month\"] = df_temp1[\"datetime Day_of_month\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2f036e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp[\"count_UA\"] = df_temp3[\"datetime Day_of_month\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ed925960",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp[\"Day duration\"] = df_temp1[\"datetime Day_of_month\"] - df_temp4[\"datetime Day_of_month\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db2e425",
   "metadata": {},
   "source": [
    "Will add 1 to \"day duration\" after merging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3e1a9f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct = df_temp "
   ]
  },
  {
   "cell_type": "raw",
   "id": "de727517",
   "metadata": {},
   "source": [
    "df_userAct.sort_values('User_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b14e9f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_userAct = df_userAct.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3d647748",
   "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>datetime Day_of_month</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>...</th>\n",
       "      <th>Viewed_All_Jobs</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",
       "      <th>count_UA</th>\n",
       "      <th>Day duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>26</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>0.0</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>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_003YJHQA_Month_4</td>\n",
       "      <td>12</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>...</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>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_004UKSJV_Month_1</td>\n",
       "      <td>15</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>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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_004UKSJV_Month_12</td>\n",
       "      <td>29</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <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>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_008D2J6C_Month_1</td>\n",
       "      <td>15</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>...</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>10</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",
       "      <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>15134</th>\n",
       "      <td>ID_ZZJKFBR5_Month_2</td>\n",
       "      <td>20</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>...</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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15135</th>\n",
       "      <td>ID_ZZMHNLNT_Month_4</td>\n",
       "      <td>1</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>...</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>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15136</th>\n",
       "      <td>ID_ZZQK0LU6_Month_3</td>\n",
       "      <td>30</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>...</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>67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15137</th>\n",
       "      <td>ID_ZZUNZJ2U_Month_2</td>\n",
       "      <td>30</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>3.0</td>\n",
       "      <td>3.0</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>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15138</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</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>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>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15139 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID  datetime Day_of_month  \\\n",
       "0       ID_000H9XOP_Month_5                     26   \n",
       "1       ID_003YJHQA_Month_4                     12   \n",
       "2       ID_004UKSJV_Month_1                     15   \n",
       "3      ID_004UKSJV_Month_12                     29   \n",
       "4       ID_008D2J6C_Month_1                     15   \n",
       "...                     ...                    ...   \n",
       "15134   ID_ZZJKFBR5_Month_2                     20   \n",
       "15135   ID_ZZMHNLNT_Month_4                      1   \n",
       "15136   ID_ZZQK0LU6_Month_3                     30   \n",
       "15137   ID_ZZUNZJ2U_Month_2                     30   \n",
       "15138   ID_ZZVUJ45W_Month_3                      5   \n",
       "\n",
       "       Downloaded_Competition_Datafile  Viewed_All_Discussions  \\\n",
       "0                                  0.0                     1.0   \n",
       "1                                  0.0                     2.0   \n",
       "2                                  0.0                     0.0   \n",
       "3                                  0.0                     1.0   \n",
       "4                                  0.0                     1.0   \n",
       "...                                ...                     ...   \n",
       "15134                              0.0                     0.0   \n",
       "15135                              0.0                     1.0   \n",
       "15136                              7.0                     4.0   \n",
       "15137                              0.0                     1.0   \n",
       "15138                              3.0                     2.0   \n",
       "\n",
       "       Confirmed_Email  Joined_Competition  Signed_Up  \\\n",
       "0                  0.0                 0.0        1.0   \n",
       "1                  1.0                 0.0        1.0   \n",
       "2                  0.0                 0.0        0.0   \n",
       "3                  1.0                 0.0        1.0   \n",
       "4                  1.0                 0.0        1.0   \n",
       "...                ...                 ...        ...   \n",
       "15134              0.0                 0.0        0.0   \n",
       "15135              1.0                 0.0        1.0   \n",
       "15136              1.0                 2.0        1.0   \n",
       "15137              1.0                 0.0        1.0   \n",
       "15138              1.0                 1.0        1.0   \n",
       "\n",
       "       Viewed_All_Competitions  Viewed_All_Learning_Pages  badge_OCZE  ...  \\\n",
       "0                          1.0                        0.0         0.0  ...   \n",
       "1                          1.0                        1.0         1.0  ...   \n",
       "2                          0.0                        0.0         1.0  ...   \n",
       "3                          1.0                        1.0         0.0  ...   \n",
       "4                          1.0                        1.0         1.0  ...   \n",
       "...                        ...                        ...         ...  ...   \n",
       "15134                      0.0                        0.0         0.0  ...   \n",
       "15135                      1.0                        1.0         1.0  ...   \n",
       "15136                     14.0                        1.0         1.0  ...   \n",
       "15137                      3.0                        3.0         1.0  ...   \n",
       "15138                      1.0                        1.0         1.0  ...   \n",
       "\n",
       "       Viewed_All_Jobs  Created_Submission  Signed_Out  Updated_Profile  \\\n",
       "0                  0.0                 0.0         0.0              0.0   \n",
       "1                  0.0                 0.0         0.0              2.0   \n",
       "2                  0.0                 0.0         0.0              0.0   \n",
       "3                  0.0                 0.0         0.0              4.0   \n",
       "4                  0.0                 0.0         0.0              2.0   \n",
       "...                ...                 ...         ...              ...   \n",
       "15134              0.0                 0.0         0.0              0.0   \n",
       "15135              0.0                 0.0         0.0              0.0   \n",
       "15136              0.0                 0.0         0.0              0.0   \n",
       "15137              0.0                 0.0         0.0              0.0   \n",
       "15138              0.0                 0.0         0.0              0.0   \n",
       "\n",
       "       blog_ID  Viewed_Discussion  Updated_Submission  badge_HYIO  count_UA  \\\n",
       "0          0.0                0.0                 0.0         0.0         6   \n",
       "1          0.0                0.0                 0.0         0.0        11   \n",
       "2          0.0                0.0                 0.0         0.0         1   \n",
       "3          0.0                0.0                 0.0         0.0        11   \n",
       "4          0.0                0.0                 0.0         0.0        10   \n",
       "...        ...                ...                 ...         ...       ...   \n",
       "15134      0.0                0.0                 0.0         0.0         2   \n",
       "15135      0.0                0.0                 0.0         0.0         8   \n",
       "15136      0.0                0.0                 0.0         0.0        67   \n",
       "15137      0.0                0.0                 0.0         0.0        12   \n",
       "15138      0.0                0.0                 0.0         0.0        13   \n",
       "\n",
       "       Day duration  \n",
       "0                 4  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 1  \n",
       "4                 8  \n",
       "...             ...  \n",
       "15134             0  \n",
       "15135             0  \n",
       "15136             1  \n",
       "15137             0  \n",
       "15138             0  \n",
       "\n",
       "[15139 rows x 26 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_userAct"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1583267",
   "metadata": {},
   "source": [
    "# COMPETITIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5a8227ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = pd.read_csv(\"Competition.csv\").drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d1a71db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 247 entries, 0 to 246\n",
      "Data columns (total 20 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   FeatureA                 247 non-null    object \n",
      " 1   FeatureB                 247 non-null    object \n",
      " 2   FeatureC                 196 non-null    float64\n",
      " 3   FeatureD                 247 non-null    int64  \n",
      " 4   FeatureE                 247 non-null    int64  \n",
      " 5   SecretCode               247 non-null    int64  \n",
      " 6   FeatureF                 247 non-null    object \n",
      " 7   FeatureG                 247 non-null    object \n",
      " 8   FeatureH                 247 non-null    object \n",
      " 9   FeatureI                 186 non-null    float64\n",
      " 10  Country_ID               121 non-null    object \n",
      " 11  Comp_ID                  247 non-null    object \n",
      " 12  End Time time            224 non-null    object \n",
      " 13  End Time Year            224 non-null    float64\n",
      " 14  End Time Month           224 non-null    float64\n",
      " 15  End Time Day_of_month    224 non-null    float64\n",
      " 16  Start Time time          247 non-null    object \n",
      " 17  Start Time Year          247 non-null    int64  \n",
      " 18  Start Time Month         247 non-null    int64  \n",
      " 19  Start Time Day_of_month  247 non-null    int64  \n",
      "dtypes: float64(5), int64(6), object(9)\n",
      "memory usage: 40.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_comp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bc6e19e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>FeatureE</th>\n",
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       "      <th>End Time Month</th>\n",
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       "      <th>Start Time Year</th>\n",
       "      <th>Start Time Month</th>\n",
       "      <th>Start Time Day_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>[2, 4]</td>\n",
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       "      <td>[3]</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>G3F0</td>\n",
       "      <td>ID_AU0T</td>\n",
       "      <td>21:05:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0Q35</td>\n",
       "      <td>ID_O21T</td>\n",
       "      <td>15:30:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1]</td>\n",
       "      <td>[9]</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>[1, 5]</td>\n",
       "      <td>[5]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>EGI9</td>\n",
       "      <td>ID_W6XE</td>\n",
       "      <td>21:59:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[4]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_A2FN</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "      <td>[6]</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[4]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_X0PD</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
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       "      <td>[]</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[5]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_GR92</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>[1]</td>\n",
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       "      <td>[1]</td>\n",
       "      <td>[5]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_8BH9</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>[1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>H03V</td>\n",
       "      <td>ID_IHRZ</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>08:57:00</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>[1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[5]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EGI9</td>\n",
       "      <td>ID_CMWO</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>[1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ZEWU</td>\n",
       "      <td>ID_OAR6</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>247 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FeatureA FeatureB  FeatureC  FeatureD  FeatureE  SecretCode FeatureF  \\\n",
       "0     [2, 4]     [14]      15.0         0         2           1      [3]   \n",
       "1        [1]       []       3.0         0         3           1      [1]   \n",
       "2        [1]      [9]       2.0         0         2           1   [1, 5]   \n",
       "3         []       []       1.0         0         2           1      [1]   \n",
       "4         []      [6]       NaN         0         2           0       []   \n",
       "..       ...      ...       ...       ...       ...         ...      ...   \n",
       "242       []       []       NaN         1         2           0      [2]   \n",
       "243      [1]       []       2.0         1         2           0      [1]   \n",
       "244      [1]       []       NaN         0         2           1      [1]   \n",
       "245      [1]       []      25.0         0         2           1      [1]   \n",
       "246      [1]       []       2.0         0         2           0      [3]   \n",
       "\n",
       "    FeatureG FeatureH  FeatureI Country_ID  Comp_ID End Time time  \\\n",
       "0         []       []       NaN       G3F0  ID_AU0T      21:05:00   \n",
       "1         []      [1]       3.0       0Q35  ID_O21T      15:30:00   \n",
       "2        [5]      [1]       2.0       EGI9  ID_W6XE      21:59:00   \n",
       "3        [4]       []       NaN        NaN  ID_A2FN      12:00:00   \n",
       "4        [4]      [2]       9.0        NaN  ID_X0PD      23:59:00   \n",
       "..       ...      ...       ...        ...      ...           ...   \n",
       "242      [5]      [2]       NaN        NaN  ID_GR92      15:00:00   \n",
       "243      [5]      [1]       2.0        NaN  ID_8BH9      23:59:00   \n",
       "244      [3]       []       NaN       H03V  ID_IHRZ      17:00:00   \n",
       "245      [5]      [2]       NaN       EGI9  ID_CMWO      12:00:00   \n",
       "246      [3]      [2]       2.0       ZEWU  ID_OAR6      23:59:00   \n",
       "\n",
       "     End Time Year  End Time Month  End Time Day_of_month Start Time time  \\\n",
       "0              2.0             6.0                   19.0        07:00:00   \n",
       "1              2.0             2.0                   15.0        12:00:00   \n",
       "2              2.0             5.0                    9.0        08:00:00   \n",
       "3              2.0             6.0                   22.0        12:00:00   \n",
       "4              2.0             5.0                   31.0        09:00:00   \n",
       "..             ...             ...                    ...             ...   \n",
       "242            1.0             2.0                    1.0        05:00:00   \n",
       "243            1.0             6.0                   27.0        15:00:00   \n",
       "244            1.0            10.0                   20.0        08:57:00   \n",
       "245            1.0             5.0                   31.0        12:00:00   \n",
       "246            1.0             4.0                    4.0        11:00:00   \n",
       "\n",
       "     Start Time Year  Start Time Month  Start Time Day_of_month  \n",
       "0                  2                 6                       19  \n",
       "1                  2                 2                        1  \n",
       "2                  2                 3                       28  \n",
       "3                  2                 6                       12  \n",
       "4                  2                 5                       27  \n",
       "..               ...               ...                      ...  \n",
       "242                1                 1                        1  \n",
       "243                1                 6                        1  \n",
       "244                1                10                       19  \n",
       "245                1                 5                        1  \n",
       "246                1                 4                        2  \n",
       "\n",
       "[247 rows x 20 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "de6978c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "247"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_comp['Comp_ID']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd89891d",
   "metadata": {},
   "source": [
    "# COMPETITION PARTICIPATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "901743eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_compP = pd.read_csv(\"CompetitionPartipation.csv\").drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1d6b3db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8385 entries, 0 to 8384\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   Competition ID               8385 non-null   object\n",
      " 1   Participant Type             8385 non-null   int64 \n",
      " 2   Successful Submission Count  1449 non-null   object\n",
      " 3   User_ID                      8385 non-null   object\n",
      " 4   Created At time              8385 non-null   object\n",
      " 5   Created At Year              8385 non-null   int64 \n",
      " 6   Created At Month             8385 non-null   int64 \n",
      " 7   Created At Day_of_month      8385 non-null   int64 \n",
      "dtypes: int64(4), object(4)\n",
      "memory usage: 589.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_compP.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "86de401c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Competition ID</th>\n",
       "      <th>Participant Type</th>\n",
       "      <th>Successful Submission Count</th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Created At time</th>\n",
       "      <th>Created At Year</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_WPP3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV</td>\n",
       "      <td>19:38:04.689865</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZN92</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV</td>\n",
       "      <td>19:38:04.670583</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_I9BN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV</td>\n",
       "      <td>12:06:55.884320</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_PDKH</td>\n",
       "      <td>1</td>\n",
       "      <td>count 10</td>\n",
       "      <td>ID_40G01FGV</td>\n",
       "      <td>11:24:04.239058</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_CFN7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_P33ABQX5</td>\n",
       "      <td>05:16:09.052129</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>26</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8380</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_QR2HEY6L</td>\n",
       "      <td>22:10:28.532519</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8381</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_K1MDGNKE</td>\n",
       "      <td>20:54:47.902303</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8382</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_M8FQBYIH</td>\n",
       "      <td>18:57:10.479540</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8383</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_9IP2OICN</td>\n",
       "      <td>21:03:14.798218</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8384</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_JUE09Z86</td>\n",
       "      <td>11:02:22.739630</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8385 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Competition ID  Participant Type Successful Submission Count  \\\n",
       "0           ID_WPP3                 1                         NaN   \n",
       "1           ID_ZN92                 1                         NaN   \n",
       "2           ID_I9BN                 1                         NaN   \n",
       "3           ID_PDKH                 1                    count 10   \n",
       "4           ID_CFN7                 1                         NaN   \n",
       "...             ...               ...                         ...   \n",
       "8380        ID_F7QD                 1                         NaN   \n",
       "8381        ID_F7QD                 1                         NaN   \n",
       "8382        ID_F7QD                 1                         NaN   \n",
       "8383        ID_F7QD                 1                         NaN   \n",
       "8384        ID_F7QD                 1                         NaN   \n",
       "\n",
       "          User_ID  Created At time  Created At Year  Created At Month  \\\n",
       "0     ID_40G01FGV  19:38:04.689865                1                11   \n",
       "1     ID_40G01FGV  19:38:04.670583                1                11   \n",
       "2     ID_40G01FGV  12:06:55.884320                1                12   \n",
       "3     ID_40G01FGV  11:24:04.239058                1                12   \n",
       "4     ID_P33ABQX5  05:16:09.052129                1                 5   \n",
       "...           ...              ...              ...               ...   \n",
       "8380  ID_QR2HEY6L  22:10:28.532519                1                 1   \n",
       "8381  ID_K1MDGNKE  20:54:47.902303                1                 3   \n",
       "8382  ID_M8FQBYIH  18:57:10.479540                1                12   \n",
       "8383  ID_9IP2OICN  21:03:14.798218                1                12   \n",
       "8384  ID_JUE09Z86  11:02:22.739630                1                 5   \n",
       "\n",
       "      Created At Day_of_month  \n",
       "0                          20  \n",
       "1                          20  \n",
       "2                          17  \n",
       "3                           6  \n",
       "4                          26  \n",
       "...                       ...  \n",
       "8380                       15  \n",
       "8381                        1  \n",
       "8382                       27  \n",
       "8383                       13  \n",
       "8384                        5  \n",
       "\n",
       "[8385 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_compP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "81bf4f33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5245"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_compP['User_ID']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "26c84ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_compP['Created At Year']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6084626a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_compP.drop('Created At Year', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "35437760",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_compP.rename(columns={'Competition ID': 'Comp_ID'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6df35171",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_compP[\"User_ID\"] = df_compP[\"User_ID\"].astype(\"string\")\n",
    "df_compP[\"Created At Month\"] = df_compP[\"Created At Month\"].astype(\"string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0591ca7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df_compP[\"Created At Month\"].copy()\n",
    "df_compP[\"User_ID\"] = df_compP[\"User_ID\"].str.cat(new, sep =\"_Month_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4be9aab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_compP.drop('Created At Month', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "be3d24ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_compP.rename(columns={'Created At time': 'Comp_participation_time'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "75488f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_compP.rename(columns={'Created At Day_of_month': 'Comp_participation_day'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f43dfb68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comp_ID</th>\n",
       "      <th>Participant Type</th>\n",
       "      <th>Successful Submission Count</th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Created At time</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>19:38:04.689865</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZN92</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>19:38:04.670583</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_I9BN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_12</td>\n",
       "      <td>12:06:55.884320</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_PDKH</td>\n",
       "      <td>1</td>\n",
       "      <td>count 10</td>\n",
       "      <td>ID_40G01FGV_Month_12</td>\n",
       "      <td>11:24:04.239058</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_CFN7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_P33ABQX5_Month_5</td>\n",
       "      <td>05:16:09.052129</td>\n",
       "      <td>26</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>8380</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_QR2HEY6L_Month_1</td>\n",
       "      <td>22:10:28.532519</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8381</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_K1MDGNKE_Month_3</td>\n",
       "      <td>20:54:47.902303</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8382</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_M8FQBYIH_Month_12</td>\n",
       "      <td>18:57:10.479540</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8383</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_9IP2OICN_Month_12</td>\n",
       "      <td>21:03:14.798218</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8384</th>\n",
       "      <td>ID_F7QD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_JUE09Z86_Month_5</td>\n",
       "      <td>11:02:22.739630</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8385 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Comp_ID  Participant Type Successful Submission Count  \\\n",
       "0     ID_WPP3                 1                         NaN   \n",
       "1     ID_ZN92                 1                         NaN   \n",
       "2     ID_I9BN                 1                         NaN   \n",
       "3     ID_PDKH                 1                    count 10   \n",
       "4     ID_CFN7                 1                         NaN   \n",
       "...       ...               ...                         ...   \n",
       "8380  ID_F7QD                 1                         NaN   \n",
       "8381  ID_F7QD                 1                         NaN   \n",
       "8382  ID_F7QD                 1                         NaN   \n",
       "8383  ID_F7QD                 1                         NaN   \n",
       "8384  ID_F7QD                 1                         NaN   \n",
       "\n",
       "                   User_ID  Created At time  Comp_participation_day  \n",
       "0     ID_40G01FGV_Month_11  19:38:04.689865                      20  \n",
       "1     ID_40G01FGV_Month_11  19:38:04.670583                      20  \n",
       "2     ID_40G01FGV_Month_12  12:06:55.884320                      17  \n",
       "3     ID_40G01FGV_Month_12  11:24:04.239058                       6  \n",
       "4      ID_P33ABQX5_Month_5  05:16:09.052129                      26  \n",
       "...                    ...              ...                     ...  \n",
       "8380   ID_QR2HEY6L_Month_1  22:10:28.532519                      15  \n",
       "8381   ID_K1MDGNKE_Month_3  20:54:47.902303                       1  \n",
       "8382  ID_M8FQBYIH_Month_12  18:57:10.479540                      27  \n",
       "8383  ID_9IP2OICN_Month_12  21:03:14.798218                      13  \n",
       "8384   ID_JUE09Z86_Month_5  11:02:22.739630                       5  \n",
       "\n",
       "[8385 rows x 6 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_compP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b75814",
   "metadata": {},
   "source": [
    "# MERGE 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f46842f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = pd.merge(df_compP, df_comp, how=\"outer\", on= 'Comp_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "421d0c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = result1[result1['User_ID'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5b4488e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8385 entries, 0 to 8384\n",
      "Data columns (total 25 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Comp_ID                      8385 non-null   object \n",
      " 1   Participant Type             8385 non-null   float64\n",
      " 2   Successful Submission Count  1449 non-null   object \n",
      " 3   User_ID                      8385 non-null   string \n",
      " 4   Created At time              8385 non-null   object \n",
      " 5   Comp_participation_day       8385 non-null   float64\n",
      " 6   FeatureA                     8385 non-null   object \n",
      " 7   FeatureB                     8385 non-null   object \n",
      " 8   FeatureC                     5655 non-null   float64\n",
      " 9   FeatureD                     8385 non-null   int64  \n",
      " 10  FeatureE                     8385 non-null   int64  \n",
      " 11  SecretCode                   8385 non-null   int64  \n",
      " 12  FeatureF                     8385 non-null   object \n",
      " 13  FeatureG                     8385 non-null   object \n",
      " 14  FeatureH                     8385 non-null   object \n",
      " 15  FeatureI                     7206 non-null   float64\n",
      " 16  Country_ID                   4073 non-null   object \n",
      " 17  End Time time                6340 non-null   object \n",
      " 18  End Time Year                6340 non-null   float64\n",
      " 19  End Time Month               6340 non-null   float64\n",
      " 20  End Time Day_of_month        6340 non-null   float64\n",
      " 21  Start Time time              8385 non-null   object \n",
      " 22  Start Time Year              8385 non-null   int64  \n",
      " 23  Start Time Month             8385 non-null   int64  \n",
      " 24  Start Time Day_of_month      8385 non-null   int64  \n",
      "dtypes: float64(7), int64(6), object(11), string(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "result1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cdd809f8",
   "metadata": {
    "scrolled": false
   },
   "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>Comp_ID</th>\n",
       "      <th>Participant Type</th>\n",
       "      <th>Successful Submission Count</th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Created At time</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>...</th>\n",
       "      <th>FeatureI</th>\n",
       "      <th>Country_ID</th>\n",
       "      <th>End Time time</th>\n",
       "      <th>End Time Year</th>\n",
       "      <th>End Time Month</th>\n",
       "      <th>End Time Day_of_month</th>\n",
       "      <th>Start Time time</th>\n",
       "      <th>Start Time Year</th>\n",
       "      <th>Start Time Month</th>\n",
       "      <th>Start Time Day_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>19:38:04.689865</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>21:15:54.617702</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_G23UREIF_Month_11</td>\n",
       "      <td>16:43:35.871761</td>\n",
       "      <td>30.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_H4DF1YA9_Month_11</td>\n",
       "      <td>17:55:40.888019</td>\n",
       "      <td>23.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_F840O4ZH_Month_5</td>\n",
       "      <td>09:42:13.812496</td>\n",
       "      <td>16.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>25</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>8380</th>\n",
       "      <td>ID_CNBR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_6RPFZR7I_Month_11</td>\n",
       "      <td>06:38:13.766016</td>\n",
       "      <td>17.0</td>\n",
       "      <td>[2, 7]</td>\n",
       "      <td>[]</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8381</th>\n",
       "      <td>ID_208Z</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_YEZY3ABB_Month_1</td>\n",
       "      <td>14:26:36.511271</td>\n",
       "      <td>6.0</td>\n",
       "      <td>[1, 5, 4]</td>\n",
       "      <td>[5, 7]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N6EN</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8382</th>\n",
       "      <td>ID_H3QQ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_GS03UEEX_Month_4</td>\n",
       "      <td>10:40:22.978880</td>\n",
       "      <td>22.0</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[]</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4PRY</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8383</th>\n",
       "      <td>ID_7079</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_1CY5JAGU_Month_4</td>\n",
       "      <td>04:23:17.682554</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[7, 3, 2]</td>\n",
       "      <td>[15]</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>G3F0</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8384</th>\n",
       "      <td>ID_DFKK</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_2YXKSD1Q_Month_4</td>\n",
       "      <td>15:34:15.045823</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[5]</td>\n",
       "      <td>[15]</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23:59:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8385 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Comp_ID  Participant Type Successful Submission Count  \\\n",
       "0     ID_WPP3               1.0                         NaN   \n",
       "1     ID_WPP3               1.0                         NaN   \n",
       "2     ID_WPP3               1.0                         NaN   \n",
       "3     ID_WPP3               1.0                         NaN   \n",
       "4     ID_WPP3               1.0                         NaN   \n",
       "...       ...               ...                         ...   \n",
       "8380  ID_CNBR               1.0                         NaN   \n",
       "8381  ID_208Z               1.0                         NaN   \n",
       "8382  ID_H3QQ               1.0                         NaN   \n",
       "8383  ID_7079               1.0                         NaN   \n",
       "8384  ID_DFKK               1.0                         NaN   \n",
       "\n",
       "                   User_ID  Created At time  Comp_participation_day  \\\n",
       "0     ID_40G01FGV_Month_11  19:38:04.689865                    20.0   \n",
       "1      ID_D5I1M68U_Month_4  21:15:54.617702                     7.0   \n",
       "2     ID_G23UREIF_Month_11  16:43:35.871761                    30.0   \n",
       "3     ID_H4DF1YA9_Month_11  17:55:40.888019                    23.0   \n",
       "4      ID_F840O4ZH_Month_5  09:42:13.812496                    16.0   \n",
       "...                    ...              ...                     ...   \n",
       "8380  ID_6RPFZR7I_Month_11  06:38:13.766016                    17.0   \n",
       "8381   ID_YEZY3ABB_Month_1  14:26:36.511271                     6.0   \n",
       "8382   ID_GS03UEEX_Month_4  10:40:22.978880                    22.0   \n",
       "8383   ID_1CY5JAGU_Month_4  04:23:17.682554                    11.0   \n",
       "8384   ID_2YXKSD1Q_Month_4  15:34:15.045823                     7.0   \n",
       "\n",
       "       FeatureA  FeatureB  FeatureC  FeatureD  ...  FeatureI  Country_ID  \\\n",
       "0           [1]  [12, 16]       NaN         1  ...       NaN         NaN   \n",
       "1           [1]  [12, 16]       NaN         1  ...       NaN         NaN   \n",
       "2           [1]  [12, 16]       NaN         1  ...       NaN         NaN   \n",
       "3           [1]  [12, 16]       NaN         1  ...       NaN         NaN   \n",
       "4           [1]  [12, 16]       NaN         1  ...       NaN         NaN   \n",
       "...         ...       ...       ...       ...  ...       ...         ...   \n",
       "8380     [2, 7]        []       9.0         1  ...       9.0         NaN   \n",
       "8381  [1, 5, 4]    [5, 7]       NaN         1  ...       NaN        N6EN   \n",
       "8382        [2]        []      17.0         1  ...      17.0        4PRY   \n",
       "8383  [7, 3, 2]      [15]      10.0         1  ...      10.0        G3F0   \n",
       "8384        [5]      [15]      10.0         1  ...      10.0         NaN   \n",
       "\n",
       "     End Time time End Time Year End Time Month  End Time Day_of_month  \\\n",
       "0         00:00:00           3.0            9.0                   24.0   \n",
       "1         00:00:00           3.0            9.0                   24.0   \n",
       "2         00:00:00           3.0            9.0                   24.0   \n",
       "3         00:00:00           3.0            9.0                   24.0   \n",
       "4         00:00:00           3.0            9.0                   24.0   \n",
       "...            ...           ...            ...                    ...   \n",
       "8380      00:00:00           3.0           11.0                   20.0   \n",
       "8381      23:59:00           3.0           12.0                   17.0   \n",
       "8382      00:00:00           3.0            9.0                   17.0   \n",
       "8383      23:59:00           3.0            6.0                   29.0   \n",
       "8384      23:59:00           2.0            2.0                    4.0   \n",
       "\n",
       "     Start Time time Start Time Year  Start Time Month  \\\n",
       "0           00:00:00               4                 6   \n",
       "1           00:00:00               4                 6   \n",
       "2           00:00:00               4                 6   \n",
       "3           00:00:00               4                 6   \n",
       "4           00:00:00               4                 6   \n",
       "...              ...             ...               ...   \n",
       "8380        00:00:00               3                 8   \n",
       "8381        00:00:00               4                 7   \n",
       "8382        00:00:00               4                 5   \n",
       "8383        06:00:00               3                 4   \n",
       "8384        06:00:00               2                10   \n",
       "\n",
       "      Start Time Day_of_month  \n",
       "0                          25  \n",
       "1                          25  \n",
       "2                          25  \n",
       "3                          25  \n",
       "4                          25  \n",
       "...                       ...  \n",
       "8380                       16  \n",
       "8381                        2  \n",
       "8382                       18  \n",
       "8383                       11  \n",
       "8384                       26  \n",
       "\n",
       "[8385 rows x 25 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb36659",
   "metadata": {},
   "source": [
    "# USERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c7178f32",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user = pd.read_csv('Users.csv').drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3b2e8c30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 12413 entries, 0 to 12412\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   FeatureX                 12413 non-null  int64 \n",
      " 1   FeatureY                 12413 non-null  int64 \n",
      " 2   User_ID                  12413 non-null  object\n",
      " 3   Countries_ID             6531 non-null   object\n",
      " 4   Created At time          12413 non-null  object\n",
      " 5   Created At Year          12413 non-null  int64 \n",
      " 6   Created At Month         12413 non-null  int64 \n",
      " 7   Created At Day_of_month  12413 non-null  int64 \n",
      "dtypes: int64(5), object(3)\n",
      "memory usage: 872.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_user.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "81843e49",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Countries_ID</th>\n",
       "      <th>Created At time</th>\n",
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       "      <th>Created At Month</th>\n",
       "      <th>Created At Day_of_month</th>\n",
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       "      <td>0</td>\n",
       "      <td>ID_W942LN7F</td>\n",
       "      <td>X9GR</td>\n",
       "      <td>13:33:06.531662</td>\n",
       "      <td>1</td>\n",
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       "      <td>16</td>\n",
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       "      <td>ID_C5393PD0</td>\n",
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       "      <td>05:02:39.352427</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12409</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ID_QX7XRT0C</td>\n",
       "      <td>K4DG</td>\n",
       "      <td>02:50:19.027320</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ID_RGN2RK66</td>\n",
       "      <td>ONON</td>\n",
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       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>12411</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ID_XZS1QE0W</td>\n",
       "      <td>3YQR</td>\n",
       "      <td>06:56:02.570173</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ID_8K4FGJYO</td>\n",
       "      <td>G4PG</td>\n",
       "      <td>10:22:54.369939</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12413 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FeatureX  FeatureY      User_ID Countries_ID  Created At time  \\\n",
       "0             0         0  ID_DC6S4E9O         X9GR  19:33:13.663391   \n",
       "1             1         0  ID_E8S97OUT         X9GR  22:18:18.228921   \n",
       "2             0         0  ID_QZ1HASL3         X9GR  23:13:03.266635   \n",
       "3             1         0  ID_SDV9Q3E9         X9GR  09:50:09.697573   \n",
       "4             1         0  ID_W942LN7F         X9GR  13:33:06.531662   \n",
       "...         ...       ...          ...          ...              ...   \n",
       "12408         0         1  ID_C5393PD0         7DKX  05:02:39.352427   \n",
       "12409         0         1  ID_QX7XRT0C         K4DG  02:50:19.027320   \n",
       "12410         0         1  ID_RGN2RK66         ONON  22:06:33.713692   \n",
       "12411         0         0  ID_XZS1QE0W         3YQR  06:56:02.570173   \n",
       "12412         0         1  ID_8K4FGJYO         G4PG  10:22:54.369939   \n",
       "\n",
       "       Created At Year  Created At Month  Created At Day_of_month  \n",
       "0                    1                 1                       16  \n",
       "1                    1                12                       27  \n",
       "2                    1                 1                        1  \n",
       "3                    1                12                       27  \n",
       "4                    1                 1                       16  \n",
       "...                ...               ...                      ...  \n",
       "12408                1                11                        6  \n",
       "12409                1                 3                       25  \n",
       "12410                1                12                        7  \n",
       "12411                1                 5                       27  \n",
       "12412                1                12                        5  \n",
       "\n",
       "[12413 rows x 8 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "135d278c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12413"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_user['User_ID']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a6536600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_user['Created At Year']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3f8fabc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user.drop('Created At Year', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9651893b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user[\"User_ID\"] = df_user[\"User_ID\"].astype(\"string\")\n",
    "df_user[\"Created At Month\"] = df_user[\"Created At Month\"].astype(\"string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "60f3d1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df_user[\"Created At Month\"].copy()\n",
    "df_user[\"User_ID\"] = df_user[\"User_ID\"].str.cat(new, sep =\"_Month_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4b0ad8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12413"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df_user['User_ID']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b5662e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user.rename(columns={'Created At Month': 'User sign-in month'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "07c053b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user.rename(columns={'Created At time': 'User sign-in time'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e8b05b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user.rename(columns={'Created At Day_of_month': 'User sign-in day'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "97d057bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>ID_QX7XRT0C_Month_3</td>\n",
       "      <td>K4DG</td>\n",
       "      <td>02:50:19.027320</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>ID_XZS1QE0W_Month_5</td>\n",
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       "      <td>06:56:02.570173</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12412</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ID_8K4FGJYO_Month_12</td>\n",
       "      <td>G4PG</td>\n",
       "      <td>10:22:54.369939</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12413 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FeatureX  FeatureY               User_ID Countries_ID  \\\n",
       "0             0         0   ID_DC6S4E9O_Month_1         X9GR   \n",
       "1             1         0  ID_E8S97OUT_Month_12         X9GR   \n",
       "2             0         0   ID_QZ1HASL3_Month_1         X9GR   \n",
       "3             1         0  ID_SDV9Q3E9_Month_12         X9GR   \n",
       "4             1         0   ID_W942LN7F_Month_1         X9GR   \n",
       "...         ...       ...                   ...          ...   \n",
       "12408         0         1  ID_C5393PD0_Month_11         7DKX   \n",
       "12409         0         1   ID_QX7XRT0C_Month_3         K4DG   \n",
       "12410         0         1  ID_RGN2RK66_Month_12         ONON   \n",
       "12411         0         0   ID_XZS1QE0W_Month_5         3YQR   \n",
       "12412         0         1  ID_8K4FGJYO_Month_12         G4PG   \n",
       "\n",
       "      User sign-in time User sign-in month  User sign-in day  \n",
       "0       19:33:13.663391                  1                16  \n",
       "1       22:18:18.228921                 12                27  \n",
       "2       23:13:03.266635                  1                 1  \n",
       "3       09:50:09.697573                 12                27  \n",
       "4       13:33:06.531662                  1                16  \n",
       "...                 ...                ...               ...  \n",
       "12408   05:02:39.352427                 11                 6  \n",
       "12409   02:50:19.027320                  3                25  \n",
       "12410   22:06:33.713692                 12                 7  \n",
       "12411   06:56:02.570173                  5                27  \n",
       "12412   10:22:54.369939                 12                 5  \n",
       "\n",
       "[12413 rows x 7 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a171fc36",
   "metadata": {},
   "source": [
    "# MERGE 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f4391e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "result2 = pd.merge(df_userAct, df_user, how=\"outer\", on= 'User_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "19c94b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>datetime Day_of_month</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>...</th>\n",
       "      <th>Updated_Submission</th>\n",
       "      <th>badge_HYIO</th>\n",
       "      <th>count_UA</th>\n",
       "      <th>Day duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>Countries_ID</th>\n",
       "      <th>User sign-in time</th>\n",
       "      <th>User sign-in month</th>\n",
       "      <th>User sign-in day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>26.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15:18:11.439200</td>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_003YJHQA_Month_4</td>\n",
       "      <td>12.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GYYX</td>\n",
       "      <td>10:30:32.261855</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_004UKSJV_Month_1</td>\n",
       "      <td>15.0</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>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_004UKSJV_Month_12</td>\n",
       "      <td>29.0</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IFO5</td>\n",
       "      <td>08:32:14.997443</td>\n",
       "      <td>12</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_008D2J6C_Month_1</td>\n",
       "      <td>15.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GYYX</td>\n",
       "      <td>09:42:39.695313</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</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>17182</th>\n",
       "      <td>ID_Y02FKZWI_Month_3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7JT1</td>\n",
       "      <td>04:04:35.100180</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17183</th>\n",
       "      <td>ID_FDOGWALS_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UKC2</td>\n",
       "      <td>13:30:49.814791</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17184</th>\n",
       "      <td>ID_YJCY8FF5_Month_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H801</td>\n",
       "      <td>15:54:12.676400</td>\n",
       "      <td>5</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17185</th>\n",
       "      <td>ID_QDL8A9FQ_Month_4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NRDP</td>\n",
       "      <td>09:49:35.535847</td>\n",
       "      <td>4</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17186</th>\n",
       "      <td>ID_C5393PD0_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7DKX</td>\n",
       "      <td>05:02:39.352427</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17187 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID  datetime Day_of_month  \\\n",
       "0       ID_000H9XOP_Month_5                   26.0   \n",
       "1       ID_003YJHQA_Month_4                   12.0   \n",
       "2       ID_004UKSJV_Month_1                   15.0   \n",
       "3      ID_004UKSJV_Month_12                   29.0   \n",
       "4       ID_008D2J6C_Month_1                   15.0   \n",
       "...                     ...                    ...   \n",
       "17182   ID_Y02FKZWI_Month_3                    NaN   \n",
       "17183  ID_FDOGWALS_Month_11                    NaN   \n",
       "17184   ID_YJCY8FF5_Month_5                    NaN   \n",
       "17185   ID_QDL8A9FQ_Month_4                    NaN   \n",
       "17186  ID_C5393PD0_Month_11                    NaN   \n",
       "\n",
       "       Downloaded_Competition_Datafile  Viewed_All_Discussions  \\\n",
       "0                                  0.0                     1.0   \n",
       "1                                  0.0                     2.0   \n",
       "2                                  0.0                     0.0   \n",
       "3                                  0.0                     1.0   \n",
       "4                                  0.0                     1.0   \n",
       "...                                ...                     ...   \n",
       "17182                              NaN                     NaN   \n",
       "17183                              NaN                     NaN   \n",
       "17184                              NaN                     NaN   \n",
       "17185                              NaN                     NaN   \n",
       "17186                              NaN                     NaN   \n",
       "\n",
       "       Confirmed_Email  Joined_Competition  Signed_Up  \\\n",
       "0                  0.0                 0.0        1.0   \n",
       "1                  1.0                 0.0        1.0   \n",
       "2                  0.0                 0.0        0.0   \n",
       "3                  1.0                 0.0        1.0   \n",
       "4                  1.0                 0.0        1.0   \n",
       "...                ...                 ...        ...   \n",
       "17182              NaN                 NaN        NaN   \n",
       "17183              NaN                 NaN        NaN   \n",
       "17184              NaN                 NaN        NaN   \n",
       "17185              NaN                 NaN        NaN   \n",
       "17186              NaN                 NaN        NaN   \n",
       "\n",
       "       Viewed_All_Competitions  Viewed_All_Learning_Pages  badge_OCZE  ...  \\\n",
       "0                          1.0                        0.0         0.0  ...   \n",
       "1                          1.0                        1.0         1.0  ...   \n",
       "2                          0.0                        0.0         1.0  ...   \n",
       "3                          1.0                        1.0         0.0  ...   \n",
       "4                          1.0                        1.0         1.0  ...   \n",
       "...                        ...                        ...         ...  ...   \n",
       "17182                      NaN                        NaN         NaN  ...   \n",
       "17183                      NaN                        NaN         NaN  ...   \n",
       "17184                      NaN                        NaN         NaN  ...   \n",
       "17185                      NaN                        NaN         NaN  ...   \n",
       "17186                      NaN                        NaN         NaN  ...   \n",
       "\n",
       "       Updated_Submission  badge_HYIO  count_UA  Day duration  FeatureX  \\\n",
       "0                     0.0         0.0       6.0           4.0       0.0   \n",
       "1                     0.0         0.0      11.0           0.0       0.0   \n",
       "2                     0.0         0.0       1.0           0.0       NaN   \n",
       "3                     0.0         0.0      11.0           1.0       0.0   \n",
       "4                     0.0         0.0      10.0           8.0       0.0   \n",
       "...                   ...         ...       ...           ...       ...   \n",
       "17182                 NaN         NaN       NaN           NaN       0.0   \n",
       "17183                 NaN         NaN       NaN           NaN       0.0   \n",
       "17184                 NaN         NaN       NaN           NaN       0.0   \n",
       "17185                 NaN         NaN       NaN           NaN       0.0   \n",
       "17186                 NaN         NaN       NaN           NaN       0.0   \n",
       "\n",
       "       FeatureY  Countries_ID  User sign-in time  User sign-in month  \\\n",
       "0           1.0           NaN    15:18:11.439200                   5   \n",
       "1           1.0          GYYX    10:30:32.261855                   4   \n",
       "2           NaN           NaN                NaN                <NA>   \n",
       "3           0.0          IFO5    08:32:14.997443                  12   \n",
       "4           0.0          GYYX    09:42:39.695313                   1   \n",
       "...         ...           ...                ...                 ...   \n",
       "17182       0.0          7JT1    04:04:35.100180                   3   \n",
       "17183       0.0          UKC2    13:30:49.814791                  11   \n",
       "17184       0.0          H801    15:54:12.676400                   5   \n",
       "17185       0.0          NRDP    09:49:35.535847                   4   \n",
       "17186       1.0          7DKX    05:02:39.352427                  11   \n",
       "\n",
       "       User sign-in day  \n",
       "0                  22.0  \n",
       "1                  12.0  \n",
       "2                   NaN  \n",
       "3                  28.0  \n",
       "4                   7.0  \n",
       "...                 ...  \n",
       "17182              26.0  \n",
       "17183               5.0  \n",
       "17184              11.0  \n",
       "17185              28.0  \n",
       "17186               6.0  \n",
       "\n",
       "[17187 rows x 32 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40dcd973",
   "metadata": {},
   "source": [
    "# MERGE 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "23ad4c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3 = pd.merge(result1, result2, how=\"outer\", on= 'User_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7bcccc18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19842 entries, 0 to 19841\n",
      "Data columns (total 56 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Comp_ID                          8385 non-null   object \n",
      " 1   Participant Type                 8385 non-null   float64\n",
      " 2   Successful Submission Count      1449 non-null   object \n",
      " 3   User_ID                          19842 non-null  object \n",
      " 4   Created At time                  8385 non-null   object \n",
      " 5   Comp_participation_day           8385 non-null   float64\n",
      " 6   FeatureA                         8385 non-null   object \n",
      " 7   FeatureB                         8385 non-null   object \n",
      " 8   FeatureC                         5655 non-null   float64\n",
      " 9   FeatureD                         8385 non-null   float64\n",
      " 10  FeatureE                         8385 non-null   float64\n",
      " 11  SecretCode                       8385 non-null   float64\n",
      " 12  FeatureF                         8385 non-null   object \n",
      " 13  FeatureG                         8385 non-null   object \n",
      " 14  FeatureH                         8385 non-null   object \n",
      " 15  FeatureI                         7206 non-null   float64\n",
      " 16  Country_ID                       4073 non-null   object \n",
      " 17  End Time time                    6340 non-null   object \n",
      " 18  End Time Year                    6340 non-null   float64\n",
      " 19  End Time Month                   6340 non-null   float64\n",
      " 20  End Time Day_of_month            6340 non-null   float64\n",
      " 21  Start Time time                  8385 non-null   object \n",
      " 22  Start Time Year                  8385 non-null   float64\n",
      " 23  Start Time Month                 8385 non-null   float64\n",
      " 24  Start Time Day_of_month          8385 non-null   float64\n",
      " 25  datetime Day_of_month            17132 non-null  float64\n",
      " 26  Downloaded_Competition_Datafile  17132 non-null  float64\n",
      " 27  Viewed_All_Discussions           17132 non-null  float64\n",
      " 28  Confirmed_Email                  17132 non-null  float64\n",
      " 29  Joined_Competition               17132 non-null  float64\n",
      " 30  Signed_Up                        17132 non-null  float64\n",
      " 31  Viewed_All_Competitions          17132 non-null  float64\n",
      " 32  Viewed_All_Learning_Pages        17132 non-null  float64\n",
      " 33  badge_OCZE                       17132 non-null  float64\n",
      " 34  create_alias                     17132 non-null  float64\n",
      " 35  identify                         17132 non-null  float64\n",
      " 36  comp_ID                          17132 non-null  float64\n",
      " 37  Signed_In                        17132 non-null  float64\n",
      " 38  job_ID                           17132 non-null  float64\n",
      " 39  Updated_Discussion               17132 non-null  float64\n",
      " 40  Viewed_All_Jobs                  17132 non-null  float64\n",
      " 41  Created_Submission               17132 non-null  float64\n",
      " 42  Signed_Out                       17132 non-null  float64\n",
      " 43  Updated_Profile                  17132 non-null  float64\n",
      " 44  blog_ID                          17132 non-null  float64\n",
      " 45  Viewed_Discussion                17132 non-null  float64\n",
      " 46  Updated_Submission               17132 non-null  float64\n",
      " 47  badge_HYIO                       17132 non-null  float64\n",
      " 48  count_UA                         17132 non-null  float64\n",
      " 49  Day duration                     17132 non-null  float64\n",
      " 50  FeatureX                         14220 non-null  float64\n",
      " 51  FeatureY                         14220 non-null  float64\n",
      " 52  Countries_ID                     7459 non-null   object \n",
      " 53  User sign-in time                14220 non-null  object \n",
      " 54  User sign-in month               14220 non-null  string \n",
      " 55  User sign-in day                 14220 non-null  float64\n",
      "dtypes: float64(41), object(14), string(1)\n",
      "memory usage: 8.6+ MB\n"
     ]
    }
   ],
   "source": [
    "result3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "cb30280a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comp_ID</th>\n",
       "      <th>Participant Type</th>\n",
       "      <th>Successful Submission Count</th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Created At time</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>...</th>\n",
       "      <th>Updated_Submission</th>\n",
       "      <th>badge_HYIO</th>\n",
       "      <th>count_UA</th>\n",
       "      <th>Day duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>Countries_ID</th>\n",
       "      <th>User sign-in time</th>\n",
       "      <th>User sign-in month</th>\n",
       "      <th>User sign-in day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>19:38:04.689865</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EOMO</td>\n",
       "      <td>19:37:04.756448</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZN92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>19:38:04.670583</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EOMO</td>\n",
       "      <td>19:37:04.756448</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>21:15:54.617702</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ZN92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>21:15:54.596519</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_6IDY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>21:43:19.694875</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[3, 2]</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</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>19837</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_Y02FKZWI_Month_3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7JT1</td>\n",
       "      <td>04:04:35.100180</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19838</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_FDOGWALS_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UKC2</td>\n",
       "      <td>13:30:49.814791</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19839</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_YJCY8FF5_Month_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H801</td>\n",
       "      <td>15:54:12.676400</td>\n",
       "      <td>5</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19840</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_QDL8A9FQ_Month_4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NRDP</td>\n",
       "      <td>09:49:35.535847</td>\n",
       "      <td>4</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19841</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_C5393PD0_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7DKX</td>\n",
       "      <td>05:02:39.352427</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19842 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Comp_ID  Participant Type Successful Submission Count  \\\n",
       "0      ID_WPP3               1.0                         NaN   \n",
       "1      ID_ZN92               1.0                         NaN   \n",
       "2      ID_WPP3               1.0                         NaN   \n",
       "3      ID_ZN92               1.0                         NaN   \n",
       "4      ID_6IDY               1.0                         NaN   \n",
       "...        ...               ...                         ...   \n",
       "19837      NaN               NaN                         NaN   \n",
       "19838      NaN               NaN                         NaN   \n",
       "19839      NaN               NaN                         NaN   \n",
       "19840      NaN               NaN                         NaN   \n",
       "19841      NaN               NaN                         NaN   \n",
       "\n",
       "                    User_ID  Created At time  Comp_participation_day FeatureA  \\\n",
       "0      ID_40G01FGV_Month_11  19:38:04.689865                    20.0      [1]   \n",
       "1      ID_40G01FGV_Month_11  19:38:04.670583                    20.0      [1]   \n",
       "2       ID_D5I1M68U_Month_4  21:15:54.617702                     7.0      [1]   \n",
       "3       ID_D5I1M68U_Month_4  21:15:54.596519                     7.0      [1]   \n",
       "4       ID_D5I1M68U_Month_4  21:43:19.694875                     7.0   [3, 2]   \n",
       "...                     ...              ...                     ...      ...   \n",
       "19837   ID_Y02FKZWI_Month_3              NaN                     NaN      NaN   \n",
       "19838  ID_FDOGWALS_Month_11              NaN                     NaN      NaN   \n",
       "19839   ID_YJCY8FF5_Month_5              NaN                     NaN      NaN   \n",
       "19840   ID_QDL8A9FQ_Month_4              NaN                     NaN      NaN   \n",
       "19841  ID_C5393PD0_Month_11              NaN                     NaN      NaN   \n",
       "\n",
       "       FeatureB  FeatureC  FeatureD  ...  Updated_Submission  badge_HYIO  \\\n",
       "0      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "1      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "2      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "3      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "4            []       6.0       1.0  ...                 0.0         0.0   \n",
       "...         ...       ...       ...  ...                 ...         ...   \n",
       "19837       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19838       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19839       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19840       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19841       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "\n",
       "      count_UA Day duration FeatureX  FeatureY Countries_ID User sign-in time  \\\n",
       "0         21.0          3.0      0.0       0.0         EOMO   19:37:04.756448   \n",
       "1         21.0          3.0      0.0       0.0         EOMO   19:37:04.756448   \n",
       "2         22.0          0.0      0.0       3.0          NaN   21:11:55.295294   \n",
       "3         22.0          0.0      0.0       3.0          NaN   21:11:55.295294   \n",
       "4         22.0          0.0      0.0       3.0          NaN   21:11:55.295294   \n",
       "...        ...          ...      ...       ...          ...               ...   \n",
       "19837      NaN          NaN      0.0       0.0         7JT1   04:04:35.100180   \n",
       "19838      NaN          NaN      0.0       0.0         UKC2   13:30:49.814791   \n",
       "19839      NaN          NaN      0.0       0.0         H801   15:54:12.676400   \n",
       "19840      NaN          NaN      0.0       0.0         NRDP   09:49:35.535847   \n",
       "19841      NaN          NaN      0.0       1.0         7DKX   05:02:39.352427   \n",
       "\n",
       "       User sign-in month  User sign-in day  \n",
       "0                      11              20.0  \n",
       "1                      11              20.0  \n",
       "2                       4               7.0  \n",
       "3                       4               7.0  \n",
       "4                       4               7.0  \n",
       "...                   ...               ...  \n",
       "19837                   3              26.0  \n",
       "19838                  11               5.0  \n",
       "19839                   5              11.0  \n",
       "19840                   4              28.0  \n",
       "19841                  11               6.0  \n",
       "\n",
       "[19842 rows x 56 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6d97d41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'End Time time': 'competition_ended_time'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "144c2644",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'End Time Year': 'competition_ended_year'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a4f6df9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'End Time Month': 'competition_ended_month'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8613dfa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'End Time Day_of_month': 'competition_ended_day'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5bb8beb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'Start Time time': 'competition_started_time'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f1a5cce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'Start Time Year': 'competition_started_year'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "42e88248",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'Start Time Month': 'competition_started_month'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "39d441ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.rename(columns={'Start Time Day_of_month': 'competition_started_day'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1a03d171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19842 entries, 0 to 19841\n",
      "Data columns (total 56 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Comp_ID                          8385 non-null   object \n",
      " 1   Participant Type                 8385 non-null   float64\n",
      " 2   Successful Submission Count      1449 non-null   object \n",
      " 3   User_ID                          19842 non-null  object \n",
      " 4   Created At time                  8385 non-null   object \n",
      " 5   Comp_participation_day           8385 non-null   float64\n",
      " 6   FeatureA                         8385 non-null   object \n",
      " 7   FeatureB                         8385 non-null   object \n",
      " 8   FeatureC                         5655 non-null   float64\n",
      " 9   FeatureD                         8385 non-null   float64\n",
      " 10  FeatureE                         8385 non-null   float64\n",
      " 11  SecretCode                       8385 non-null   float64\n",
      " 12  FeatureF                         8385 non-null   object \n",
      " 13  FeatureG                         8385 non-null   object \n",
      " 14  FeatureH                         8385 non-null   object \n",
      " 15  FeatureI                         7206 non-null   float64\n",
      " 16  Country_ID                       4073 non-null   object \n",
      " 17  competition_ended_time           6340 non-null   object \n",
      " 18  competition_ended_year           6340 non-null   float64\n",
      " 19  competition_ended_month          6340 non-null   float64\n",
      " 20  competition_ended_day            6340 non-null   float64\n",
      " 21  competition_started_time         8385 non-null   object \n",
      " 22  competition_started_year         8385 non-null   float64\n",
      " 23  competition_started_month        8385 non-null   float64\n",
      " 24  competition_started_day          8385 non-null   float64\n",
      " 25  datetime Day_of_month            17132 non-null  float64\n",
      " 26  Downloaded_Competition_Datafile  17132 non-null  float64\n",
      " 27  Viewed_All_Discussions           17132 non-null  float64\n",
      " 28  Confirmed_Email                  17132 non-null  float64\n",
      " 29  Joined_Competition               17132 non-null  float64\n",
      " 30  Signed_Up                        17132 non-null  float64\n",
      " 31  Viewed_All_Competitions          17132 non-null  float64\n",
      " 32  Viewed_All_Learning_Pages        17132 non-null  float64\n",
      " 33  badge_OCZE                       17132 non-null  float64\n",
      " 34  create_alias                     17132 non-null  float64\n",
      " 35  identify                         17132 non-null  float64\n",
      " 36  comp_ID                          17132 non-null  float64\n",
      " 37  Signed_In                        17132 non-null  float64\n",
      " 38  job_ID                           17132 non-null  float64\n",
      " 39  Updated_Discussion               17132 non-null  float64\n",
      " 40  Viewed_All_Jobs                  17132 non-null  float64\n",
      " 41  Created_Submission               17132 non-null  float64\n",
      " 42  Signed_Out                       17132 non-null  float64\n",
      " 43  Updated_Profile                  17132 non-null  float64\n",
      " 44  blog_ID                          17132 non-null  float64\n",
      " 45  Viewed_Discussion                17132 non-null  float64\n",
      " 46  Updated_Submission               17132 non-null  float64\n",
      " 47  badge_HYIO                       17132 non-null  float64\n",
      " 48  count_UA                         17132 non-null  float64\n",
      " 49  Day duration                     17132 non-null  float64\n",
      " 50  FeatureX                         14220 non-null  float64\n",
      " 51  FeatureY                         14220 non-null  float64\n",
      " 52  Countries_ID                     7459 non-null   object \n",
      " 53  User sign-in time                14220 non-null  object \n",
      " 54  User sign-in month               14220 non-null  string \n",
      " 55  User sign-in day                 14220 non-null  float64\n",
      "dtypes: float64(41), object(14), string(1)\n",
      "memory usage: 8.6+ MB\n"
     ]
    }
   ],
   "source": [
    "result3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4935f6c1",
   "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>Comp_ID</th>\n",
       "      <th>Participant Type</th>\n",
       "      <th>Successful Submission Count</th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Created At time</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>...</th>\n",
       "      <th>Updated_Submission</th>\n",
       "      <th>badge_HYIO</th>\n",
       "      <th>count_UA</th>\n",
       "      <th>Day duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>Countries_ID</th>\n",
       "      <th>User sign-in time</th>\n",
       "      <th>User sign-in month</th>\n",
       "      <th>User sign-in day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>19:38:04.689865</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EOMO</td>\n",
       "      <td>19:37:04.756448</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_ZN92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>19:38:04.670583</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EOMO</td>\n",
       "      <td>19:37:04.756448</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_WPP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>21:15:54.617702</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ZN92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>21:15:54.596519</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_6IDY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>21:43:19.694875</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[3, 2]</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</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>19837</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_Y02FKZWI_Month_3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7JT1</td>\n",
       "      <td>04:04:35.100180</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19838</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_FDOGWALS_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UKC2</td>\n",
       "      <td>13:30:49.814791</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19839</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_YJCY8FF5_Month_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H801</td>\n",
       "      <td>15:54:12.676400</td>\n",
       "      <td>5</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19840</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_QDL8A9FQ_Month_4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NRDP</td>\n",
       "      <td>09:49:35.535847</td>\n",
       "      <td>4</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19841</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ID_C5393PD0_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7DKX</td>\n",
       "      <td>05:02:39.352427</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19842 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Comp_ID  Participant Type Successful Submission Count  \\\n",
       "0      ID_WPP3               1.0                         NaN   \n",
       "1      ID_ZN92               1.0                         NaN   \n",
       "2      ID_WPP3               1.0                         NaN   \n",
       "3      ID_ZN92               1.0                         NaN   \n",
       "4      ID_6IDY               1.0                         NaN   \n",
       "...        ...               ...                         ...   \n",
       "19837      NaN               NaN                         NaN   \n",
       "19838      NaN               NaN                         NaN   \n",
       "19839      NaN               NaN                         NaN   \n",
       "19840      NaN               NaN                         NaN   \n",
       "19841      NaN               NaN                         NaN   \n",
       "\n",
       "                    User_ID  Created At time  Comp_participation_day FeatureA  \\\n",
       "0      ID_40G01FGV_Month_11  19:38:04.689865                    20.0      [1]   \n",
       "1      ID_40G01FGV_Month_11  19:38:04.670583                    20.0      [1]   \n",
       "2       ID_D5I1M68U_Month_4  21:15:54.617702                     7.0      [1]   \n",
       "3       ID_D5I1M68U_Month_4  21:15:54.596519                     7.0      [1]   \n",
       "4       ID_D5I1M68U_Month_4  21:43:19.694875                     7.0   [3, 2]   \n",
       "...                     ...              ...                     ...      ...   \n",
       "19837   ID_Y02FKZWI_Month_3              NaN                     NaN      NaN   \n",
       "19838  ID_FDOGWALS_Month_11              NaN                     NaN      NaN   \n",
       "19839   ID_YJCY8FF5_Month_5              NaN                     NaN      NaN   \n",
       "19840   ID_QDL8A9FQ_Month_4              NaN                     NaN      NaN   \n",
       "19841  ID_C5393PD0_Month_11              NaN                     NaN      NaN   \n",
       "\n",
       "       FeatureB  FeatureC  FeatureD  ...  Updated_Submission  badge_HYIO  \\\n",
       "0      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "1      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "2      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "3      [12, 16]       NaN       1.0  ...                 0.0         0.0   \n",
       "4            []       6.0       1.0  ...                 0.0         0.0   \n",
       "...         ...       ...       ...  ...                 ...         ...   \n",
       "19837       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19838       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19839       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19840       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "19841       NaN       NaN       NaN  ...                 NaN         NaN   \n",
       "\n",
       "      count_UA Day duration FeatureX  FeatureY Countries_ID User sign-in time  \\\n",
       "0         21.0          3.0      0.0       0.0         EOMO   19:37:04.756448   \n",
       "1         21.0          3.0      0.0       0.0         EOMO   19:37:04.756448   \n",
       "2         22.0          0.0      0.0       3.0          NaN   21:11:55.295294   \n",
       "3         22.0          0.0      0.0       3.0          NaN   21:11:55.295294   \n",
       "4         22.0          0.0      0.0       3.0          NaN   21:11:55.295294   \n",
       "...        ...          ...      ...       ...          ...               ...   \n",
       "19837      NaN          NaN      0.0       0.0         7JT1   04:04:35.100180   \n",
       "19838      NaN          NaN      0.0       0.0         UKC2   13:30:49.814791   \n",
       "19839      NaN          NaN      0.0       0.0         H801   15:54:12.676400   \n",
       "19840      NaN          NaN      0.0       0.0         NRDP   09:49:35.535847   \n",
       "19841      NaN          NaN      0.0       1.0         7DKX   05:02:39.352427   \n",
       "\n",
       "       User sign-in month  User sign-in day  \n",
       "0                      11              20.0  \n",
       "1                      11              20.0  \n",
       "2                       4               7.0  \n",
       "3                       4               7.0  \n",
       "4                       4               7.0  \n",
       "...                   ...               ...  \n",
       "19837                   3              26.0  \n",
       "19838                  11               5.0  \n",
       "19839                   5              11.0  \n",
       "19840                   4              28.0  \n",
       "19841                  11               6.0  \n",
       "\n",
       "[19842 rows x 56 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5a230d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = result3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "00f20228",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop('Comp_ID', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "dc2bab79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'ZEWU', 'DYYF', 'H03V', 'KYT4', 'G3F0', 'SE4K', 'X9GR',\n",
       "       '03GB', 'GYYX', 'YSLJ', 'N6EN', 'ISIQ', 'EGI9', '4PRY'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[\"Country_ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a9fba33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_column = df_final.pop('User_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5bbeaa82",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.insert(0, 'User_ID', first_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "06cbe88c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19842 entries, 0 to 19841\n",
      "Data columns (total 55 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   User_ID                          19842 non-null  object \n",
      " 1   Participant Type                 8385 non-null   float64\n",
      " 2   Successful Submission Count      1449 non-null   object \n",
      " 3   Created At time                  8385 non-null   object \n",
      " 4   Comp_participation_day           8385 non-null   float64\n",
      " 5   FeatureA                         8385 non-null   object \n",
      " 6   FeatureB                         8385 non-null   object \n",
      " 7   FeatureC                         5655 non-null   float64\n",
      " 8   FeatureD                         8385 non-null   float64\n",
      " 9   FeatureE                         8385 non-null   float64\n",
      " 10  SecretCode                       8385 non-null   float64\n",
      " 11  FeatureF                         8385 non-null   object \n",
      " 12  FeatureG                         8385 non-null   object \n",
      " 13  FeatureH                         8385 non-null   object \n",
      " 14  FeatureI                         7206 non-null   float64\n",
      " 15  Country_ID                       4073 non-null   object \n",
      " 16  competition_ended_time           6340 non-null   object \n",
      " 17  competition_ended_year           6340 non-null   float64\n",
      " 18  competition_ended_month          6340 non-null   float64\n",
      " 19  competition_ended_day            6340 non-null   float64\n",
      " 20  competition_started_time         8385 non-null   object \n",
      " 21  competition_started_year         8385 non-null   float64\n",
      " 22  competition_started_month        8385 non-null   float64\n",
      " 23  competition_started_day          8385 non-null   float64\n",
      " 24  datetime Day_of_month            17132 non-null  float64\n",
      " 25  Downloaded_Competition_Datafile  17132 non-null  float64\n",
      " 26  Viewed_All_Discussions           17132 non-null  float64\n",
      " 27  Confirmed_Email                  17132 non-null  float64\n",
      " 28  Joined_Competition               17132 non-null  float64\n",
      " 29  Signed_Up                        17132 non-null  float64\n",
      " 30  Viewed_All_Competitions          17132 non-null  float64\n",
      " 31  Viewed_All_Learning_Pages        17132 non-null  float64\n",
      " 32  badge_OCZE                       17132 non-null  float64\n",
      " 33  create_alias                     17132 non-null  float64\n",
      " 34  identify                         17132 non-null  float64\n",
      " 35  comp_ID                          17132 non-null  float64\n",
      " 36  Signed_In                        17132 non-null  float64\n",
      " 37  job_ID                           17132 non-null  float64\n",
      " 38  Updated_Discussion               17132 non-null  float64\n",
      " 39  Viewed_All_Jobs                  17132 non-null  float64\n",
      " 40  Created_Submission               17132 non-null  float64\n",
      " 41  Signed_Out                       17132 non-null  float64\n",
      " 42  Updated_Profile                  17132 non-null  float64\n",
      " 43  blog_ID                          17132 non-null  float64\n",
      " 44  Viewed_Discussion                17132 non-null  float64\n",
      " 45  Updated_Submission               17132 non-null  float64\n",
      " 46  badge_HYIO                       17132 non-null  float64\n",
      " 47  count_UA                         17132 non-null  float64\n",
      " 48  Day duration                     17132 non-null  float64\n",
      " 49  FeatureX                         14220 non-null  float64\n",
      " 50  FeatureY                         14220 non-null  float64\n",
      " 51  Countries_ID                     7459 non-null   object \n",
      " 52  User sign-in time                14220 non-null  object \n",
      " 53  User sign-in month               14220 non-null  string \n",
      " 54  User sign-in day                 14220 non-null  float64\n",
      "dtypes: float64(41), object(13), string(1)\n",
      "memory usage: 8.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "217f2886",
   "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>Participant Type</th>\n",
       "      <th>Successful Submission Count</th>\n",
       "      <th>Created At time</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>FeatureE</th>\n",
       "      <th>...</th>\n",
       "      <th>Updated_Submission</th>\n",
       "      <th>badge_HYIO</th>\n",
       "      <th>count_UA</th>\n",
       "      <th>Day duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>Countries_ID</th>\n",
       "      <th>User sign-in time</th>\n",
       "      <th>User sign-in month</th>\n",
       "      <th>User sign-in day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19:38:04.689865</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EOMO</td>\n",
       "      <td>19:37:04.756448</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_40G01FGV_Month_11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19:38:04.670583</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</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>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EOMO</td>\n",
       "      <td>19:37:04.756448</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:15:54.617702</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:15:54.596519</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[12, 16]</td>\n",
       "      <td>NaN</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>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_D5I1M68U_Month_4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:43:19.694875</td>\n",
       "      <td>7.0</td>\n",
       "      <td>[3, 2]</td>\n",
       "      <td>[]</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:11:55.295294</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</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>19837</th>\n",
       "      <td>ID_Y02FKZWI_Month_3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7JT1</td>\n",
       "      <td>04:04:35.100180</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19838</th>\n",
       "      <td>ID_FDOGWALS_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UKC2</td>\n",
       "      <td>13:30:49.814791</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19839</th>\n",
       "      <td>ID_YJCY8FF5_Month_5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H801</td>\n",
       "      <td>15:54:12.676400</td>\n",
       "      <td>5</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19840</th>\n",
       "      <td>ID_QDL8A9FQ_Month_4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NRDP</td>\n",
       "      <td>09:49:35.535847</td>\n",
       "      <td>4</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19841</th>\n",
       "      <td>ID_C5393PD0_Month_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7DKX</td>\n",
       "      <td>05:02:39.352427</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19842 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID  Participant Type Successful Submission Count  \\\n",
       "0      ID_40G01FGV_Month_11               1.0                         NaN   \n",
       "1      ID_40G01FGV_Month_11               1.0                         NaN   \n",
       "2       ID_D5I1M68U_Month_4               1.0                         NaN   \n",
       "3       ID_D5I1M68U_Month_4               1.0                         NaN   \n",
       "4       ID_D5I1M68U_Month_4               1.0                         NaN   \n",
       "...                     ...               ...                         ...   \n",
       "19837   ID_Y02FKZWI_Month_3               NaN                         NaN   \n",
       "19838  ID_FDOGWALS_Month_11               NaN                         NaN   \n",
       "19839   ID_YJCY8FF5_Month_5               NaN                         NaN   \n",
       "19840   ID_QDL8A9FQ_Month_4               NaN                         NaN   \n",
       "19841  ID_C5393PD0_Month_11               NaN                         NaN   \n",
       "\n",
       "       Created At time  Comp_participation_day FeatureA  FeatureB  FeatureC  \\\n",
       "0      19:38:04.689865                    20.0      [1]  [12, 16]       NaN   \n",
       "1      19:38:04.670583                    20.0      [1]  [12, 16]       NaN   \n",
       "2      21:15:54.617702                     7.0      [1]  [12, 16]       NaN   \n",
       "3      21:15:54.596519                     7.0      [1]  [12, 16]       NaN   \n",
       "4      21:43:19.694875                     7.0   [3, 2]        []       6.0   \n",
       "...                ...                     ...      ...       ...       ...   \n",
       "19837              NaN                     NaN      NaN       NaN       NaN   \n",
       "19838              NaN                     NaN      NaN       NaN       NaN   \n",
       "19839              NaN                     NaN      NaN       NaN       NaN   \n",
       "19840              NaN                     NaN      NaN       NaN       NaN   \n",
       "19841              NaN                     NaN      NaN       NaN       NaN   \n",
       "\n",
       "       FeatureD  FeatureE  ...  Updated_Submission badge_HYIO count_UA  \\\n",
       "0           1.0       2.0  ...                 0.0        0.0     21.0   \n",
       "1           1.0       1.0  ...                 0.0        0.0     21.0   \n",
       "2           1.0       2.0  ...                 0.0        0.0     22.0   \n",
       "3           1.0       1.0  ...                 0.0        0.0     22.0   \n",
       "4           1.0       3.0  ...                 0.0        0.0     22.0   \n",
       "...         ...       ...  ...                 ...        ...      ...   \n",
       "19837       NaN       NaN  ...                 NaN        NaN      NaN   \n",
       "19838       NaN       NaN  ...                 NaN        NaN      NaN   \n",
       "19839       NaN       NaN  ...                 NaN        NaN      NaN   \n",
       "19840       NaN       NaN  ...                 NaN        NaN      NaN   \n",
       "19841       NaN       NaN  ...                 NaN        NaN      NaN   \n",
       "\n",
       "      Day duration  FeatureX FeatureY Countries_ID  User sign-in time  \\\n",
       "0              3.0       0.0      0.0         EOMO    19:37:04.756448   \n",
       "1              3.0       0.0      0.0         EOMO    19:37:04.756448   \n",
       "2              0.0       0.0      3.0          NaN    21:11:55.295294   \n",
       "3              0.0       0.0      3.0          NaN    21:11:55.295294   \n",
       "4              0.0       0.0      3.0          NaN    21:11:55.295294   \n",
       "...            ...       ...      ...          ...                ...   \n",
       "19837          NaN       0.0      0.0         7JT1    04:04:35.100180   \n",
       "19838          NaN       0.0      0.0         UKC2    13:30:49.814791   \n",
       "19839          NaN       0.0      0.0         H801    15:54:12.676400   \n",
       "19840          NaN       0.0      0.0         NRDP    09:49:35.535847   \n",
       "19841          NaN       0.0      1.0         7DKX    05:02:39.352427   \n",
       "\n",
       "       User sign-in month  User sign-in day  \n",
       "0                      11              20.0  \n",
       "1                      11              20.0  \n",
       "2                       4               7.0  \n",
       "3                       4               7.0  \n",
       "4                       4               7.0  \n",
       "...                   ...               ...  \n",
       "19837                   3              26.0  \n",
       "19838                  11               5.0  \n",
       "19839                   5              11.0  \n",
       "19840                   4              28.0  \n",
       "19841                  11               6.0  \n",
       "\n",
       "[19842 rows x 55 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7dd7b76",
   "metadata": {},
   "source": [
    "# DATA CLEANING AND TRANSFORMATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "edcc2b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19842, 55)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8e00f64b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11457"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['Participant Type'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "19c7bc29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1., nan])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[\"Participant Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "33cf336e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Participant Type'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "999010a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'count 10', 'count 6', 'count 8', 'count 9', 'count 7',\n",
       "       'count 4', 'count 3', 'count 5'], dtype=object)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[\"Successful Submission Count\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "fc5214b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18393"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['Successful Submission Count'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "53b6a4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Successful Submission Count'] = df_final['Successful Submission Count'].str[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "044e0032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '10', '6', '8', '9', '7', '4', '3', '5'], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['Successful Submission Count'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5b34c507",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Successful Submission Count'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "cfb08778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20.,  7., 30., 23., 16., 15., 19., 26., 17., 21.,  9.,  4.,  2.,\n",
       "       28., 25., 22.,  6., 24., 13., 18.,  3., 29., 12., 27.,  5.,  8.,\n",
       "        1., 10., 14., 31., 11., nan])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['Comp_participation_day'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "2c878b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Comp_participation_day'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9d9d90d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11457"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureA'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "fa424d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[1]', '[3, 2]', '[1, 6]', '[2, 3]', '[2]', '[4]', '[5]', '[]',\n",
       "       '[7]', '[2, 4]', '[4, 2]', '[1, 7]', '[5, 4]', '[5, 2]',\n",
       "       '[4, 7, 2]', '[3, 7]', '[1, 4, 5]', '[3]', '[2, 4, 5]', '[8]',\n",
       "       '[1, 9]', '[2, 7]', '[1, 5, 4]', '[7, 3, 2]', nan], dtype=object)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureA'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "173a1668",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureA'].fillna('[-1]', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "0f61033d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureA'] = df_final.FeatureA.map(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "e9969826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureA'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "2aa56679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[12, 16]', '[]', '[5, 6]', '[9]', '[1]', '[14]', '[7]', '[15]',\n",
       "       '[5]', '[7, 5]', '[2]', '[6]', '[5, 7]', '[16]', '[14, 7]', '[10]',\n",
       "       '[12]', '[14, 8]', nan], dtype=object)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "0d2e61c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureB'].fillna('[-1]', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "1a28ef18",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureB'] = df_final.FeatureB.map(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "8c6dd234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureB'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "be9e405c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureB'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "7f1959ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  6.,  9.,  8.,  3., 26., 25.,  2., 37., 29., 12.,  4., 10.,\n",
       "       13.,  5., 35.,  7., 11., 28., 15., 24., 14.,  1., 27., 20., 31.,\n",
       "       16., 18., 17.])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureC'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "95be403d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureC'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5bf0c2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0., nan])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureD'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c8165638",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureD'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "bea84a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  1.,  3., nan])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "14e47be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureE'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "e86ce0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[1, 5]', '[2]', '[7]', '[1]', '[1, 12]', '[2, 3]', '[]', '[3, 2]',\n",
       "       '[2, 8]', '[10]', '[3]', '[2, 11]', '[2, 13]', '[6]', '[9]',\n",
       "       '[3, 1]', '[15]', '[1, 3]', '[2, 1]', '[8]', '[1, 11]', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureF'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "7ff2c5fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureF'].fillna('[-1]', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0ee10e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureF'] = df_final.FeatureF.map(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "ac1bc586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureF'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "5854f711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureF'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "06c6d03b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[5]', '[]', '[4]', '[3]', '[5, 3, 4]', nan], dtype=object)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureG'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d0abe41a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureG'].fillna('[-1]', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "854a4de3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureG'] = df_final.FeatureG.map(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "7f0db25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureG'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "2c29cfbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureG'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "78cc5f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[1]', '[2]', '[7]', '[]', '[2, 3]', '[3, 2]', '[3]', '[2, 8]',\n",
       "       '[10]', '[6]', '[9]', '[3, 1]', '[8, 10]', '[1, 3]', '[2, 1]',\n",
       "       '[8]', nan], dtype=object)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureH'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "a9f33892",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureH'].fillna('[-1]', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "eacb0bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureH'] = df_final.FeatureH.map(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "997ceac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureH'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "c866bdb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureH'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c8099462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  6.,  9.,  8.,  3., 26., 25.,  2., 37., 29., 12.,  4., 10.,\n",
       "       13.,  5., 35.,  7., 31., 11., 28., 15., 24., 14., 33.,  1., 27.,\n",
       "       20., 16., 18., 17.])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureI'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "452eaaa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureI'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "1626e422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureX'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b1eb2904",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureX'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "78bcc98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  3.,  1., nan])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['FeatureY'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "f2c85e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureY'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8d810851",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df_final)):\n",
    "    arr = df_final.loc[i, \"FeatureA\"]\n",
    "    mean = np.mean(arr)\n",
    "    df_final.loc[i, \"FeatureA\"] = mean\n",
    "    arr = df_final.loc[i, \"FeatureB\"]\n",
    "    mean = np.mean(arr)\n",
    "    df_final.loc[i, \"FeatureB\"] = mean\n",
    "    arr = df_final.loc[i, \"FeatureF\"]\n",
    "    mean = np.mean(arr)\n",
    "    df_final.loc[i, \"FeatureF\"] = mean\n",
    "    arr = df_final.loc[i, \"FeatureG\"]\n",
    "    mean = np.mean(arr)\n",
    "    df_final.loc[i, \"FeatureG\"] = mean\n",
    "    arr = df_final.loc[i, \"FeatureH\"]\n",
    "    mean = np.mean(arr)\n",
    "    df_final.loc[i, \"FeatureH\"] = mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "5838bd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureA'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "d32362c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureB'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "842fb6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureF'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5c7d1b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureG'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "4e3da499",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['FeatureH'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "4c6cec9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11457"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['SecretCode'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c1e26e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['SecretCode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ba7de0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['SecretCode'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "28d4770a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00:00:00', '23:59:00', nan, '14:00:00', '22:59:00', '15:00:00',\n",
       "       '11:59:00', '06:00:00', '11:00:00', '05:59:00', '12:00:00',\n",
       "       '09:30:00', '23:09:00', '16:00:00', '02:00:00', '07:00:00',\n",
       "       '18:00:00', '23:30:00'], dtype=object)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_ended_time'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "cd7dae07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop('competition_ended_time', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "8dc88c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.000e+00, 1.000e+00,       nan, 2.000e+00, 4.000e+00, 2.023e+03,\n",
       "       5.000e+00])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_ended_year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "c55ac0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['competition_ended_year'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b2811b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9., 12., nan, 10.,  1.,  4.,  6.,  3., 11.,  2.,  7.,  5.,  8.])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_ended_month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "fa2b466f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['competition_ended_month'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "2ceca791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.,  1., nan,  2., 27., 13., 28., 23., 10., 16., 26.,  4., 14.,\n",
       "        3., 30.,  7., 21.,  8., 25., 18.,  5., 29., 31., 17., 12., 22.,\n",
       "       19., 15.,  9.,  6., 20.])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_ended_day'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "09832490",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['competition_ended_day'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "22c426e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00:00:00', '06:00:00', '08:00:00', '04:00:00', '12:00:00',\n",
       "       '20:00:00', '00:01:00', '09:00:00', '22:59:00', '05:00:00',\n",
       "       '11:00:00', '23:59:00', '10:00:00', '07:00:00', '02:00:00',\n",
       "       '23:00:00', '14:00:00', '09:10:00', '09:30:00', '15:00:00',\n",
       "       '05:13:00', '13:30:00', '01:59:00', '10:30:00', '17:00:00',\n",
       "       '13:00:00', '05:59:00', nan], dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_started_time'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "53654de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop('competition_started_time', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "627fcf41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  1.,  3.,  2.,  5., nan])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_started_year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "3c359fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['competition_started_year'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "982671cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.,  9.,  2., 10.,  4.,  3., 12.,  5.,  1., 11.,  8.,  7., nan])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_started_month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "7b1880e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['competition_started_month'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "84674d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.,  4.,  9., 29., 20., 26.,  1., 27., 11., 18., 23., 15.,  3.,\n",
       "       19., 14.,  5., 22., 24.,  6., 16., 13., 28., 30.,  7.,  2., 17.,\n",
       "       21., 12., nan])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['competition_started_day'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "409b1c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['competition_started_day'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "21f1373d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.rename(columns={'datetime Day_of_month': 'Day last active'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "dda92750",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop('User sign-in time', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "ab3f0138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.,  7., nan, 16., 26., 30.,  9.,  2., 29., 25., 31., 28., 13.,\n",
       "       15., 19., 27., 20., 24., 10., 21., 17.,  1.,  6., 22., 18.,  8.,\n",
       "       11., 12.,  3., 14.,  4.,  5.])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['Day last active'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "6d107de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['competition_started_day'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "18c03d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2710"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['count_UA'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "0f3b08b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "268"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_final['count_UA'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "fce902e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['count_UA'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "3a0d93a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5622"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['User sign-in day'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "1d991b12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20.,  7., 30., 23., 15., 19., nan, 16., 26.,  9.,  4.,  2., 25.,\n",
       "       22.,  6., 13., 18.,  3., 29., 11.,  8.,  1., 17., 12., 10., 14.,\n",
       "       24.,  5., 27., 21., 31., 28.])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['User sign-in day'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "f924906b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['User sign-in day'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "7caa4a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2710"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['Day duration'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "b414eec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_final['Day duration'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "5a8318c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Day duration'].fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "3dba2740",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Day duration'] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "db2cc5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Successful Submission Count'] = pd.to_numeric(df_final['Successful Submission Count'])\n",
    "df_final['User sign-in month'] = pd.to_numeric(df_final['User sign-in month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "bba0fee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final[\"User sign-in month\"].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "9b968c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final[\"Day last active\"].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "d913a6e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(\"Countries_ID\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "3f795382",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(\"User sign-in month\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "0797034f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.rename(columns={'Title': 'Activity Type'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "7a438a00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(\"competition_ended_year\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "a3e4df71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(\"competition_started_year\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "3050a486",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(\"competition_ended_day\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "2505ccbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(\"competition_started_day\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "46b11db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19842 entries, 0 to 19841\n",
      "Data columns (total 46 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   User_ID                          19842 non-null  object \n",
      " 1   Participant Type                 19842 non-null  float64\n",
      " 2   Successful Submission Count      19842 non-null  int64  \n",
      " 3   Created At time                  8385 non-null   object \n",
      " 4   Comp_participation_day           19842 non-null  float64\n",
      " 5   FeatureA                         19842 non-null  float64\n",
      " 6   FeatureB                         19842 non-null  float64\n",
      " 7   FeatureC                         19842 non-null  float64\n",
      " 8   FeatureD                         19842 non-null  float64\n",
      " 9   FeatureE                         19842 non-null  float64\n",
      " 10  SecretCode                       19842 non-null  float64\n",
      " 11  FeatureF                         19842 non-null  float64\n",
      " 12  FeatureG                         19842 non-null  float64\n",
      " 13  FeatureH                         19842 non-null  float64\n",
      " 14  FeatureI                         19842 non-null  float64\n",
      " 15  Country_ID                       4073 non-null   object \n",
      " 16  competition_ended_month          19842 non-null  float64\n",
      " 17  competition_started_month        19842 non-null  float64\n",
      " 18  Day last active                  19842 non-null  float64\n",
      " 19  Downloaded_Competition_Datafile  17132 non-null  float64\n",
      " 20  Viewed_All_Discussions           17132 non-null  float64\n",
      " 21  Confirmed_Email                  17132 non-null  float64\n",
      " 22  Joined_Competition               17132 non-null  float64\n",
      " 23  Signed_Up                        17132 non-null  float64\n",
      " 24  Viewed_All_Competitions          17132 non-null  float64\n",
      " 25  Viewed_All_Learning_Pages        17132 non-null  float64\n",
      " 26  badge_OCZE                       17132 non-null  float64\n",
      " 27  create_alias                     17132 non-null  float64\n",
      " 28  identify                         17132 non-null  float64\n",
      " 29  comp_ID                          17132 non-null  float64\n",
      " 30  Signed_In                        17132 non-null  float64\n",
      " 31  job_ID                           17132 non-null  float64\n",
      " 32  Updated_Discussion               17132 non-null  float64\n",
      " 33  Viewed_All_Jobs                  17132 non-null  float64\n",
      " 34  Created_Submission               17132 non-null  float64\n",
      " 35  Signed_Out                       17132 non-null  float64\n",
      " 36  Updated_Profile                  17132 non-null  float64\n",
      " 37  blog_ID                          17132 non-null  float64\n",
      " 38  Viewed_Discussion                17132 non-null  float64\n",
      " 39  Updated_Submission               17132 non-null  float64\n",
      " 40  badge_HYIO                       17132 non-null  float64\n",
      " 41  count_UA                         19842 non-null  float64\n",
      " 42  Day duration                     19842 non-null  float64\n",
      " 43  FeatureX                         19842 non-null  float64\n",
      " 44  FeatureY                         19842 non-null  float64\n",
      " 45  User sign-in day                 19842 non-null  float64\n",
      "dtypes: float64(42), int64(1), object(3)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e1619e",
   "metadata": {},
   "source": [
    "### Label Encoder for Country_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "cc13ba1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'ZEWU', 'DYYF', 'H03V', 'KYT4', 'G3F0', 'SE4K', 'X9GR',\n",
       "       '03GB', 'GYYX', 'YSLJ', 'N6EN', 'ISIQ', 'EGI9', '4PRY'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[\"Country_ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "082ea9de",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "6111974b",
   "metadata": {},
   "outputs": [
    {
     "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>LabelEncoder()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.fit(df_final[\"Country_ID\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "e1c0a3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final[\"Country_ID\"] = le.transform(df_final[\"Country_ID\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "077cf945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14, 13,  2,  6,  8,  4, 10, 11,  0,  5, 12,  9,  7,  3,  1])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[\"Country_ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "58ada006",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19842 entries, 0 to 19841\n",
      "Data columns (total 46 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   User_ID                          19842 non-null  object \n",
      " 1   Participant Type                 19842 non-null  float64\n",
      " 2   Successful Submission Count      19842 non-null  int64  \n",
      " 3   Created At time                  8385 non-null   object \n",
      " 4   Comp_participation_day           19842 non-null  float64\n",
      " 5   FeatureA                         19842 non-null  float64\n",
      " 6   FeatureB                         19842 non-null  float64\n",
      " 7   FeatureC                         19842 non-null  float64\n",
      " 8   FeatureD                         19842 non-null  float64\n",
      " 9   FeatureE                         19842 non-null  float64\n",
      " 10  SecretCode                       19842 non-null  float64\n",
      " 11  FeatureF                         19842 non-null  float64\n",
      " 12  FeatureG                         19842 non-null  float64\n",
      " 13  FeatureH                         19842 non-null  float64\n",
      " 14  FeatureI                         19842 non-null  float64\n",
      " 15  Country_ID                       19842 non-null  int32  \n",
      " 16  competition_ended_month          19842 non-null  float64\n",
      " 17  competition_started_month        19842 non-null  float64\n",
      " 18  Day last active                  19842 non-null  float64\n",
      " 19  Downloaded_Competition_Datafile  17132 non-null  float64\n",
      " 20  Viewed_All_Discussions           17132 non-null  float64\n",
      " 21  Confirmed_Email                  17132 non-null  float64\n",
      " 22  Joined_Competition               17132 non-null  float64\n",
      " 23  Signed_Up                        17132 non-null  float64\n",
      " 24  Viewed_All_Competitions          17132 non-null  float64\n",
      " 25  Viewed_All_Learning_Pages        17132 non-null  float64\n",
      " 26  badge_OCZE                       17132 non-null  float64\n",
      " 27  create_alias                     17132 non-null  float64\n",
      " 28  identify                         17132 non-null  float64\n",
      " 29  comp_ID                          17132 non-null  float64\n",
      " 30  Signed_In                        17132 non-null  float64\n",
      " 31  job_ID                           17132 non-null  float64\n",
      " 32  Updated_Discussion               17132 non-null  float64\n",
      " 33  Viewed_All_Jobs                  17132 non-null  float64\n",
      " 34  Created_Submission               17132 non-null  float64\n",
      " 35  Signed_Out                       17132 non-null  float64\n",
      " 36  Updated_Profile                  17132 non-null  float64\n",
      " 37  blog_ID                          17132 non-null  float64\n",
      " 38  Viewed_Discussion                17132 non-null  float64\n",
      " 39  Updated_Submission               17132 non-null  float64\n",
      " 40  badge_HYIO                       17132 non-null  float64\n",
      " 41  count_UA                         19842 non-null  float64\n",
      " 42  Day duration                     19842 non-null  float64\n",
      " 43  FeatureX                         19842 non-null  float64\n",
      " 44  FeatureY                         19842 non-null  float64\n",
      " 45  User sign-in day                 19842 non-null  float64\n",
      "dtypes: float64(42), int32(1), int64(1), object(2)\n",
      "memory usage: 7.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "b64fb5bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17362"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_final[\"User_ID\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "f874d1d8",
   "metadata": {},
   "outputs": [
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       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19841</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19842 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID  Participant Type  Successful Submission Count  \\\n",
       "0      ID_40G01FGV_Month_11               1.0                            0   \n",
       "1      ID_40G01FGV_Month_11               1.0                            0   \n",
       "2       ID_D5I1M68U_Month_4               1.0                            0   \n",
       "3       ID_D5I1M68U_Month_4               1.0                            0   \n",
       "4       ID_D5I1M68U_Month_4               1.0                            0   \n",
       "...                     ...               ...                          ...   \n",
       "19837   ID_Y02FKZWI_Month_3               0.0                            0   \n",
       "19838  ID_FDOGWALS_Month_11               0.0                            0   \n",
       "19839   ID_YJCY8FF5_Month_5               0.0                            0   \n",
       "19840   ID_QDL8A9FQ_Month_4               0.0                            0   \n",
       "19841  ID_C5393PD0_Month_11               0.0                            0   \n",
       "\n",
       "       Created At time  Comp_participation_day  FeatureA  FeatureB  FeatureC  \\\n",
       "0      19:38:04.689865                    20.0       1.0      14.0      -1.0   \n",
       "1      19:38:04.670583                    20.0       1.0      14.0      -1.0   \n",
       "2      21:15:54.617702                     7.0       1.0      14.0      -1.0   \n",
       "3      21:15:54.596519                     7.0       1.0      14.0      -1.0   \n",
       "4      21:43:19.694875                     7.0       2.5       0.0       6.0   \n",
       "...                ...                     ...       ...       ...       ...   \n",
       "19837              NaN                     0.0      -1.0      -1.0      -1.0   \n",
       "19838              NaN                     0.0      -1.0      -1.0      -1.0   \n",
       "19839              NaN                     0.0      -1.0      -1.0      -1.0   \n",
       "19840              NaN                     0.0      -1.0      -1.0      -1.0   \n",
       "19841              NaN                     0.0      -1.0      -1.0      -1.0   \n",
       "\n",
       "       FeatureD  FeatureE  ...  Updated_Profile  blog_ID  Viewed_Discussion  \\\n",
       "0           1.0       2.0  ...              0.0      0.0                0.0   \n",
       "1           1.0       1.0  ...              0.0      0.0                0.0   \n",
       "2           1.0       2.0  ...              0.0      0.0                0.0   \n",
       "3           1.0       1.0  ...              0.0      0.0                0.0   \n",
       "4           1.0       3.0  ...              0.0      0.0                0.0   \n",
       "...         ...       ...  ...              ...      ...                ...   \n",
       "19837      -1.0      -1.0  ...              NaN      NaN                NaN   \n",
       "19838      -1.0      -1.0  ...              NaN      NaN                NaN   \n",
       "19839      -1.0      -1.0  ...              NaN      NaN                NaN   \n",
       "19840      -1.0      -1.0  ...              NaN      NaN                NaN   \n",
       "19841      -1.0      -1.0  ...              NaN      NaN                NaN   \n",
       "\n",
       "       Updated_Submission  badge_HYIO  count_UA  Day duration  FeatureX  \\\n",
       "0                     0.0         0.0      21.0           4.0       0.0   \n",
       "1                     0.0         0.0      21.0           4.0       0.0   \n",
       "2                     0.0         0.0      22.0           1.0       0.0   \n",
       "3                     0.0         0.0      22.0           1.0       0.0   \n",
       "4                     0.0         0.0      22.0           1.0       0.0   \n",
       "...                   ...         ...       ...           ...       ...   \n",
       "19837                 NaN         NaN       0.0           0.0       0.0   \n",
       "19838                 NaN         NaN       0.0           0.0       0.0   \n",
       "19839                 NaN         NaN       0.0           0.0       0.0   \n",
       "19840                 NaN         NaN       0.0           0.0       0.0   \n",
       "19841                 NaN         NaN       0.0           0.0       0.0   \n",
       "\n",
       "       FeatureY  User sign-in day  \n",
       "0           0.0              20.0  \n",
       "1           0.0              20.0  \n",
       "2           3.0               7.0  \n",
       "3           3.0               7.0  \n",
       "4           3.0               7.0  \n",
       "...         ...               ...  \n",
       "19837       0.0              26.0  \n",
       "19838       0.0               5.0  \n",
       "19839       0.0              11.0  \n",
       "19840       0.0              28.0  \n",
       "19841       1.0               6.0  \n",
       "\n",
       "[19842 rows x 46 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "5aea350f",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df_final.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "920d1381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17362"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(temp[\"User_ID\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "8dcd13eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.groupby(\"User_ID\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "788815f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Participant Type</th>\n",
       "      <th>Successful Submission Count</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>FeatureE</th>\n",
       "      <th>SecretCode</th>\n",
       "      <th>FeatureF</th>\n",
       "      <th>...</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>count_UA</th>\n",
       "      <th>Day duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>User sign-in day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID_000H9XOP_Month_5</th>\n",
       "      <td>0.0</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>-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>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_003YJHQA_Month_4</th>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</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>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_004UKSJV_Month_1</th>\n",
       "      <td>0.0</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>-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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_004UKSJV_Month_12</th>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</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>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_008D2J6C_Month_1</th>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</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>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</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>ID_ZZMHNLNT_Month_4</th>\n",
       "      <td>0.0</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>-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>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_ZZQK0LU6_Month_3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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>67.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_ZZUNZJ2U_Month_2</th>\n",
       "      <td>0.0</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>-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>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_ZZVM2KO7_Month_4</th>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_ZZVUJ45W_Month_3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</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>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17362 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Participant Type  Successful Submission Count  \\\n",
       "User_ID                                                               \n",
       "ID_000H9XOP_Month_5                0.0                          0.0   \n",
       "ID_003YJHQA_Month_4                0.0                          0.0   \n",
       "ID_004UKSJV_Month_1                0.0                          0.0   \n",
       "ID_004UKSJV_Month_12               0.0                          0.0   \n",
       "ID_008D2J6C_Month_1                0.0                          0.0   \n",
       "...                                ...                          ...   \n",
       "ID_ZZMHNLNT_Month_4                0.0                          0.0   \n",
       "ID_ZZQK0LU6_Month_3                1.0                          0.0   \n",
       "ID_ZZUNZJ2U_Month_2                0.0                          0.0   \n",
       "ID_ZZVM2KO7_Month_4                0.0                          0.0   \n",
       "ID_ZZVUJ45W_Month_3                1.0                          0.0   \n",
       "\n",
       "                      Comp_participation_day  FeatureA  FeatureB  FeatureC  \\\n",
       "User_ID                                                                      \n",
       "ID_000H9XOP_Month_5                      0.0      -1.0      -1.0      -1.0   \n",
       "ID_003YJHQA_Month_4                      0.0      -1.0      -1.0      -1.0   \n",
       "ID_004UKSJV_Month_1                      0.0      -1.0      -1.0      -1.0   \n",
       "ID_004UKSJV_Month_12                     0.0      -1.0      -1.0      -1.0   \n",
       "ID_008D2J6C_Month_1                      0.0      -1.0      -1.0      -1.0   \n",
       "...                                      ...       ...       ...       ...   \n",
       "ID_ZZMHNLNT_Month_4                      0.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZQK0LU6_Month_3                     29.0       1.5       0.0      13.5   \n",
       "ID_ZZUNZJ2U_Month_2                      0.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZVM2KO7_Month_4                      0.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZVUJ45W_Month_3                      5.0       1.0       7.0       4.0   \n",
       "\n",
       "                      FeatureD  FeatureE  SecretCode  FeatureF  ...  \\\n",
       "User_ID                                                         ...   \n",
       "ID_000H9XOP_Month_5       -1.0      -1.0        -1.0      -1.0  ...   \n",
       "ID_003YJHQA_Month_4       -1.0      -1.0        -1.0      -1.0  ...   \n",
       "ID_004UKSJV_Month_1       -1.0      -1.0        -1.0      -1.0  ...   \n",
       "ID_004UKSJV_Month_12      -1.0      -1.0        -1.0      -1.0  ...   \n",
       "ID_008D2J6C_Month_1       -1.0      -1.0        -1.0      -1.0  ...   \n",
       "...                        ...       ...         ...       ...  ...   \n",
       "ID_ZZMHNLNT_Month_4       -1.0      -1.0        -1.0      -1.0  ...   \n",
       "ID_ZZQK0LU6_Month_3        0.0       2.0         1.0       3.0  ...   \n",
       "ID_ZZUNZJ2U_Month_2       -1.0      -1.0        -1.0      -1.0  ...   \n",
       "ID_ZZVM2KO7_Month_4       -1.0      -1.0        -1.0      -1.0  ...   \n",
       "ID_ZZVUJ45W_Month_3        1.0       1.0         0.0       1.0  ...   \n",
       "\n",
       "                      Updated_Profile  blog_ID  Viewed_Discussion  \\\n",
       "User_ID                                                             \n",
       "ID_000H9XOP_Month_5               0.0      0.0                0.0   \n",
       "ID_003YJHQA_Month_4               2.0      0.0                0.0   \n",
       "ID_004UKSJV_Month_1               0.0      0.0                0.0   \n",
       "ID_004UKSJV_Month_12              4.0      0.0                0.0   \n",
       "ID_008D2J6C_Month_1               2.0      0.0                0.0   \n",
       "...                               ...      ...                ...   \n",
       "ID_ZZMHNLNT_Month_4               0.0      0.0                0.0   \n",
       "ID_ZZQK0LU6_Month_3               0.0      0.0                0.0   \n",
       "ID_ZZUNZJ2U_Month_2               0.0      0.0                0.0   \n",
       "ID_ZZVM2KO7_Month_4               NaN      NaN                NaN   \n",
       "ID_ZZVUJ45W_Month_3               0.0      0.0                0.0   \n",
       "\n",
       "                      Updated_Submission  badge_HYIO  count_UA  Day duration  \\\n",
       "User_ID                                                                        \n",
       "ID_000H9XOP_Month_5                  0.0         0.0       6.0           5.0   \n",
       "ID_003YJHQA_Month_4                  0.0         0.0      11.0           1.0   \n",
       "ID_004UKSJV_Month_1                  0.0         0.0       1.0           1.0   \n",
       "ID_004UKSJV_Month_12                 0.0         0.0      11.0           2.0   \n",
       "ID_008D2J6C_Month_1                  0.0         0.0      10.0           9.0   \n",
       "...                                  ...         ...       ...           ...   \n",
       "ID_ZZMHNLNT_Month_4                  0.0         0.0       8.0           1.0   \n",
       "ID_ZZQK0LU6_Month_3                  0.0         0.0      67.0           2.0   \n",
       "ID_ZZUNZJ2U_Month_2                  0.0         0.0      12.0           1.0   \n",
       "ID_ZZVM2KO7_Month_4                  NaN         NaN       0.0           0.0   \n",
       "ID_ZZVUJ45W_Month_3                  0.0         0.0      13.0           1.0   \n",
       "\n",
       "                      FeatureX  FeatureY  User sign-in day  \n",
       "User_ID                                                     \n",
       "ID_000H9XOP_Month_5        0.0       1.0              22.0  \n",
       "ID_003YJHQA_Month_4        0.0       1.0              12.0  \n",
       "ID_004UKSJV_Month_1       -1.0      -1.0               0.0  \n",
       "ID_004UKSJV_Month_12       0.0       0.0              28.0  \n",
       "ID_008D2J6C_Month_1        0.0       0.0               7.0  \n",
       "...                        ...       ...               ...  \n",
       "ID_ZZMHNLNT_Month_4        0.0       0.0               1.0  \n",
       "ID_ZZQK0LU6_Month_3        0.0       3.0              29.0  \n",
       "ID_ZZUNZJ2U_Month_2        0.0       0.0              30.0  \n",
       "ID_ZZVM2KO7_Month_4        0.0       0.0              22.0  \n",
       "ID_ZZVUJ45W_Month_3        0.0       0.0               5.0  \n",
       "\n",
       "[17362 rows x 44 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.sort_values(\"User_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "b444df2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_max = df_final.groupby('User_ID').max()\n",
    "df_sum = df_final.groupby('User_ID').sum()\n",
    "df_mean = df_final.groupby('User_ID').mean()\n",
    "df_min = df_final.groupby('User_ID').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "dc1aca50",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "918881e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final[\"count_UA\"] = df_sum[\"count_UA\"]\n",
    "df_final[\"Day last active\"] = df_max[\"Day last active\"]\n",
    "df_final[\"User sign-in day\"] = df_min[\"User sign-in day\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "fae3b241",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "5d0a7885",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.columns = df_final.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "83caa20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "4bff62c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17362 entries, 0 to 17361\n",
      "Data columns (total 45 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   User_ID                          17362 non-null  object \n",
      " 1   Participant_Type                 17362 non-null  float64\n",
      " 2   Successful_Submission_Count      17362 non-null  float64\n",
      " 3   Comp_participation_day           17362 non-null  float64\n",
      " 4   FeatureA                         17362 non-null  float64\n",
      " 5   FeatureB                         17362 non-null  float64\n",
      " 6   FeatureC                         17362 non-null  float64\n",
      " 7   FeatureD                         17362 non-null  float64\n",
      " 8   FeatureE                         17362 non-null  float64\n",
      " 9   SecretCode                       17362 non-null  float64\n",
      " 10  FeatureF                         17362 non-null  float64\n",
      " 11  FeatureG                         17362 non-null  float64\n",
      " 12  FeatureH                         17362 non-null  float64\n",
      " 13  FeatureI                         17362 non-null  float64\n",
      " 14  Country_ID                       17362 non-null  float64\n",
      " 15  competition_ended_month          17362 non-null  float64\n",
      " 16  competition_started_month        17362 non-null  float64\n",
      " 17  Day_last_active                  17362 non-null  float64\n",
      " 18  Downloaded_Competition_Datafile  17362 non-null  float64\n",
      " 19  Viewed_All_Discussions           17362 non-null  float64\n",
      " 20  Confirmed_Email                  17362 non-null  float64\n",
      " 21  Joined_Competition               17362 non-null  float64\n",
      " 22  Signed_Up                        17362 non-null  float64\n",
      " 23  Viewed_All_Competitions          17362 non-null  float64\n",
      " 24  Viewed_All_Learning_Pages        17362 non-null  float64\n",
      " 25  badge_OCZE                       17362 non-null  float64\n",
      " 26  create_alias                     17362 non-null  float64\n",
      " 27  identify                         17362 non-null  float64\n",
      " 28  comp_ID                          17362 non-null  float64\n",
      " 29  Signed_In                        17362 non-null  float64\n",
      " 30  job_ID                           17362 non-null  float64\n",
      " 31  Updated_Discussion               17362 non-null  float64\n",
      " 32  Viewed_All_Jobs                  17362 non-null  float64\n",
      " 33  Created_Submission               17362 non-null  float64\n",
      " 34  Signed_Out                       17362 non-null  float64\n",
      " 35  Updated_Profile                  17362 non-null  float64\n",
      " 36  blog_ID                          17362 non-null  float64\n",
      " 37  Viewed_Discussion                17362 non-null  float64\n",
      " 38  Updated_Submission               17362 non-null  float64\n",
      " 39  badge_HYIO                       17362 non-null  float64\n",
      " 40  count_UA                         17362 non-null  float64\n",
      " 41  Day_duration                     17362 non-null  float64\n",
      " 42  FeatureX                         17362 non-null  float64\n",
      " 43  FeatureY                         17362 non-null  float64\n",
      " 44  User_sign-in_day                 17362 non-null  float64\n",
      "dtypes: float64(44), object(1)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "4c956eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Participant_Type</th>\n",
       "      <th>Successful_Submission_Count</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>FeatureE</th>\n",
       "      <th>SecretCode</th>\n",
       "      <th>...</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>count_UA</th>\n",
       "      <th>Day_duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>User_sign-in_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_000H9XOP_Month_5</td>\n",
       "      <td>0.0</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>-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>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_003YJHQA_Month_4</td>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</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>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_004UKSJV_Month_1</td>\n",
       "      <td>0.0</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>-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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_004UKSJV_Month_12</td>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</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>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_008D2J6C_Month_1</td>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</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>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</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>17357</th>\n",
       "      <td>ID_ZZMHNLNT_Month_4</td>\n",
       "      <td>0.0</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>-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>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17358</th>\n",
       "      <td>ID_ZZQK0LU6_Month_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</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>134.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17359</th>\n",
       "      <td>ID_ZZUNZJ2U_Month_2</td>\n",
       "      <td>0.0</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>-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>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17360</th>\n",
       "      <td>ID_ZZVM2KO7_Month_4</td>\n",
       "      <td>0.0</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>-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>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17361</th>\n",
       "      <td>ID_ZZVUJ45W_Month_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</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>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17362 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    User_ID  Participant_Type  Successful_Submission_Count  \\\n",
       "0       ID_000H9XOP_Month_5               0.0                          0.0   \n",
       "1       ID_003YJHQA_Month_4               0.0                          0.0   \n",
       "2       ID_004UKSJV_Month_1               0.0                          0.0   \n",
       "3      ID_004UKSJV_Month_12               0.0                          0.0   \n",
       "4       ID_008D2J6C_Month_1               0.0                          0.0   \n",
       "...                     ...               ...                          ...   \n",
       "17357   ID_ZZMHNLNT_Month_4               0.0                          0.0   \n",
       "17358   ID_ZZQK0LU6_Month_3               1.0                          0.0   \n",
       "17359   ID_ZZUNZJ2U_Month_2               0.0                          0.0   \n",
       "17360   ID_ZZVM2KO7_Month_4               0.0                          0.0   \n",
       "17361   ID_ZZVUJ45W_Month_3               1.0                          0.0   \n",
       "\n",
       "       Comp_participation_day  FeatureA  FeatureB  FeatureC  FeatureD  \\\n",
       "0                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "1                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "2                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "3                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "4                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "17357                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "17358                    29.0       1.5       0.0      13.5       0.0   \n",
       "17359                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "17360                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "17361                     5.0       1.0       7.0       4.0       1.0   \n",
       "\n",
       "       FeatureE  SecretCode  ...  Updated_Profile  blog_ID  Viewed_Discussion  \\\n",
       "0          -1.0        -1.0  ...              0.0      0.0                0.0   \n",
       "1          -1.0        -1.0  ...              2.0      0.0                0.0   \n",
       "2          -1.0        -1.0  ...              0.0      0.0                0.0   \n",
       "3          -1.0        -1.0  ...              4.0      0.0                0.0   \n",
       "4          -1.0        -1.0  ...              2.0      0.0                0.0   \n",
       "...         ...         ...  ...              ...      ...                ...   \n",
       "17357      -1.0        -1.0  ...              0.0      0.0                0.0   \n",
       "17358       2.0         1.0  ...              0.0      0.0                0.0   \n",
       "17359      -1.0        -1.0  ...              0.0      0.0                0.0   \n",
       "17360      -1.0        -1.0  ...              0.0      0.0                0.0   \n",
       "17361       1.0         0.0  ...              0.0      0.0                0.0   \n",
       "\n",
       "       Updated_Submission  badge_HYIO  count_UA  Day_duration  FeatureX  \\\n",
       "0                     0.0         0.0       6.0           5.0       0.0   \n",
       "1                     0.0         0.0      11.0           1.0       0.0   \n",
       "2                     0.0         0.0       1.0           1.0      -1.0   \n",
       "3                     0.0         0.0      11.0           2.0       0.0   \n",
       "4                     0.0         0.0      10.0           9.0       0.0   \n",
       "...                   ...         ...       ...           ...       ...   \n",
       "17357                 0.0         0.0       8.0           1.0       0.0   \n",
       "17358                 0.0         0.0     134.0           2.0       0.0   \n",
       "17359                 0.0         0.0      12.0           1.0       0.0   \n",
       "17360                 0.0         0.0       0.0           0.0       0.0   \n",
       "17361                 0.0         0.0      52.0           1.0       0.0   \n",
       "\n",
       "       FeatureY  User_sign-in_day  \n",
       "0           1.0              22.0  \n",
       "1           1.0              12.0  \n",
       "2          -1.0               0.0  \n",
       "3           0.0              28.0  \n",
       "4           0.0               7.0  \n",
       "...         ...               ...  \n",
       "17357       0.0               1.0  \n",
       "17358       3.0              29.0  \n",
       "17359       0.0              30.0  \n",
       "17360       0.0              22.0  \n",
       "17361       0.0               5.0  \n",
       "\n",
       "[17362 rows x 45 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "ffaa0422",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('DataLast.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245d91e5",
   "metadata": {},
   "source": [
    "## Groupby User_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "6f817477",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df_final.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "d28ed748",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"User_ID\"] = temp[\"User_ID\"].str[0:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "1d4a1bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12413"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(temp[\"User_ID\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "322545c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.groupby(\"User_ID\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "e9c12b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>Participant_Type</th>\n",
       "      <th>Successful_Submission_Count</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>FeatureC</th>\n",
       "      <th>FeatureD</th>\n",
       "      <th>FeatureE</th>\n",
       "      <th>SecretCode</th>\n",
       "      <th>FeatureF</th>\n",
       "      <th>...</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>count_UA</th>\n",
       "      <th>Day_duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>User_sign-in_day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID_000H9XOP</th>\n",
       "      <td>0.0</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>-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>6.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_003YJHQA</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>-1.0</td>\n",
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       "      <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>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_004UKSJV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>-1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_008D2J6C</th>\n",
       "      <td>0.0</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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</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>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>ID_00ETFE7J</th>\n",
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       "      <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>-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>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>ID_ZZQK0LU6</th>\n",
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       "      <td>29.0</td>\n",
       "      <td>1.5</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>ID_ZZUNZJ2U</th>\n",
       "      <td>0.0</td>\n",
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       "      <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>-1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_ZZVM2KO7</th>\n",
       "      <td>0.0</td>\n",
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       "      <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>-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>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID_ZZVUJ45W</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</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>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12413 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Participant_Type  Successful_Submission_Count  \\\n",
       "User_ID                                                      \n",
       "ID_000H9XOP               0.0                          0.0   \n",
       "ID_003YJHQA               0.0                          0.0   \n",
       "ID_004UKSJV               0.0                          0.0   \n",
       "ID_008D2J6C               0.0                          0.0   \n",
       "ID_00ETFE7J               0.0                          0.0   \n",
       "...                       ...                          ...   \n",
       "ID_ZZMHNLNT               0.0                          0.0   \n",
       "ID_ZZQK0LU6               1.0                          0.0   \n",
       "ID_ZZUNZJ2U               0.0                          0.0   \n",
       "ID_ZZVM2KO7               0.0                          0.0   \n",
       "ID_ZZVUJ45W               1.0                          0.0   \n",
       "\n",
       "             Comp_participation_day  FeatureA  FeatureB  FeatureC  FeatureD  \\\n",
       "User_ID                                                                       \n",
       "ID_000H9XOP                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_003YJHQA                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_004UKSJV                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_008D2J6C                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_00ETFE7J                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "ID_ZZMHNLNT                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZQK0LU6                    29.0       1.5       0.0      13.5       0.0   \n",
       "ID_ZZUNZJ2U                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZVM2KO7                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZVUJ45W                     5.0       1.0       7.0       4.0       1.0   \n",
       "\n",
       "             FeatureE  SecretCode  FeatureF  ...  Updated_Profile  blog_ID  \\\n",
       "User_ID                                      ...                             \n",
       "ID_000H9XOP      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_003YJHQA      -1.0        -1.0      -1.0  ...              2.0      0.0   \n",
       "ID_004UKSJV      -1.0        -1.0      -1.0  ...              2.0      0.0   \n",
       "ID_008D2J6C      -1.0        -1.0      -1.0  ...              2.0      0.0   \n",
       "ID_00ETFE7J      -1.0        -1.0      -1.0  ...              0.0      1.0   \n",
       "...               ...         ...       ...  ...              ...      ...   \n",
       "ID_ZZMHNLNT      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_ZZQK0LU6       2.0         1.0       3.0  ...              0.0      0.0   \n",
       "ID_ZZUNZJ2U      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_ZZVM2KO7      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_ZZVUJ45W       1.0         0.0       1.0  ...              0.0      0.0   \n",
       "\n",
       "             Viewed_Discussion  Updated_Submission  badge_HYIO  count_UA  \\\n",
       "User_ID                                                                    \n",
       "ID_000H9XOP                0.0                 0.0         0.0       6.0   \n",
       "ID_003YJHQA                0.0                 0.0         0.0      11.0   \n",
       "ID_004UKSJV                0.0                 0.0         0.0       6.0   \n",
       "ID_008D2J6C                0.0                 0.0         0.0      10.0   \n",
       "ID_00ETFE7J                0.0                 0.0         0.0      10.0   \n",
       "...                        ...                 ...         ...       ...   \n",
       "ID_ZZMHNLNT                0.0                 0.0         0.0       8.0   \n",
       "ID_ZZQK0LU6                0.0                 0.0         0.0     134.0   \n",
       "ID_ZZUNZJ2U                0.0                 0.0         0.0      12.0   \n",
       "ID_ZZVM2KO7                0.0                 0.0         0.0       0.0   \n",
       "ID_ZZVUJ45W                0.0                 0.0         0.0      52.0   \n",
       "\n",
       "             Day_duration  FeatureX  FeatureY  User_sign-in_day  \n",
       "User_ID                                                          \n",
       "ID_000H9XOP           5.0       0.0       1.0              22.0  \n",
       "ID_003YJHQA           1.0       0.0       1.0              12.0  \n",
       "ID_004UKSJV           1.5      -0.5      -0.5              14.0  \n",
       "ID_008D2J6C           9.0       0.0       0.0               7.0  \n",
       "ID_00ETFE7J          10.0       0.0       0.0               6.0  \n",
       "...                   ...       ...       ...               ...  \n",
       "ID_ZZMHNLNT           1.0       0.0       0.0               1.0  \n",
       "ID_ZZQK0LU6           2.0       0.0       3.0              29.0  \n",
       "ID_ZZUNZJ2U           1.0       0.0       0.0              30.0  \n",
       "ID_ZZVM2KO7           0.0       0.0       0.0              22.0  \n",
       "ID_ZZVUJ45W           1.0       0.0       0.0               5.0  \n",
       "\n",
       "[12413 rows x 44 columns]"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.sort_values(\"User_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "af8312a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_max = temp.groupby('User_ID').max()\n",
    "df_sum = temp.groupby('User_ID').sum()\n",
    "df_mean = temp.groupby('User_ID').mean()\n",
    "df_min = temp.groupby('User_ID').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "f58c7d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "d54db73d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final[\"count_UA\"] = df_sum[\"count_UA\"]\n",
    "df_final[\"Day_last_active\"] = df_max[\"Day_last_active\"]\n",
    "df_final[\"User_sign-in_day\"] = df_min[\"User_sign-in_day\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "57e00fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Participant_Type</th>\n",
       "      <th>Successful_Submission_Count</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
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       "      <th>Day_duration</th>\n",
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       "      <th>User_sign-in_day</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>52.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12413 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Participant_Type  Successful_Submission_Count  \\\n",
       "User_ID                                                      \n",
       "ID_000H9XOP               0.0                          0.0   \n",
       "ID_003YJHQA               0.0                          0.0   \n",
       "ID_004UKSJV               0.0                          0.0   \n",
       "ID_008D2J6C               0.0                          0.0   \n",
       "ID_00ETFE7J               0.0                          0.0   \n",
       "...                       ...                          ...   \n",
       "ID_ZZMHNLNT               0.0                          0.0   \n",
       "ID_ZZQK0LU6               1.0                          0.0   \n",
       "ID_ZZUNZJ2U               0.0                          0.0   \n",
       "ID_ZZVM2KO7               0.0                          0.0   \n",
       "ID_ZZVUJ45W               1.0                          0.0   \n",
       "\n",
       "             Comp_participation_day  FeatureA  FeatureB  FeatureC  FeatureD  \\\n",
       "User_ID                                                                       \n",
       "ID_000H9XOP                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_003YJHQA                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_004UKSJV                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_008D2J6C                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_00ETFE7J                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "ID_ZZMHNLNT                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZQK0LU6                    29.0       1.5       0.0      13.5       0.0   \n",
       "ID_ZZUNZJ2U                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZVM2KO7                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "ID_ZZVUJ45W                     5.0       1.0       7.0       4.0       1.0   \n",
       "\n",
       "             FeatureE  SecretCode  FeatureF  ...  Updated_Profile  blog_ID  \\\n",
       "User_ID                                      ...                             \n",
       "ID_000H9XOP      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_003YJHQA      -1.0        -1.0      -1.0  ...              2.0      0.0   \n",
       "ID_004UKSJV      -1.0        -1.0      -1.0  ...              2.0      0.0   \n",
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       "ID_00ETFE7J      -1.0        -1.0      -1.0  ...              0.0      1.0   \n",
       "...               ...         ...       ...  ...              ...      ...   \n",
       "ID_ZZMHNLNT      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_ZZQK0LU6       2.0         1.0       3.0  ...              0.0      0.0   \n",
       "ID_ZZUNZJ2U      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_ZZVM2KO7      -1.0        -1.0      -1.0  ...              0.0      0.0   \n",
       "ID_ZZVUJ45W       1.0         0.0       1.0  ...              0.0      0.0   \n",
       "\n",
       "             Viewed_Discussion  Updated_Submission  badge_HYIO  count_UA  \\\n",
       "User_ID                                                                    \n",
       "ID_000H9XOP                0.0                 0.0         0.0       6.0   \n",
       "ID_003YJHQA                0.0                 0.0         0.0      11.0   \n",
       "ID_004UKSJV                0.0                 0.0         0.0       6.0   \n",
       "ID_008D2J6C                0.0                 0.0         0.0      10.0   \n",
       "ID_00ETFE7J                0.0                 0.0         0.0      10.0   \n",
       "...                        ...                 ...         ...       ...   \n",
       "ID_ZZMHNLNT                0.0                 0.0         0.0       8.0   \n",
       "ID_ZZQK0LU6                0.0                 0.0         0.0     134.0   \n",
       "ID_ZZUNZJ2U                0.0                 0.0         0.0      12.0   \n",
       "ID_ZZVM2KO7                0.0                 0.0         0.0       0.0   \n",
       "ID_ZZVUJ45W                0.0                 0.0         0.0      52.0   \n",
       "\n",
       "             Day_duration  FeatureX  FeatureY  User_sign-in_day  \n",
       "User_ID                                                          \n",
       "ID_000H9XOP           5.0       0.0       1.0              22.0  \n",
       "ID_003YJHQA           1.0       0.0       1.0              12.0  \n",
       "ID_004UKSJV           1.5      -0.5      -0.5              14.0  \n",
       "ID_008D2J6C           9.0       0.0       0.0               7.0  \n",
       "ID_00ETFE7J          10.0       0.0       0.0               6.0  \n",
       "...                   ...       ...       ...               ...  \n",
       "ID_ZZMHNLNT           1.0       0.0       0.0               1.0  \n",
       "ID_ZZQK0LU6           2.0       0.0       3.0              29.0  \n",
       "ID_ZZUNZJ2U           1.0       0.0       0.0              30.0  \n",
       "ID_ZZVM2KO7           0.0       0.0       0.0              22.0  \n",
       "ID_ZZVUJ45W           1.0       0.0       0.0               5.0  \n",
       "\n",
       "[12413 rows x 44 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "a3709d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "d0698bdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12413 entries, 0 to 12412\n",
      "Data columns (total 45 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   User_ID                          12413 non-null  object \n",
      " 1   Participant_Type                 12413 non-null  float64\n",
      " 2   Successful_Submission_Count      12413 non-null  float64\n",
      " 3   Comp_participation_day           12413 non-null  float64\n",
      " 4   FeatureA                         12413 non-null  float64\n",
      " 5   FeatureB                         12413 non-null  float64\n",
      " 6   FeatureC                         12413 non-null  float64\n",
      " 7   FeatureD                         12413 non-null  float64\n",
      " 8   FeatureE                         12413 non-null  float64\n",
      " 9   SecretCode                       12413 non-null  float64\n",
      " 10  FeatureF                         12413 non-null  float64\n",
      " 11  FeatureG                         12413 non-null  float64\n",
      " 12  FeatureH                         12413 non-null  float64\n",
      " 13  FeatureI                         12413 non-null  float64\n",
      " 14  Country_ID                       12413 non-null  float64\n",
      " 15  competition_ended_month          12413 non-null  float64\n",
      " 16  competition_started_month        12413 non-null  float64\n",
      " 17  Day_last_active                  12413 non-null  float64\n",
      " 18  Downloaded_Competition_Datafile  12413 non-null  float64\n",
      " 19  Viewed_All_Discussions           12413 non-null  float64\n",
      " 20  Confirmed_Email                  12413 non-null  float64\n",
      " 21  Joined_Competition               12413 non-null  float64\n",
      " 22  Signed_Up                        12413 non-null  float64\n",
      " 23  Viewed_All_Competitions          12413 non-null  float64\n",
      " 24  Viewed_All_Learning_Pages        12413 non-null  float64\n",
      " 25  badge_OCZE                       12413 non-null  float64\n",
      " 26  create_alias                     12413 non-null  float64\n",
      " 27  identify                         12413 non-null  float64\n",
      " 28  comp_ID                          12413 non-null  float64\n",
      " 29  Signed_In                        12413 non-null  float64\n",
      " 30  job_ID                           12413 non-null  float64\n",
      " 31  Updated_Discussion               12413 non-null  float64\n",
      " 32  Viewed_All_Jobs                  12413 non-null  float64\n",
      " 33  Created_Submission               12413 non-null  float64\n",
      " 34  Signed_Out                       12413 non-null  float64\n",
      " 35  Updated_Profile                  12413 non-null  float64\n",
      " 36  blog_ID                          12413 non-null  float64\n",
      " 37  Viewed_Discussion                12413 non-null  float64\n",
      " 38  Updated_Submission               12413 non-null  float64\n",
      " 39  badge_HYIO                       12413 non-null  float64\n",
      " 40  count_UA                         12413 non-null  float64\n",
      " 41  Day_duration                     12413 non-null  float64\n",
      " 42  FeatureX                         12413 non-null  float64\n",
      " 43  FeatureY                         12413 non-null  float64\n",
      " 44  User_sign-in_day                 12413 non-null  float64\n",
      "dtypes: float64(44), object(1)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "a754a1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>User_ID</th>\n",
       "      <th>Participant_Type</th>\n",
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       "    <tr>\n",
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       "<p>12413 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  Participant_Type  Successful_Submission_Count  \\\n",
       "0      ID_000H9XOP               0.0                          0.0   \n",
       "1      ID_003YJHQA               0.0                          0.0   \n",
       "2      ID_004UKSJV               0.0                          0.0   \n",
       "3      ID_008D2J6C               0.0                          0.0   \n",
       "4      ID_00ETFE7J               0.0                          0.0   \n",
       "...            ...               ...                          ...   \n",
       "12408  ID_ZZMHNLNT               0.0                          0.0   \n",
       "12409  ID_ZZQK0LU6               1.0                          0.0   \n",
       "12410  ID_ZZUNZJ2U               0.0                          0.0   \n",
       "12411  ID_ZZVM2KO7               0.0                          0.0   \n",
       "12412  ID_ZZVUJ45W               1.0                          0.0   \n",
       "\n",
       "       Comp_participation_day  FeatureA  FeatureB  FeatureC  FeatureD  \\\n",
       "0                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "1                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "2                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "3                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "4                         0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "...                       ...       ...       ...       ...       ...   \n",
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       "12409                    29.0       1.5       0.0      13.5       0.0   \n",
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       "12411                     0.0      -1.0      -1.0      -1.0      -1.0   \n",
       "12412                     5.0       1.0       7.0       4.0       1.0   \n",
       "\n",
       "       FeatureE  SecretCode  ...  Updated_Profile  blog_ID  Viewed_Discussion  \\\n",
       "0          -1.0        -1.0  ...              0.0      0.0                0.0   \n",
       "1          -1.0        -1.0  ...              2.0      0.0                0.0   \n",
       "2          -1.0        -1.0  ...              2.0      0.0                0.0   \n",
       "3          -1.0        -1.0  ...              2.0      0.0                0.0   \n",
       "4          -1.0        -1.0  ...              0.0      1.0                0.0   \n",
       "...         ...         ...  ...              ...      ...                ...   \n",
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       "12409       2.0         1.0  ...              0.0      0.0                0.0   \n",
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       "12412       1.0         0.0  ...              0.0      0.0                0.0   \n",
       "\n",
       "       Updated_Submission  badge_HYIO  count_UA  Day_duration  FeatureX  \\\n",
       "0                     0.0         0.0       6.0           5.0       0.0   \n",
       "1                     0.0         0.0      11.0           1.0       0.0   \n",
       "2                     0.0         0.0       6.0           1.5      -0.5   \n",
       "3                     0.0         0.0      10.0           9.0       0.0   \n",
       "4                     0.0         0.0      10.0          10.0       0.0   \n",
       "...                   ...         ...       ...           ...       ...   \n",
       "12408                 0.0         0.0       8.0           1.0       0.0   \n",
       "12409                 0.0         0.0     134.0           2.0       0.0   \n",
       "12410                 0.0         0.0      12.0           1.0       0.0   \n",
       "12411                 0.0         0.0       0.0           0.0       0.0   \n",
       "12412                 0.0         0.0      52.0           1.0       0.0   \n",
       "\n",
       "       FeatureY  User_sign-in_day  \n",
       "0           1.0              22.0  \n",
       "1           1.0              12.0  \n",
       "2          -0.5              14.0  \n",
       "3           0.0               7.0  \n",
       "4           0.0               6.0  \n",
       "...         ...               ...  \n",
       "12408       0.0               1.0  \n",
       "12409       3.0              29.0  \n",
       "12410       0.0              30.0  \n",
       "12411       0.0              22.0  \n",
       "12412       0.0               5.0  \n",
       "\n",
       "[12413 rows x 45 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "cf20f44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('Dataset1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c53e445c",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "70bcefaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = df_final.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "ad38357e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.drop(\"User_ID\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "d11695d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df_temp.columns:\n",
    "    df_temp[column] = df_temp[column]  / df_temp[column].abs().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "2aef3570",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       Personal  Theme  Discussion_day  comment_day  Participant_Type  \\\n",
       "0          -1.0    0.0             0.0          0.0               0.0   \n",
       "1          -1.0    0.0             0.0          0.0               0.0   \n",
       "2          -1.0    0.0             0.0          0.0               0.0   \n",
       "3          -1.0    0.0             0.0          0.0               0.0   \n",
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       "...         ...    ...             ...          ...               ...   \n",
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       "12409      -1.0    0.0             0.0          0.0               1.0   \n",
       "12410      -1.0    0.0             0.0          0.0               0.0   \n",
       "12411      -1.0    0.0             0.0          0.0               0.0   \n",
       "12412      -1.0    0.0             0.0          0.0               1.0   \n",
       "\n",
       "       Successful_Submission_Count  Comp_participation_day  FeatureA  \\\n",
       "0                              0.0                0.000000   -0.1250   \n",
       "1                              0.0                0.000000   -0.1250   \n",
       "2                              0.0                0.000000   -0.1250   \n",
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       "12411                          0.0                0.000000   -0.1250   \n",
       "12412                          0.0                0.161290    0.1250   \n",
       "\n",
       "       FeatureB  FeatureC  ...  Function_DE  Function_DM  Function_Bus  \\\n",
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       "...         ...       ...  ...          ...          ...           ...   \n",
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       "12412    0.4375  0.114286  ...          0.0          0.0           0.0   \n",
       "\n",
       "       Function_AI  Function_ML  count_UA  Day_duration  FeatureX  FeatureY  \\\n",
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       "...            ...          ...       ...           ...       ...       ...   \n",
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       "12412          0.0          0.0  0.004516      0.032258       0.0  0.000000   \n",
       "\n",
       "       User_sign-in_day  \n",
       "0              0.709677  \n",
       "1              0.387097  \n",
       "2              0.451613  \n",
       "3              0.225806  \n",
       "4              0.193548  \n",
       "...                 ...  \n",
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       "\n",
       "[12413 rows x 62 columns]"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "79ed0d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.insert(0, 'User_ID', np.array(df_final[\"User_ID\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "0f14f09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>User_sign-in_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_000H9XOP</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000521</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.709677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_003YJHQA</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000955</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_004UKSJV</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000521</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_008D2J6C</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000869</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_00ETFE7J</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000869</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.193548</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>12408</th>\n",
       "      <td>ID_ZZMHNLNT</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000695</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12409</th>\n",
       "      <td>ID_ZZQK0LU6</td>\n",
       "      <td>-1.0</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>0.935484</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.0000</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>0.011638</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>ID_ZZUNZJ2U</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.001042</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.967742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12411</th>\n",
       "      <td>ID_ZZVM2KO7</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.709677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12412</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>-1.0</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>0.161290</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.4375</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>0.004516</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12413 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  Personal  Theme  Discussion_day  comment_day  \\\n",
       "0      ID_000H9XOP      -1.0    0.0             0.0          0.0   \n",
       "1      ID_003YJHQA      -1.0    0.0             0.0          0.0   \n",
       "2      ID_004UKSJV      -1.0    0.0             0.0          0.0   \n",
       "3      ID_008D2J6C      -1.0    0.0             0.0          0.0   \n",
       "4      ID_00ETFE7J      -1.0    0.0             0.0          0.0   \n",
       "...            ...       ...    ...             ...          ...   \n",
       "12408  ID_ZZMHNLNT      -1.0    0.0             0.0          0.0   \n",
       "12409  ID_ZZQK0LU6      -1.0    0.0             0.0          0.0   \n",
       "12410  ID_ZZUNZJ2U      -1.0    0.0             0.0          0.0   \n",
       "12411  ID_ZZVM2KO7      -1.0    0.0             0.0          0.0   \n",
       "12412  ID_ZZVUJ45W      -1.0    0.0             0.0          0.0   \n",
       "\n",
       "       Participant_Type  Successful_Submission_Count  Comp_participation_day  \\\n",
       "0                   0.0                          0.0                0.000000   \n",
       "1                   0.0                          0.0                0.000000   \n",
       "2                   0.0                          0.0                0.000000   \n",
       "3                   0.0                          0.0                0.000000   \n",
       "4                   0.0                          0.0                0.000000   \n",
       "...                 ...                          ...                     ...   \n",
       "12408               0.0                          0.0                0.000000   \n",
       "12409               1.0                          0.0                0.935484   \n",
       "12410               0.0                          0.0                0.000000   \n",
       "12411               0.0                          0.0                0.000000   \n",
       "12412               1.0                          0.0                0.161290   \n",
       "\n",
       "       FeatureA  FeatureB  ...  Function_DE  Function_DM  Function_Bus  \\\n",
       "0       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "1       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "2       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "3       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "4       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "...         ...       ...  ...          ...          ...           ...   \n",
       "12408   -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "12409    0.1875    0.0000  ...          0.0          0.0           0.0   \n",
       "12410   -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "12411   -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "12412    0.1250    0.4375  ...          0.0          0.0           0.0   \n",
       "\n",
       "       Function_AI  Function_ML  count_UA  Day_duration  FeatureX  FeatureY  \\\n",
       "0              0.0          0.0  0.000521      0.161290       0.0  0.333333   \n",
       "1              0.0          0.0  0.000955      0.032258       0.0  0.333333   \n",
       "2              0.0          0.0  0.000521      0.048387      -0.5 -0.166667   \n",
       "3              0.0          0.0  0.000869      0.290323       0.0  0.000000   \n",
       "4              0.0          0.0  0.000869      0.322581       0.0  0.000000   \n",
       "...            ...          ...       ...           ...       ...       ...   \n",
       "12408          0.0          0.0  0.000695      0.032258       0.0  0.000000   \n",
       "12409          0.0          0.0  0.011638      0.064516       0.0  1.000000   \n",
       "12410          0.0          0.0  0.001042      0.032258       0.0  0.000000   \n",
       "12411          0.0          0.0  0.000000      0.000000       0.0  0.000000   \n",
       "12412          0.0          0.0  0.004516      0.032258       0.0  0.000000   \n",
       "\n",
       "       User_sign-in_day  \n",
       "0              0.709677  \n",
       "1              0.387097  \n",
       "2              0.451613  \n",
       "3              0.225806  \n",
       "4              0.193548  \n",
       "...                 ...  \n",
       "12408          0.032258  \n",
       "12409          0.935484  \n",
       "12410          0.967742  \n",
       "12411          0.709677  \n",
       "12412          0.161290  \n",
       "\n",
       "[12413 rows x 63 columns]"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "2294dd07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "f9c65771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12413 entries, 0 to 12412\n",
      "Data columns (total 63 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   User_ID                      12413 non-null  object \n",
      " 1   Personal                     12413 non-null  float64\n",
      " 2   Theme                        12413 non-null  float64\n",
      " 3   Discussion_day               12413 non-null  float64\n",
      " 4   comment_day                  12413 non-null  float64\n",
      " 5   Participant_Type             12413 non-null  float64\n",
      " 6   Successful_Submission_Count  12413 non-null  float64\n",
      " 7   Comp_participation_day       12413 non-null  float64\n",
      " 8   FeatureA                     12413 non-null  float64\n",
      " 9   FeatureB                     12413 non-null  float64\n",
      " 10  FeatureC                     12413 non-null  float64\n",
      " 11  FeatureD                     12413 non-null  float64\n",
      " 12  FeatureE                     12413 non-null  float64\n",
      " 13  SecretCode                   12413 non-null  float64\n",
      " 14  FeatureF                     12413 non-null  float64\n",
      " 15  FeatureG                     12413 non-null  float64\n",
      " 16  FeatureH                     12413 non-null  float64\n",
      " 17  FeatureI                     12413 non-null  float64\n",
      " 18  Country_ID                   12413 non-null  float64\n",
      " 19  competition_ended_year       12413 non-null  float64\n",
      " 20  competition_ended_month      12413 non-null  float64\n",
      " 21  competition_ended_day        12413 non-null  float64\n",
      " 22  competition_started_year     12413 non-null  float64\n",
      " 23  competition_started_month    12413 non-null  float64\n",
      " 24  competition_started_day      12413 non-null  float64\n",
      " 25  Activity_Type                12413 non-null  float64\n",
      " 26  Day_last_active              12413 non-null  float64\n",
      " 27  Blog_Theme                   12413 non-null  float64\n",
      " 28  Blog_Published_Year          12413 non-null  float64\n",
      " 29  Blog_Published_Month         12413 non-null  float64\n",
      " 30  Published_At_Day_of_month    12413 non-null  float64\n",
      " 31  Remote                       12413 non-null  float64\n",
      " 32  Experience                   12413 non-null  float64\n",
      " 33  Employment_Type              12413 non-null  float64\n",
      " 34  Company_Size                 12413 non-null  float64\n",
      " 35  Industry_FS                  12413 non-null  float64\n",
      " 36  Industry_Bank                12413 non-null  float64\n",
      " 37  Industry_Loc                 12413 non-null  float64\n",
      " 38  Industry_Gov                 12413 non-null  float64\n",
      " 39  Industry_Health              12413 non-null  float64\n",
      " 40  Industry_Res                 12413 non-null  float64\n",
      " 41  Industry_Amb                 12413 non-null  float64\n",
      " 42  Industry_Transp              12413 non-null  float64\n",
      " 43  Industry_Scholar             12413 non-null  float64\n",
      " 44  Industry_Log                 12413 non-null  float64\n",
      " 45  Industry_Media               12413 non-null  float64\n",
      " 46  Industry_Manuf               12413 non-null  float64\n",
      " 47  Industry_Sales               12413 non-null  float64\n",
      " 48  Industry_Insur               12413 non-null  float64\n",
      " 49  Industry_Manage              12413 non-null  float64\n",
      " 50  Industry_AI                  12413 non-null  float64\n",
      " 51  Industry_Zindi               12413 non-null  float64\n",
      " 52  Industry_Tech                12413 non-null  float64\n",
      " 53  Function_DE                  12413 non-null  float64\n",
      " 54  Function_DM                  12413 non-null  float64\n",
      " 55  Function_Bus                 12413 non-null  float64\n",
      " 56  Function_AI                  12413 non-null  float64\n",
      " 57  Function_ML                  12413 non-null  float64\n",
      " 58  count_UA                     12413 non-null  float64\n",
      " 59  Day_duration                 12413 non-null  float64\n",
      " 60  FeatureX                     12413 non-null  float64\n",
      " 61  FeatureY                     12413 non-null  float64\n",
      " 62  User_sign-in_day             12413 non-null  float64\n",
      "dtypes: float64(62), object(1)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "7adef9b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Personal</th>\n",
       "      <th>Theme</th>\n",
       "      <th>Discussion_day</th>\n",
       "      <th>comment_day</th>\n",
       "      <th>Participant_Type</th>\n",
       "      <th>Successful_Submission_Count</th>\n",
       "      <th>Comp_participation_day</th>\n",
       "      <th>FeatureA</th>\n",
       "      <th>FeatureB</th>\n",
       "      <th>...</th>\n",
       "      <th>Function_DE</th>\n",
       "      <th>Function_DM</th>\n",
       "      <th>Function_Bus</th>\n",
       "      <th>Function_AI</th>\n",
       "      <th>Function_ML</th>\n",
       "      <th>count_UA</th>\n",
       "      <th>Day_duration</th>\n",
       "      <th>FeatureX</th>\n",
       "      <th>FeatureY</th>\n",
       "      <th>User_sign-in_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_000H9XOP</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000521</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.709677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_003YJHQA</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000955</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_004UKSJV</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000521</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_008D2J6C</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000869</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_00ETFE7J</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000869</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.193548</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>12408</th>\n",
       "      <td>ID_ZZMHNLNT</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000695</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12409</th>\n",
       "      <td>ID_ZZQK0LU6</td>\n",
       "      <td>-1.0</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>0.935484</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.0000</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>0.011638</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>ID_ZZUNZJ2U</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.001042</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.967742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12411</th>\n",
       "      <td>ID_ZZVM2KO7</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>0.000000</td>\n",
       "      <td>-0.1250</td>\n",
       "      <td>-0.0625</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.709677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12412</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>-1.0</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>0.161290</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.4375</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>0.004516</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12413 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  Personal  Theme  Discussion_day  comment_day  \\\n",
       "0      ID_000H9XOP      -1.0    0.0             0.0          0.0   \n",
       "1      ID_003YJHQA      -1.0    0.0             0.0          0.0   \n",
       "2      ID_004UKSJV      -1.0    0.0             0.0          0.0   \n",
       "3      ID_008D2J6C      -1.0    0.0             0.0          0.0   \n",
       "4      ID_00ETFE7J      -1.0    0.0             0.0          0.0   \n",
       "...            ...       ...    ...             ...          ...   \n",
       "12408  ID_ZZMHNLNT      -1.0    0.0             0.0          0.0   \n",
       "12409  ID_ZZQK0LU6      -1.0    0.0             0.0          0.0   \n",
       "12410  ID_ZZUNZJ2U      -1.0    0.0             0.0          0.0   \n",
       "12411  ID_ZZVM2KO7      -1.0    0.0             0.0          0.0   \n",
       "12412  ID_ZZVUJ45W      -1.0    0.0             0.0          0.0   \n",
       "\n",
       "       Participant_Type  Successful_Submission_Count  Comp_participation_day  \\\n",
       "0                   0.0                          0.0                0.000000   \n",
       "1                   0.0                          0.0                0.000000   \n",
       "2                   0.0                          0.0                0.000000   \n",
       "3                   0.0                          0.0                0.000000   \n",
       "4                   0.0                          0.0                0.000000   \n",
       "...                 ...                          ...                     ...   \n",
       "12408               0.0                          0.0                0.000000   \n",
       "12409               1.0                          0.0                0.935484   \n",
       "12410               0.0                          0.0                0.000000   \n",
       "12411               0.0                          0.0                0.000000   \n",
       "12412               1.0                          0.0                0.161290   \n",
       "\n",
       "       FeatureA  FeatureB  ...  Function_DE  Function_DM  Function_Bus  \\\n",
       "0       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "1       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "2       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "3       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "4       -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "...         ...       ...  ...          ...          ...           ...   \n",
       "12408   -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "12409    0.1875    0.0000  ...          0.0          0.0           0.0   \n",
       "12410   -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "12411   -0.1250   -0.0625  ...          0.0          0.0           0.0   \n",
       "12412    0.1250    0.4375  ...          0.0          0.0           0.0   \n",
       "\n",
       "       Function_AI  Function_ML  count_UA  Day_duration  FeatureX  FeatureY  \\\n",
       "0              0.0          0.0  0.000521      0.161290       0.0  0.333333   \n",
       "1              0.0          0.0  0.000955      0.032258       0.0  0.333333   \n",
       "2              0.0          0.0  0.000521      0.048387      -0.5 -0.166667   \n",
       "3              0.0          0.0  0.000869      0.290323       0.0  0.000000   \n",
       "4              0.0          0.0  0.000869      0.322581       0.0  0.000000   \n",
       "...            ...          ...       ...           ...       ...       ...   \n",
       "12408          0.0          0.0  0.000695      0.032258       0.0  0.000000   \n",
       "12409          0.0          0.0  0.011638      0.064516       0.0  1.000000   \n",
       "12410          0.0          0.0  0.001042      0.032258       0.0  0.000000   \n",
       "12411          0.0          0.0  0.000000      0.000000       0.0  0.000000   \n",
       "12412          0.0          0.0  0.004516      0.032258       0.0  0.000000   \n",
       "\n",
       "       User_sign-in_day  \n",
       "0              0.709677  \n",
       "1              0.387097  \n",
       "2              0.451613  \n",
       "3              0.225806  \n",
       "4              0.193548  \n",
       "...                 ...  \n",
       "12408          0.032258  \n",
       "12409          0.935484  \n",
       "12410          0.967742  \n",
       "12411          0.709677  \n",
       "12412          0.161290  \n",
       "\n",
       "[12413 rows x 63 columns]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "1bb3155b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('DatasetNorm.csv')"
   ]
  }
 ],
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