CriteriaKMeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19dbc488",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import operator as op\n",
    "import warnings\n",
    "warnings.filterwarnings(action = 'ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfef073b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Datasetf.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3bb13545",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Unnamed: 0\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed42014b",
   "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>Personal</th>\n",
       "      <th>Theme</th>\n",
       "      <th>Discussion_day</th>\n",
       "      <th>Discussion_Hour</th>\n",
       "      <th>Discussion_Minute</th>\n",
       "      <th>Discussion_Second</th>\n",
       "      <th>comment_day</th>\n",
       "      <th>Comment_Hour</th>\n",
       "      <th>Comment_Minute</th>\n",
       "      <th>...</th>\n",
       "      <th>competition_participation_second</th>\n",
       "      <th>competition_ended_hour</th>\n",
       "      <th>competition_ended_minute</th>\n",
       "      <th>competition_ended_second</th>\n",
       "      <th>competition_started_hour</th>\n",
       "      <th>competition_started_minute</th>\n",
       "      <th>competition_started_second</th>\n",
       "      <th>User_sign-in_hour</th>\n",
       "      <th>User_sign-in_minute</th>\n",
       "      <th>User_sign-in_second</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.190673</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.508475</td>\n",
       "      <td>0.537754</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.262712</td>\n",
       "      <td>0.116658</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.661658</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.269932</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.932203</td>\n",
       "      <td>0.190620</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.518322</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.814532</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.302296</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.343564</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.882392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.406780</td>\n",
       "      <td>0.562270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12413 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  Personal  Theme  Discussion_day  Discussion_Hour  \\\n",
       "0      ID_000H9XOP      -1.0    0.0             0.0        -0.043478   \n",
       "1      ID_003YJHQA      -1.0    0.0             0.0        -0.043478   \n",
       "2      ID_004UKSJV      -1.0    0.0             0.0        -0.043478   \n",
       "3      ID_008D2J6C      -1.0    0.0             0.0        -0.043478   \n",
       "4      ID_00ETFE7J      -1.0    0.0             0.0        -0.043478   \n",
       "...            ...       ...    ...             ...              ...   \n",
       "12408  ID_ZZMHNLNT      -1.0    0.0             0.0        -0.043478   \n",
       "12409  ID_ZZQK0LU6      -1.0    0.0             0.0        -0.043478   \n",
       "12410  ID_ZZUNZJ2U      -1.0    0.0             0.0        -0.043478   \n",
       "12411  ID_ZZVM2KO7      -1.0    0.0             0.0        -0.043478   \n",
       "12412  ID_ZZVUJ45W      -1.0    0.0             0.0        -0.043478   \n",
       "\n",
       "       Discussion_Minute  Discussion_Second  comment_day  Comment_Hour  \\\n",
       "0              -0.016949          -0.016668          0.0         -0.05   \n",
       "1              -0.016949          -0.016668          0.0         -0.05   \n",
       "2              -0.016949          -0.016668          0.0         -0.05   \n",
       "3              -0.016949          -0.016668          0.0         -0.05   \n",
       "4              -0.016949          -0.016668          0.0         -0.05   \n",
       "...                  ...                ...          ...           ...   \n",
       "12408          -0.016949          -0.016668          0.0         -0.05   \n",
       "12409          -0.016949          -0.016668          0.0         -0.05   \n",
       "12410          -0.016949          -0.016668          0.0         -0.05   \n",
       "12411          -0.016949          -0.016668          0.0         -0.05   \n",
       "12412          -0.016949          -0.016668          0.0         -0.05   \n",
       "\n",
       "       Comment_Minute  ...  competition_participation_second  \\\n",
       "0           -0.019231  ...                         -0.016668   \n",
       "1           -0.019231  ...                         -0.016668   \n",
       "2           -0.019231  ...                         -0.016668   \n",
       "3           -0.019231  ...                         -0.016668   \n",
       "4           -0.019231  ...                         -0.016668   \n",
       "...               ...  ...                               ...   \n",
       "12408       -0.019231  ...                         -0.016668   \n",
       "12409       -0.019231  ...                          0.518322   \n",
       "12410       -0.019231  ...                         -0.016668   \n",
       "12411       -0.019231  ...                         -0.016668   \n",
       "12412       -0.019231  ...                          0.882392   \n",
       "\n",
       "       competition_ended_hour  competition_ended_minute  \\\n",
       "0                   -0.043478                 -0.016949   \n",
       "1                   -0.043478                 -0.016949   \n",
       "2                   -0.043478                 -0.016949   \n",
       "3                   -0.043478                 -0.016949   \n",
       "4                   -0.043478                 -0.016949   \n",
       "...                       ...                       ...   \n",
       "12408               -0.043478                 -0.016949   \n",
       "12409                0.217391                  1.000000   \n",
       "12410               -0.043478                 -0.016949   \n",
       "12411               -0.043478                 -0.016949   \n",
       "12412                1.000000                  1.000000   \n",
       "\n",
       "       competition_ended_second  competition_started_hour  \\\n",
       "0                          -1.0                 -0.043478   \n",
       "1                          -1.0                 -0.043478   \n",
       "2                          -1.0                 -0.043478   \n",
       "3                          -1.0                 -0.043478   \n",
       "4                          -1.0                 -0.043478   \n",
       "...                         ...                       ...   \n",
       "12408                      -1.0                 -0.043478   \n",
       "12409                       0.0                  0.391304   \n",
       "12410                      -1.0                 -0.043478   \n",
       "12411                      -1.0                 -0.043478   \n",
       "12412                       0.0                  0.260870   \n",
       "\n",
       "       competition_started_minute  competition_started_second  \\\n",
       "0                       -0.016949                        -1.0   \n",
       "1                       -0.016949                        -1.0   \n",
       "2                       -0.016949                        -1.0   \n",
       "3                       -0.016949                        -1.0   \n",
       "4                       -0.016949                        -1.0   \n",
       "...                           ...                         ...   \n",
       "12408                   -0.016949                        -1.0   \n",
       "12409                    0.000000                         0.0   \n",
       "12410                   -0.016949                        -1.0   \n",
       "12411                   -0.016949                        -1.0   \n",
       "12412                    0.000000                         0.0   \n",
       "\n",
       "       User_sign-in_hour  User_sign-in_minute  User_sign-in_second  \n",
       "0               0.652174             0.305085             0.190673  \n",
       "1               0.434783             0.508475             0.537754  \n",
       "2               0.152174             0.262712             0.116658  \n",
       "3               0.391304             0.711864             0.661658  \n",
       "4               0.391304             0.322034             0.269932  \n",
       "...                  ...                  ...                  ...  \n",
       "12408           0.434783             0.932203             0.190620  \n",
       "12409           0.391304             0.813559             0.814532  \n",
       "12410           0.956522             0.254237             0.302296  \n",
       "12411           0.739130             0.186441             0.343564  \n",
       "12412           0.347826             0.406780             0.562270  \n",
       "\n",
       "[12413 rows x 87 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6d263f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12413 entries, 0 to 12412\n",
      "Data columns (total 87 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   Discussion_Hour                       12413 non-null  float64\n",
      " 5   Discussion_Minute                     12413 non-null  float64\n",
      " 6   Discussion_Second                     12413 non-null  float64\n",
      " 7   comment_day                           12413 non-null  float64\n",
      " 8   Comment_Hour                          12413 non-null  float64\n",
      " 9   Comment_Minute                        12413 non-null  float64\n",
      " 10  Comment_Second                        12413 non-null  float64\n",
      " 11  Participant_Type                      12413 non-null  float64\n",
      " 12  Successful_Submission_Count           12413 non-null  float64\n",
      " 13  Comp_participation_day                12413 non-null  float64\n",
      " 14  FeatureA                              12413 non-null  float64\n",
      " 15  FeatureB                              12413 non-null  float64\n",
      " 16  FeatureC                              12413 non-null  float64\n",
      " 17  FeatureD                              12413 non-null  float64\n",
      " 18  FeatureE                              12413 non-null  float64\n",
      " 19  SecretCode                            12413 non-null  float64\n",
      " 20  FeatureF                              12413 non-null  float64\n",
      " 21  FeatureG                              12413 non-null  float64\n",
      " 22  FeatureH                              12413 non-null  float64\n",
      " 23  FeatureI                              12413 non-null  float64\n",
      " 24  Country_ID                            12413 non-null  float64\n",
      " 25  competition_ended_year                12413 non-null  float64\n",
      " 26  competition_ended_month               12413 non-null  float64\n",
      " 27  competition_ended_day                 12413 non-null  float64\n",
      " 28  competition_started_year              12413 non-null  float64\n",
      " 29  competition_started_month             12413 non-null  float64\n",
      " 30  competition_started_day               12413 non-null  float64\n",
      " 31  Activity_Type                         12413 non-null  float64\n",
      " 32  Day_last_active                       12413 non-null  float64\n",
      " 33  Time_user_completed_action_in_hour    12413 non-null  float64\n",
      " 34  Time_user_completed_action_in_minute  12413 non-null  float64\n",
      " 35  Time_user_completed_action_in_second  12413 non-null  float64\n",
      " 36  Blog_Theme                            12413 non-null  float64\n",
      " 37  Blog_Published_Year                   12413 non-null  float64\n",
      " 38  Blog_Published_Month                  12413 non-null  float64\n",
      " 39  Published_At_Day_of_month             12413 non-null  float64\n",
      " 40  Blog_Published_Hour                   12413 non-null  float64\n",
      " 41  Blog_Published_Minute                 12413 non-null  float64\n",
      " 42  Blog_Published_Second                 12413 non-null  float64\n",
      " 43  Remote                                12413 non-null  float64\n",
      " 44  Experience                            12413 non-null  float64\n",
      " 45  Employment_Type                       12413 non-null  float64\n",
      " 46  Company_Size                          12413 non-null  float64\n",
      " 47  Industry_FS                           12413 non-null  float64\n",
      " 48  Industry_Bank                         12413 non-null  float64\n",
      " 49  Industry_Loc                          12413 non-null  float64\n",
      " 50  Industry_Gov                          12413 non-null  float64\n",
      " 51  Industry_Health                       12413 non-null  float64\n",
      " 52  Industry_Res                          12413 non-null  float64\n",
      " 53  Industry_Amb                          12413 non-null  float64\n",
      " 54  Industry_Transp                       12413 non-null  float64\n",
      " 55  Industry_Scholar                      12413 non-null  float64\n",
      " 56  Industry_Log                          12413 non-null  float64\n",
      " 57  Industry_Media                        12413 non-null  float64\n",
      " 58  Industry_Manuf                        12413 non-null  float64\n",
      " 59  Industry_Sales                        12413 non-null  float64\n",
      " 60  Industry_Insur                        12413 non-null  float64\n",
      " 61  Industry_Manage                       12413 non-null  float64\n",
      " 62  Industry_AI                           12413 non-null  float64\n",
      " 63  Industry_Zindi                        12413 non-null  float64\n",
      " 64  Industry_Tech                         12413 non-null  float64\n",
      " 65  Function_DE                           12413 non-null  float64\n",
      " 66  Function_DM                           12413 non-null  float64\n",
      " 67  Function_Bus                          12413 non-null  float64\n",
      " 68  Function_AI                           12413 non-null  float64\n",
      " 69  Function_ML                           12413 non-null  float64\n",
      " 70  count_UA                              12413 non-null  float64\n",
      " 71  Day_duration                          12413 non-null  float64\n",
      " 72  FeatureX                              12413 non-null  float64\n",
      " 73  FeatureY                              12413 non-null  float64\n",
      " 74  User_sign-in_day                      12413 non-null  float64\n",
      " 75  competition_participation_hour        12413 non-null  float64\n",
      " 76  competition_participation_minute      12413 non-null  float64\n",
      " 77  competition_participation_second      12413 non-null  float64\n",
      " 78  competition_ended_hour                12413 non-null  float64\n",
      " 79  competition_ended_minute              12413 non-null  float64\n",
      " 80  competition_ended_second              12413 non-null  float64\n",
      " 81  competition_started_hour              12413 non-null  float64\n",
      " 82  competition_started_minute            12413 non-null  float64\n",
      " 83  competition_started_second            12413 non-null  float64\n",
      " 84  User_sign-in_hour                     12413 non-null  float64\n",
      " 85  User_sign-in_minute                   12413 non-null  float64\n",
      " 86  User_sign-in_second                   12413 non-null  float64\n",
      "dtypes: float64(86), object(1)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "705c76ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample = pd.read_csv(\"SampleSubmission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "505a441a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID_Next_month_Activity</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4TOXNBGB_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_CHFTIP26_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_FU5GMWLQ_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_254TVBQP_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_4ENO2VFE_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>ID_PI8N2KJX_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>ID_L9AHTUD7_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>ID_BB7GLV87_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>ID_WB6O1NFS_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>ID_99LD9C1B_Month_5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1340 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     User_ID_Next_month_Activity  Active\n",
       "0            ID_4TOXNBGB_Month_5       0\n",
       "1            ID_CHFTIP26_Month_5       0\n",
       "2            ID_FU5GMWLQ_Month_5       0\n",
       "3            ID_254TVBQP_Month_5       0\n",
       "4            ID_4ENO2VFE_Month_5       0\n",
       "...                          ...     ...\n",
       "1335         ID_PI8N2KJX_Month_5       0\n",
       "1336         ID_L9AHTUD7_Month_5       0\n",
       "1337         ID_BB7GLV87_Month_5       0\n",
       "1338         ID_WB6O1NFS_Month_5       0\n",
       "1339         ID_99LD9C1B_Month_5       0\n",
       "\n",
       "[1340 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae622989",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample[\"User_ID_Next_month_Activity\"] = df_sample.User_ID_Next_month_Activity.str.replace('_Month_5', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0caf0f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID_Next_month_Activity</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4TOXNBGB</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_CHFTIP26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_FU5GMWLQ</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_254TVBQP</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_4ENO2VFE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>ID_PI8N2KJX</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>ID_L9AHTUD7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>ID_BB7GLV87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>ID_WB6O1NFS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>ID_99LD9C1B</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1340 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     User_ID_Next_month_Activity  Active\n",
       "0                    ID_4TOXNBGB       0\n",
       "1                    ID_CHFTIP26       0\n",
       "2                    ID_FU5GMWLQ       0\n",
       "3                    ID_254TVBQP       0\n",
       "4                    ID_4ENO2VFE       0\n",
       "...                          ...     ...\n",
       "1335                 ID_PI8N2KJX       0\n",
       "1336                 ID_L9AHTUD7       0\n",
       "1337                 ID_BB7GLV87       0\n",
       "1338                 ID_WB6O1NFS       0\n",
       "1339                 ID_99LD9C1B       0\n",
       "\n",
       "[1340 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf7bc46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1340"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_sample[\"User_ID_Next_month_Activity\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42979d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample.rename(columns={'User_ID_Next_month_Activity': 'User_ID'},  inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8836f8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.merge(df_sample, df, on='User_ID',  how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "65924340",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.drop(\"Active\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aac0624a",
   "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>Personal</th>\n",
       "      <th>Theme</th>\n",
       "      <th>Discussion_day</th>\n",
       "      <th>Discussion_Hour</th>\n",
       "      <th>Discussion_Minute</th>\n",
       "      <th>Discussion_Second</th>\n",
       "      <th>comment_day</th>\n",
       "      <th>Comment_Hour</th>\n",
       "      <th>Comment_Minute</th>\n",
       "      <th>...</th>\n",
       "      <th>competition_participation_second</th>\n",
       "      <th>competition_ended_hour</th>\n",
       "      <th>competition_ended_minute</th>\n",
       "      <th>competition_ended_second</th>\n",
       "      <th>competition_started_hour</th>\n",
       "      <th>competition_started_minute</th>\n",
       "      <th>competition_started_second</th>\n",
       "      <th>User_sign-in_hour</th>\n",
       "      <th>User_sign-in_minute</th>\n",
       "      <th>User_sign-in_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4TOXNBGB</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.847458</td>\n",
       "      <td>0.701464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_CHFTIP26</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.954099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_FU5GMWLQ</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.836641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_254TVBQP</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.203505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.951465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_4ENO2VFE</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.728814</td>\n",
       "      <td>0.694182</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>1335</th>\n",
       "      <td>ID_PI8N2KJX</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.593220</td>\n",
       "      <td>0.349386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>ID_L9AHTUD7</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>0.071477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>ID_BB7GLV87</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>0.940898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>ID_WB6O1NFS</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.559322</td>\n",
       "      <td>0.390210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>ID_99LD9C1B</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.474576</td>\n",
       "      <td>0.144221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1340 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          User_ID  Personal  Theme  Discussion_day  Discussion_Hour  \\\n",
       "0     ID_4TOXNBGB      -1.0    0.0             0.0        -0.043478   \n",
       "1     ID_CHFTIP26      -1.0    0.0             0.0        -0.043478   \n",
       "2     ID_FU5GMWLQ      -1.0    0.0             0.0        -0.043478   \n",
       "3     ID_254TVBQP      -1.0    0.0             0.0        -0.043478   \n",
       "4     ID_4ENO2VFE      -1.0    0.0             0.0        -0.043478   \n",
       "...           ...       ...    ...             ...              ...   \n",
       "1335  ID_PI8N2KJX      -1.0    0.0             0.0        -0.043478   \n",
       "1336  ID_L9AHTUD7      -1.0    0.0             0.0        -0.043478   \n",
       "1337  ID_BB7GLV87      -1.0    0.0             0.0        -0.043478   \n",
       "1338  ID_WB6O1NFS      -1.0    0.0             0.0        -0.043478   \n",
       "1339  ID_99LD9C1B      -1.0    0.0             0.0        -0.043478   \n",
       "\n",
       "      Discussion_Minute  Discussion_Second  comment_day  Comment_Hour  \\\n",
       "0             -0.016949          -0.016668          0.0         -0.05   \n",
       "1             -0.016949          -0.016668          0.0         -0.05   \n",
       "2             -0.016949          -0.016668          0.0         -0.05   \n",
       "3             -0.016949          -0.016668          0.0         -0.05   \n",
       "4             -0.016949          -0.016668          0.0         -0.05   \n",
       "...                 ...                ...          ...           ...   \n",
       "1335          -0.016949          -0.016668          0.0         -0.05   \n",
       "1336          -0.016949          -0.016668          0.0         -0.05   \n",
       "1337          -0.016949          -0.016668          0.0         -0.05   \n",
       "1338          -0.016949          -0.016668          0.0         -0.05   \n",
       "1339          -0.016949          -0.016668          0.0         -0.05   \n",
       "\n",
       "      Comment_Minute  ...  competition_participation_second  \\\n",
       "0          -0.019231  ...                         -0.016668   \n",
       "1          -0.019231  ...                         -0.016668   \n",
       "2          -0.019231  ...                         -0.016668   \n",
       "3          -0.019231  ...                          0.203505   \n",
       "4          -0.019231  ...                         -0.016668   \n",
       "...              ...  ...                               ...   \n",
       "1335       -0.019231  ...                         -0.016668   \n",
       "1336       -0.019231  ...                         -0.016668   \n",
       "1337       -0.019231  ...                         -0.016668   \n",
       "1338       -0.019231  ...                          0.774010   \n",
       "1339       -0.019231  ...                         -0.016668   \n",
       "\n",
       "      competition_ended_hour  competition_ended_minute  \\\n",
       "0                  -0.043478                 -0.016949   \n",
       "1                  -0.043478                 -0.016949   \n",
       "2                  -0.043478                 -0.016949   \n",
       "3                   0.000000                  0.000000   \n",
       "4                  -0.043478                 -0.016949   \n",
       "...                      ...                       ...   \n",
       "1335               -0.043478                 -0.016949   \n",
       "1336               -0.043478                 -0.016949   \n",
       "1337               -0.043478                 -0.016949   \n",
       "1338                1.000000                  1.000000   \n",
       "1339               -0.043478                 -0.016949   \n",
       "\n",
       "      competition_ended_second  competition_started_hour  \\\n",
       "0                         -1.0                 -0.043478   \n",
       "1                         -1.0                 -0.043478   \n",
       "2                         -1.0                 -0.043478   \n",
       "3                          0.0                  0.000000   \n",
       "4                         -1.0                 -0.043478   \n",
       "...                        ...                       ...   \n",
       "1335                      -1.0                 -0.043478   \n",
       "1336                      -1.0                 -0.043478   \n",
       "1337                      -1.0                 -0.043478   \n",
       "1338                       0.0                  0.260870   \n",
       "1339                      -1.0                 -0.043478   \n",
       "\n",
       "      competition_started_minute  competition_started_second  \\\n",
       "0                      -0.016949                        -1.0   \n",
       "1                      -0.016949                        -1.0   \n",
       "2                      -0.016949                        -1.0   \n",
       "3                       0.000000                         0.0   \n",
       "4                      -0.016949                        -1.0   \n",
       "...                          ...                         ...   \n",
       "1335                   -0.016949                        -1.0   \n",
       "1336                   -0.016949                        -1.0   \n",
       "1337                   -0.016949                        -1.0   \n",
       "1338                    0.000000                         0.0   \n",
       "1339                   -0.016949                        -1.0   \n",
       "\n",
       "      User_sign-in_hour  User_sign-in_minute  User_sign-in_second  \n",
       "0              0.347826             0.847458             0.701464  \n",
       "1              0.652174             0.101695             0.954099  \n",
       "2              0.565217             0.186441             0.836641  \n",
       "3              0.826087             0.254237             0.951465  \n",
       "4              0.608696             0.728814             0.694182  \n",
       "...                 ...                  ...                  ...  \n",
       "1335           0.260870             0.593220             0.349386  \n",
       "1336           0.000000             0.796610             0.071477  \n",
       "1337           0.434783             0.338983             0.940898  \n",
       "1338           0.782609             0.559322             0.390210  \n",
       "1339           0.304348             0.474576             0.144221  \n",
       "\n",
       "[1340 rows x 87 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "36392055",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4231a6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df[~df.User_ID.isin(df_test.User_ID)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ddac3ff8",
   "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>Personal</th>\n",
       "      <th>Theme</th>\n",
       "      <th>Discussion_day</th>\n",
       "      <th>Discussion_Hour</th>\n",
       "      <th>Discussion_Minute</th>\n",
       "      <th>Discussion_Second</th>\n",
       "      <th>comment_day</th>\n",
       "      <th>Comment_Hour</th>\n",
       "      <th>Comment_Minute</th>\n",
       "      <th>...</th>\n",
       "      <th>competition_participation_second</th>\n",
       "      <th>competition_ended_hour</th>\n",
       "      <th>competition_ended_minute</th>\n",
       "      <th>competition_ended_second</th>\n",
       "      <th>competition_started_hour</th>\n",
       "      <th>competition_started_minute</th>\n",
       "      <th>competition_started_second</th>\n",
       "      <th>User_sign-in_hour</th>\n",
       "      <th>User_sign-in_minute</th>\n",
       "      <th>User_sign-in_second</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>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.190673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_004UKSJV</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.262712</td>\n",
       "      <td>0.116658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_008D2J6C</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.661658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_00ETFE7J</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.269932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_00RKDLAX</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.108696</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.413430</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>11068</th>\n",
       "      <td>ID_ZZHDXC1Q</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.134797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11069</th>\n",
       "      <td>ID_ZZJKFBR5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247993</td>\n",
       "      <td>-0.028986</td>\n",
       "      <td>-0.011299</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>-0.028986</td>\n",
       "      <td>-0.011299</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>0.202899</td>\n",
       "      <td>0.175141</td>\n",
       "      <td>0.118335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11070</th>\n",
       "      <td>ID_ZZQK0LU6</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.518322</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.814532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11071</th>\n",
       "      <td>ID_ZZUNZJ2U</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.302296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11072</th>\n",
       "      <td>ID_ZZVUJ45W</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.882392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.406780</td>\n",
       "      <td>0.562270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11073 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  Personal  Theme  Discussion_day  Discussion_Hour  \\\n",
       "0      ID_000H9XOP      -1.0    0.0             0.0        -0.043478   \n",
       "1      ID_004UKSJV      -1.0    0.0             0.0        -0.043478   \n",
       "2      ID_008D2J6C      -1.0    0.0             0.0        -0.043478   \n",
       "3      ID_00ETFE7J      -1.0    0.0             0.0        -0.043478   \n",
       "4      ID_00RKDLAX      -1.0    0.0             0.0        -0.043478   \n",
       "...            ...       ...    ...             ...              ...   \n",
       "11068  ID_ZZHDXC1Q      -1.0    0.0             0.0        -0.043478   \n",
       "11069  ID_ZZJKFBR5      -1.0    0.0             0.0        -0.043478   \n",
       "11070  ID_ZZQK0LU6      -1.0    0.0             0.0        -0.043478   \n",
       "11071  ID_ZZUNZJ2U      -1.0    0.0             0.0        -0.043478   \n",
       "11072  ID_ZZVUJ45W      -1.0    0.0             0.0        -0.043478   \n",
       "\n",
       "       Discussion_Minute  Discussion_Second  comment_day  Comment_Hour  \\\n",
       "0              -0.016949          -0.016668          0.0         -0.05   \n",
       "1              -0.016949          -0.016668          0.0         -0.05   \n",
       "2              -0.016949          -0.016668          0.0         -0.05   \n",
       "3              -0.016949          -0.016668          0.0         -0.05   \n",
       "4              -0.016949          -0.016668          0.0         -0.05   \n",
       "...                  ...                ...          ...           ...   \n",
       "11068          -0.016949          -0.016668          0.0         -0.05   \n",
       "11069          -0.016949          -0.016668          0.0         -0.05   \n",
       "11070          -0.016949          -0.016668          0.0         -0.05   \n",
       "11071          -0.016949          -0.016668          0.0         -0.05   \n",
       "11072          -0.016949          -0.016668          0.0         -0.05   \n",
       "\n",
       "       Comment_Minute  ...  competition_participation_second  \\\n",
       "0           -0.019231  ...                         -0.016668   \n",
       "1           -0.019231  ...                         -0.016668   \n",
       "2           -0.019231  ...                         -0.016668   \n",
       "3           -0.019231  ...                         -0.016668   \n",
       "4           -0.019231  ...                         -0.016668   \n",
       "...               ...  ...                               ...   \n",
       "11068       -0.019231  ...                         -0.016668   \n",
       "11069       -0.019231  ...                          0.247993   \n",
       "11070       -0.019231  ...                          0.518322   \n",
       "11071       -0.019231  ...                         -0.016668   \n",
       "11072       -0.019231  ...                          0.882392   \n",
       "\n",
       "       competition_ended_hour  competition_ended_minute  \\\n",
       "0                   -0.043478                 -0.016949   \n",
       "1                   -0.043478                 -0.016949   \n",
       "2                   -0.043478                 -0.016949   \n",
       "3                   -0.043478                 -0.016949   \n",
       "4                   -0.043478                 -0.016949   \n",
       "...                       ...                       ...   \n",
       "11068               -0.043478                 -0.016949   \n",
       "11069               -0.028986                 -0.011299   \n",
       "11070                0.217391                  1.000000   \n",
       "11071               -0.043478                 -0.016949   \n",
       "11072                1.000000                  1.000000   \n",
       "\n",
       "       competition_ended_second  competition_started_hour  \\\n",
       "0                     -1.000000                 -0.043478   \n",
       "1                     -1.000000                 -0.043478   \n",
       "2                     -1.000000                 -0.043478   \n",
       "3                     -1.000000                 -0.043478   \n",
       "4                     -1.000000                 -0.043478   \n",
       "...                         ...                       ...   \n",
       "11068                 -1.000000                 -0.043478   \n",
       "11069                 -0.666667                 -0.028986   \n",
       "11070                  0.000000                  0.391304   \n",
       "11071                 -1.000000                 -0.043478   \n",
       "11072                  0.000000                  0.260870   \n",
       "\n",
       "       competition_started_minute  competition_started_second  \\\n",
       "0                       -0.016949                   -1.000000   \n",
       "1                       -0.016949                   -1.000000   \n",
       "2                       -0.016949                   -1.000000   \n",
       "3                       -0.016949                   -1.000000   \n",
       "4                       -0.016949                   -1.000000   \n",
       "...                           ...                         ...   \n",
       "11068                   -0.016949                   -1.000000   \n",
       "11069                   -0.011299                   -0.666667   \n",
       "11070                    0.000000                    0.000000   \n",
       "11071                   -0.016949                   -1.000000   \n",
       "11072                    0.000000                    0.000000   \n",
       "\n",
       "       User_sign-in_hour  User_sign-in_minute  User_sign-in_second  \n",
       "0               0.652174             0.305085             0.190673  \n",
       "1               0.152174             0.262712             0.116658  \n",
       "2               0.391304             0.711864             0.661658  \n",
       "3               0.391304             0.322034             0.269932  \n",
       "4               0.108696             0.254237             0.413430  \n",
       "...                  ...                  ...                  ...  \n",
       "11068           0.695652             0.288136             0.134797  \n",
       "11069           0.202899             0.175141             0.118335  \n",
       "11070           0.391304             0.813559             0.814532  \n",
       "11071           0.956522             0.254237             0.302296  \n",
       "11072           0.347826             0.406780             0.562270  \n",
       "\n",
       "[11073 rows x 87 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8eefb976",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = df_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "800d2507",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.drop(\"User_ID\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "43caa1b6",
   "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>Personal</th>\n",
       "      <th>Theme</th>\n",
       "      <th>Discussion_day</th>\n",
       "      <th>Discussion_Hour</th>\n",
       "      <th>Discussion_Minute</th>\n",
       "      <th>Discussion_Second</th>\n",
       "      <th>comment_day</th>\n",
       "      <th>Comment_Hour</th>\n",
       "      <th>Comment_Minute</th>\n",
       "      <th>Comment_Second</th>\n",
       "      <th>...</th>\n",
       "      <th>competition_participation_second</th>\n",
       "      <th>competition_ended_hour</th>\n",
       "      <th>competition_ended_minute</th>\n",
       "      <th>competition_ended_second</th>\n",
       "      <th>competition_started_hour</th>\n",
       "      <th>competition_started_minute</th>\n",
       "      <th>competition_started_second</th>\n",
       "      <th>User_sign-in_hour</th>\n",
       "      <th>User_sign-in_minute</th>\n",
       "      <th>User_sign-in_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.190673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>0.262712</td>\n",
       "      <td>0.116658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.711864</td>\n",
       "      <td>0.661658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.269932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.108696</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.413430</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>11068</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>0.134797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11069</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247993</td>\n",
       "      <td>-0.028986</td>\n",
       "      <td>-0.011299</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>-0.028986</td>\n",
       "      <td>-0.011299</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>0.202899</td>\n",
       "      <td>0.175141</td>\n",
       "      <td>0.118335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11070</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.518322</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.814532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11071</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.302296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11072</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.016668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.018108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.882392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.406780</td>\n",
       "      <td>0.562270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11073 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Personal  Theme  Discussion_day  Discussion_Hour  Discussion_Minute  \\\n",
       "0          -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "1          -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "2          -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "3          -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "4          -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "...         ...    ...             ...              ...                ...   \n",
       "11068      -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "11069      -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "11070      -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "11071      -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "11072      -1.0    0.0             0.0        -0.043478          -0.016949   \n",
       "\n",
       "       Discussion_Second  comment_day  Comment_Hour  Comment_Minute  \\\n",
       "0              -0.016668          0.0         -0.05       -0.019231   \n",
       "1              -0.016668          0.0         -0.05       -0.019231   \n",
       "2              -0.016668          0.0         -0.05       -0.019231   \n",
       "3              -0.016668          0.0         -0.05       -0.019231   \n",
       "4              -0.016668          0.0         -0.05       -0.019231   \n",
       "...                  ...          ...           ...             ...   \n",
       "11068          -0.016668          0.0         -0.05       -0.019231   \n",
       "11069          -0.016668          0.0         -0.05       -0.019231   \n",
       "11070          -0.016668          0.0         -0.05       -0.019231   \n",
       "11071          -0.016668          0.0         -0.05       -0.019231   \n",
       "11072          -0.016668          0.0         -0.05       -0.019231   \n",
       "\n",
       "       Comment_Second  ...  competition_participation_second  \\\n",
       "0           -0.018108  ...                         -0.016668   \n",
       "1           -0.018108  ...                         -0.016668   \n",
       "2           -0.018108  ...                         -0.016668   \n",
       "3           -0.018108  ...                         -0.016668   \n",
       "4           -0.018108  ...                         -0.016668   \n",
       "...               ...  ...                               ...   \n",
       "11068       -0.018108  ...                         -0.016668   \n",
       "11069       -0.018108  ...                          0.247993   \n",
       "11070       -0.018108  ...                          0.518322   \n",
       "11071       -0.018108  ...                         -0.016668   \n",
       "11072       -0.018108  ...                          0.882392   \n",
       "\n",
       "       competition_ended_hour  competition_ended_minute  \\\n",
       "0                   -0.043478                 -0.016949   \n",
       "1                   -0.043478                 -0.016949   \n",
       "2                   -0.043478                 -0.016949   \n",
       "3                   -0.043478                 -0.016949   \n",
       "4                   -0.043478                 -0.016949   \n",
       "...                       ...                       ...   \n",
       "11068               -0.043478                 -0.016949   \n",
       "11069               -0.028986                 -0.011299   \n",
       "11070                0.217391                  1.000000   \n",
       "11071               -0.043478                 -0.016949   \n",
       "11072                1.000000                  1.000000   \n",
       "\n",
       "       competition_ended_second  competition_started_hour  \\\n",
       "0                     -1.000000                 -0.043478   \n",
       "1                     -1.000000                 -0.043478   \n",
       "2                     -1.000000                 -0.043478   \n",
       "3                     -1.000000                 -0.043478   \n",
       "4                     -1.000000                 -0.043478   \n",
       "...                         ...                       ...   \n",
       "11068                 -1.000000                 -0.043478   \n",
       "11069                 -0.666667                 -0.028986   \n",
       "11070                  0.000000                  0.391304   \n",
       "11071                 -1.000000                 -0.043478   \n",
       "11072                  0.000000                  0.260870   \n",
       "\n",
       "       competition_started_minute  competition_started_second  \\\n",
       "0                       -0.016949                   -1.000000   \n",
       "1                       -0.016949                   -1.000000   \n",
       "2                       -0.016949                   -1.000000   \n",
       "3                       -0.016949                   -1.000000   \n",
       "4                       -0.016949                   -1.000000   \n",
       "...                           ...                         ...   \n",
       "11068                   -0.016949                   -1.000000   \n",
       "11069                   -0.011299                   -0.666667   \n",
       "11070                    0.000000                    0.000000   \n",
       "11071                   -0.016949                   -1.000000   \n",
       "11072                    0.000000                    0.000000   \n",
       "\n",
       "       User_sign-in_hour  User_sign-in_minute  User_sign-in_second  \n",
       "0               0.652174             0.305085             0.190673  \n",
       "1               0.152174             0.262712             0.116658  \n",
       "2               0.391304             0.711864             0.661658  \n",
       "3               0.391304             0.322034             0.269932  \n",
       "4               0.108696             0.254237             0.413430  \n",
       "...                  ...                  ...                  ...  \n",
       "11068           0.695652             0.288136             0.134797  \n",
       "11069           0.202899             0.175141             0.118335  \n",
       "11070           0.391304             0.813559             0.814532  \n",
       "11071           0.956522             0.254237             0.302296  \n",
       "11072           0.347826             0.406780             0.562270  \n",
       "\n",
       "[11073 rows x 86 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ef5e71",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4970a5c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0a043d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA().fit(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "31836c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.03804901e-01, 9.83164866e-02, 5.38992739e-02, 5.01534404e-02,\n",
       "       3.96150284e-02, 2.79198607e-02, 2.25644210e-02, 1.93347649e-02,\n",
       "       1.87794320e-02, 1.50855461e-02, 1.37426112e-02, 1.31948346e-02,\n",
       "       1.21276323e-02, 1.05460846e-02, 8.81894931e-03, 7.75960212e-03,\n",
       "       6.31932071e-03, 5.54519859e-03, 5.29583665e-03, 5.06541863e-03,\n",
       "       4.70656586e-03, 4.57456263e-03, 4.40676643e-03, 4.03712658e-03,\n",
       "       3.81540458e-03, 2.96624532e-03, 2.70732989e-03, 2.52819324e-03,\n",
       "       2.45492759e-03, 2.42481367e-03, 2.32012588e-03, 2.08466621e-03,\n",
       "       2.03610219e-03, 1.85789221e-03, 1.77899422e-03, 1.45822936e-03,\n",
       "       1.27796780e-03, 1.20123555e-03, 1.09531693e-03, 1.04405131e-03,\n",
       "       8.91343974e-04, 8.53141506e-04, 7.92722782e-04, 7.80888470e-04,\n",
       "       7.20113259e-04, 6.73014712e-04, 6.36896020e-04, 5.89384093e-04,\n",
       "       5.42902008e-04, 5.29137098e-04, 5.13144106e-04, 4.53849287e-04,\n",
       "       4.34291526e-04, 3.92749415e-04, 2.78645659e-04, 2.46159208e-04,\n",
       "       2.42773732e-04, 1.89033019e-04, 1.76953245e-04, 1.66557330e-04,\n",
       "       1.57948336e-04, 1.21426383e-04, 1.07500895e-04, 9.68929841e-05,\n",
       "       9.65187087e-05, 8.31917032e-05, 7.49413815e-05, 6.97453779e-05,\n",
       "       6.66778016e-05, 5.81178901e-05, 5.51942020e-05, 4.90128595e-05,\n",
       "       4.73050754e-05, 4.17019818e-05, 3.61478208e-05, 2.36392177e-05,\n",
       "       2.05171225e-05, 1.15705151e-05, 7.87695463e-06, 2.75165012e-06,\n",
       "       1.82419836e-06, 3.71570622e-07, 1.40781302e-07, 1.22863326e-07,\n",
       "       9.93579976e-32, 2.65880329e-33])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6c5def2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imp = pd.DataFrame(pca.explained_variance_ratio_, columns=['pca_Importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f67eab3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imp.insert(0, \"Features\", df_temp.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bed76bf0",
   "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>Features</th>\n",
       "      <th>pca_Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Personal</td>\n",
       "      <td>5.038049e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Theme</td>\n",
       "      <td>9.831649e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Discussion_day</td>\n",
       "      <td>5.389927e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Discussion_Hour</td>\n",
       "      <td>5.015344e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Discussion_Minute</td>\n",
       "      <td>3.961503e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>competition_started_minute</td>\n",
       "      <td>3.715706e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>competition_started_second</td>\n",
       "      <td>1.407813e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>User_sign-in_hour</td>\n",
       "      <td>1.228633e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>User_sign-in_minute</td>\n",
       "      <td>9.935800e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>User_sign-in_second</td>\n",
       "      <td>2.658803e-33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Features  pca_Importance\n",
       "0                     Personal    5.038049e-01\n",
       "1                        Theme    9.831649e-02\n",
       "2               Discussion_day    5.389927e-02\n",
       "3              Discussion_Hour    5.015344e-02\n",
       "4            Discussion_Minute    3.961503e-02\n",
       "..                         ...             ...\n",
       "81  competition_started_minute    3.715706e-07\n",
       "82  competition_started_second    1.407813e-07\n",
       "83           User_sign-in_hour    1.228633e-07\n",
       "84         User_sign-in_minute    9.935800e-32\n",
       "85         User_sign-in_second    2.658803e-33\n",
       "\n",
       "[86 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "360e1fce",
   "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>Features</th>\n",
       "      <th>pca_Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Personal</td>\n",
       "      <td>5.038049e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Theme</td>\n",
       "      <td>9.831649e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Discussion_day</td>\n",
       "      <td>5.389927e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Discussion_Hour</td>\n",
       "      <td>5.015344e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Discussion_Minute</td>\n",
       "      <td>3.961503e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>competition_started_minute</td>\n",
       "      <td>3.715706e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>competition_started_second</td>\n",
       "      <td>1.407813e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>User_sign-in_hour</td>\n",
       "      <td>1.228633e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>User_sign-in_minute</td>\n",
       "      <td>9.935800e-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>User_sign-in_second</td>\n",
       "      <td>2.658803e-33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Features  pca_Importance\n",
       "0                     Personal    5.038049e-01\n",
       "1                        Theme    9.831649e-02\n",
       "2               Discussion_day    5.389927e-02\n",
       "3              Discussion_Hour    5.015344e-02\n",
       "4            Discussion_Minute    3.961503e-02\n",
       "..                         ...             ...\n",
       "81  competition_started_minute    3.715706e-07\n",
       "82  competition_started_second    1.407813e-07\n",
       "83           User_sign-in_hour    1.228633e-07\n",
       "84         User_sign-in_minute    9.935800e-32\n",
       "85         User_sign-in_second    2.658803e-33\n",
       "\n",
       "[86 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imp.sort_values(\"pca_Importance\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1d5aa8",
   "metadata": {},
   "source": [
    "## Criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "68ef01f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# count_mean = df_train[\"count_UA\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f260f6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conditions = [\n",
    "#     ((df_train[\"count_UA\"]>count_mean)|(df_train[\"Successful_Submission_Count\"]>0)|((df_train[\"Discussion_day\"]>0)&(df_train[\"comment_day\"]>0))),\n",
    "#     (df_train['count_UA']<=count_mean)]\n",
    "\n",
    "# values = [1, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9f429a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train['Active'] = np.select(conditions, values, default=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93c02225",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train[df_train['Active']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9dc3693",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "399a4883",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6059dfa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30005 entries, 0 to 30004\n",
      "Data columns (total 63 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Personal                     30005 non-null  float64\n",
      " 1   Theme                        30005 non-null  float64\n",
      " 2   Discussion_day               30005 non-null  float64\n",
      " 3   comment_day                  30005 non-null  float64\n",
      " 4   Participant Type             30005 non-null  float64\n",
      " 5   Successful Submission Count  30005 non-null  int64  \n",
      " 6   Comp_participation_day       30005 non-null  float64\n",
      " 7   FeatureA                     30005 non-null  float64\n",
      " 8   FeatureB                     30005 non-null  float64\n",
      " 9   FeatureC                     30005 non-null  float64\n",
      " 10  FeatureD                     30005 non-null  float64\n",
      " 11  FeatureE                     30005 non-null  float64\n",
      " 12  SecretCode                   30005 non-null  float64\n",
      " 13  FeatureF                     30005 non-null  float64\n",
      " 14  FeatureG                     30005 non-null  float64\n",
      " 15  FeatureH                     30005 non-null  float64\n",
      " 16  FeatureI                     30005 non-null  float64\n",
      " 17  Country_ID                   30005 non-null  int64  \n",
      " 18  competition_ended_year       30005 non-null  float64\n",
      " 19  competition_ended_month      30005 non-null  float64\n",
      " 20  competition_ended_day        30005 non-null  float64\n",
      " 21  competition_started_year     30005 non-null  float64\n",
      " 22  competition_started_month    30005 non-null  float64\n",
      " 23  competition_started_day      30005 non-null  float64\n",
      " 24  datetime Month               30005 non-null  float64\n",
      " 25  Activity Type                30005 non-null  float64\n",
      " 26  Day last active              30005 non-null  float64\n",
      " 27  Blog Theme                   30005 non-null  float64\n",
      " 28  Blog Published Year          30005 non-null  float64\n",
      " 29  Blog Published Month         30005 non-null  float64\n",
      " 30  Published At Day_of_month    30005 non-null  float64\n",
      " 31  Remote                       30005 non-null  float64\n",
      " 32  Experience                   30005 non-null  float64\n",
      " 33  Employment Type              30005 non-null  float64\n",
      " 34  Company Size                 30005 non-null  float64\n",
      " 35  Industry_FS                  30005 non-null  float64\n",
      " 36  Industry_Bank                30005 non-null  float64\n",
      " 37  Industry_Loc                 30005 non-null  float64\n",
      " 38  Industry_Gov                 30005 non-null  float64\n",
      " 39  Industry_Health              30005 non-null  float64\n",
      " 40  Industry_Res                 30005 non-null  float64\n",
      " 41  Industry_Amb                 30005 non-null  float64\n",
      " 42  Industry_Transp              30005 non-null  float64\n",
      " 43  Industry_Scholar             30005 non-null  float64\n",
      " 44  Industry_Log                 30005 non-null  float64\n",
      " 45  Industry_Media               30005 non-null  float64\n",
      " 46  Industry_Manuf               30005 non-null  float64\n",
      " 47  Industry_Sales               30005 non-null  float64\n",
      " 48  Industry_Insur               30005 non-null  float64\n",
      " 49  Industry_Manage              30005 non-null  float64\n",
      " 50  Industry_AI                  30005 non-null  float64\n",
      " 51  Industry_Zindi               30005 non-null  float64\n",
      " 52  Industry_Tech                30005 non-null  float64\n",
      " 53  Function_DE                  30005 non-null  float64\n",
      " 54  Function_DM                  30005 non-null  float64\n",
      " 55  Function_Bus                 30005 non-null  float64\n",
      " 56  Function_AI                  30005 non-null  float64\n",
      " 57  Function_ML                  30005 non-null  float64\n",
      " 58  count_UA                     30005 non-null  float64\n",
      " 59  Day duration                 30005 non-null  float64\n",
      " 60  FeatureX                     30005 non-null  float64\n",
      " 61  FeatureY                     30005 non-null  float64\n",
      " 62  User sign-in day             30005 non-null  float64\n",
      "dtypes: float64(61), int64(2)\n",
      "memory usage: 14.4 MB\n"
     ]
    }
   ],
   "source": [
    "df_temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e52390",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nameList = [\"count_UA\", \"Successful_Submission_Count\", \"Day_last_active\", \"Day_duration\", \"Activity_Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "064e0c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nameList = [\"count_UA\", \"Successful Submission Count\", \"Day last active\", \"Day duration\", \"Activity Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dc3f692e",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df_temp[nameList]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "649e1af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e28ca7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0ba82c7e",
   "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>PCA(n_components=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><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\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(df_temp[nameList])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a56d31fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_proj = pca.transform(df_temp[nameList])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a4363f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30005, 2)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_proj.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8d89042e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PCA Component 2')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_proj[:,0], X_proj[:,1])\n",
    "plt.title(\"PCA Data points\")\n",
    "plt.xlabel(\"PCA Component 1\")\n",
    "plt.ylabel(\"PCA Component 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957b475e",
   "metadata": {},
   "source": [
    "## TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "88f67005",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "19672615",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = df_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "77f942aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.drop(\"User_ID\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a9fbe21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nameList = [\"count_UA\", \"Successful_Submission_Count\", \"Day_last_active\", \"Day_duration\", \"Activity_Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c64beeba",
   "metadata": {},
   "outputs": [],
   "source": [
    "nameList = [\"count_UA\", \"Successful Submission Count\", \"Day last active\", \"Day duration\", \"Activity Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8a0ade06",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_embedded = TSNE(n_components=2, learning_rate='auto', init='random', perplexity=3).fit_transform(df_temp[nameList])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f7086aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30005, 2)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_embedded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4f2fa28d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-42.773643 ,   8.095752 ],\n",
       "       [ 15.891275 , -92.41821  ],\n",
       "       [-26.422226 ,   3.1524746],\n",
       "       ...,\n",
       "       [ 91.966286 ,  12.959077 ],\n",
       "       [ 91.96879  ,  12.9547   ],\n",
       "       [ 91.96983  ,  12.954823 ]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_embedded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "faf45183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'TSNE Dimension 2')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_embedded[:,0], X_embedded[:,1])\n",
    "plt.title(\"TSNE Data points\")\n",
    "plt.xlabel(\"TSNE Dimension 1\")\n",
    "plt.ylabel(\"TSNE Dimension 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "880d4a69",
   "metadata": {},
   "source": [
    "### KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "840a7d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "69241682",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(init='k-means++', n_clusters=2, n_init=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "56206780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, n_init=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2, n_init=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=2, n_init=2)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(df_temp[nameList])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7cbd256b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = kmeans.predict(df_temp[nameList])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2c855e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5bf620c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28758"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(y_pred, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d2b8aba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] == 0:\n",
    "        y_pred[i] = 1\n",
    "    elif y_pred[i] == 1:\n",
    "        y_pred[i] = 0\n",
    "#     elif y_pred[i] == 1:\n",
    "#         y_pred[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aa0f46ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1247"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op.countOf(y_pred, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6009ad14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21466a84790>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_proj[:,0], X_proj[:,1], c=y_pred)\n",
    "plt.title(\"KMeans Clustering Results\")\n",
    "plt.xlabel(\"PCA Component 1\")\n",
    "plt.ylabel(\"PCA Component 2\")\n",
    "plt.legend([\"1\", \"0\"], loc =\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "753bda00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25635af6f70>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_temp[\"count_UA\"], df_temp[\"Day_duration\"], c=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8734ea4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25635b664f0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_temp[\"Successful_Submission_Count\"], df_temp[\"count_UA\"], c=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "11278b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25635bc5910>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_temp[\"Day_duration\"], df_temp[\"Day_last_active\"], c=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f3c3a1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'TSNE Dimension 2')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_embedded[:,0], X_embedded[:,1], c=y_pred)\n",
    "plt.title(\"TSNE Data points\")\n",
    "plt.xlabel(\"TSNE Dimension 1\")\n",
    "plt.ylabel(\"TSNE Dimension 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f2dabdaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp[\"Active\"] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "65ded745",
   "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>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>FeatureC</th>\n",
       "      <th>...</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",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>-0.028571</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>0.000004</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>-0.028571</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>0.000008</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>-0.028571</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>0.000007</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>-0.028571</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>0.000007</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>-0.028571</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>0.000007</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>9657</th>\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>-0.028571</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>0.000005</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9658</th>\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>0.258065</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.371429</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>0.000030</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9659</th>\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>0.385714</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>0.000087</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9660</th>\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>-0.028571</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>0.000008</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9661</th>\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>0.114286</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>0.000034</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9662 rows × 64 columns</p>\n",
       "</div>"
      ],
      "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",
       "4         -1.0    0.0             0.0          0.0               0.0   \n",
       "...        ...    ...             ...          ...               ...   \n",
       "9657      -1.0    0.0             0.0          0.0               0.0   \n",
       "9658      -1.0    0.0             0.0          0.0               1.0   \n",
       "9659      -1.0    0.0             0.0          0.0               1.0   \n",
       "9660      -1.0    0.0             0.0          0.0               0.0   \n",
       "9661      -1.0    0.0             0.0          0.0               1.0   \n",
       "\n",
       "      Successful_Submission_Count  Comp_participation_day  FeatureA  FeatureB  \\\n",
       "0                             0.0                0.000000   -0.1250   -0.0625   \n",
       "1                             0.0                0.000000   -0.1250   -0.0625   \n",
       "2                             0.0                0.000000   -0.1250   -0.0625   \n",
       "3                             0.0                0.000000   -0.1250   -0.0625   \n",
       "4                             0.0                0.000000   -0.1250   -0.0625   \n",
       "...                           ...                     ...       ...       ...   \n",
       "9657                          0.0                0.000000   -0.1250   -0.0625   \n",
       "9658                          0.0                0.258065    0.1250    0.0625   \n",
       "9659                          0.0                0.935484    0.1875    0.0000   \n",
       "9660                          0.0                0.000000   -0.1250   -0.0625   \n",
       "9661                          0.0                0.161290    0.1250    0.4375   \n",
       "\n",
       "      FeatureC  ...  Function_DM  Function_Bus  Function_AI  Function_ML  \\\n",
       "0    -0.028571  ...          0.0           0.0          0.0          0.0   \n",
       "1    -0.028571  ...          0.0           0.0          0.0          0.0   \n",
       "2    -0.028571  ...          0.0           0.0          0.0          0.0   \n",
       "3    -0.028571  ...          0.0           0.0          0.0          0.0   \n",
       "4    -0.028571  ...          0.0           0.0          0.0          0.0   \n",
       "...        ...  ...          ...           ...          ...          ...   \n",
       "9657 -0.028571  ...          0.0           0.0          0.0          0.0   \n",
       "9658  0.371429  ...          0.0           0.0          0.0          0.0   \n",
       "9659  0.385714  ...          0.0           0.0          0.0          0.0   \n",
       "9660 -0.028571  ...          0.0           0.0          0.0          0.0   \n",
       "9661  0.114286  ...          0.0           0.0          0.0          0.0   \n",
       "\n",
       "      count_UA  Day_duration  FeatureX  FeatureY  User_sign-in_day  Active  \n",
       "0     0.000004      0.161290       0.0  0.333333          0.709677       0  \n",
       "1     0.000008      0.048387       0.0  0.000000          0.903226       0  \n",
       "2     0.000007      0.290323       0.0  0.000000          0.225806       1  \n",
       "3     0.000007      0.322581       0.0  0.000000          0.193548       1  \n",
       "4     0.000007      0.032258       0.0  0.333333          0.741935       0  \n",
       "...        ...           ...       ...       ...               ...     ...  \n",
       "9657  0.000005      0.032258       0.0  0.000000          0.870968       0  \n",
       "9658  0.000030      0.064516       0.0  0.333333          0.258065       0  \n",
       "9659  0.000087      0.064516       0.0  1.000000          0.935484       0  \n",
       "9660  0.000008      0.032258       0.0  0.000000          0.967742       0  \n",
       "9661  0.000034      0.032258       0.0  0.000000          0.161290       1  \n",
       "\n",
       "[9662 rows x 64 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4f7bbcc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_check = pd.DataFrame(df_temp[nameList], columns=nameList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "14347911",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_check[\"Active\"] = df_temp[\"Active\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1248729d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_UA</th>\n",
       "      <th>Successful_Submission_Count</th>\n",
       "      <th>Day_last_active</th>\n",
       "      <th>Day_duration</th>\n",
       "      <th>Activity_Type</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.579710</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>0.760870</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.604348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.626087</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.741546</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9657</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.559783</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9658</th>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.908662</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9659</th>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.850097</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9660</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.648551</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9661</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.535117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9662 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      count_UA  Successful_Submission_Count  Day_last_active  Day_duration  \\\n",
       "0     0.000004                          0.0         0.838710      0.161290   \n",
       "1     0.000008                          0.0         0.935484      0.048387   \n",
       "2     0.000007                          0.0         0.483871      0.290323   \n",
       "3     0.000007                          0.0         0.483871      0.322581   \n",
       "4     0.000007                          0.0         0.741935      0.032258   \n",
       "...        ...                          ...              ...           ...   \n",
       "9657  0.000005                          0.0         0.870968      0.032258   \n",
       "9658  0.000030                          0.0         0.645161      0.064516   \n",
       "9659  0.000087                          0.0         0.967742      0.064516   \n",
       "9660  0.000008                          0.0         0.967742      0.032258   \n",
       "9661  0.000034                          0.0         0.161290      0.032258   \n",
       "\n",
       "      Activity_Type  Active  \n",
       "0          0.579710       0  \n",
       "1          0.760870       0  \n",
       "2          0.604348       1  \n",
       "3          0.626087       1  \n",
       "4          0.741546       0  \n",
       "...             ...     ...  \n",
       "9657       0.559783       0  \n",
       "9658       0.908662       0  \n",
       "9659       0.850097       0  \n",
       "9660       0.648551       0  \n",
       "9661       0.535117       1  \n",
       "\n",
       "[9662 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4b7c8b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_UA</th>\n",
       "      <th>Successful_Submission_Count</th>\n",
       "      <th>Day_last_active</th>\n",
       "      <th>Day_duration</th>\n",
       "      <th>Activity_Type</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.284946</td>\n",
       "      <td>0.822120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.306452</td>\n",
       "      <td>0.689545</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.706333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0.241935</td>\n",
       "      <td>0.720934</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.002916</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.620968</td>\n",
       "      <td>0.720642</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9534</th>\n",
       "      <td>0.001828</td>\n",
       "      <td>0.414286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.505376</td>\n",
       "      <td>0.793345</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9537</th>\n",
       "      <td>0.000623</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.815756</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9620</th>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.741567</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9642</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.721889</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9652</th>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.502863</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      count_UA  Successful_Submission_Count  Day_last_active  Day_duration  \\\n",
       "24    0.000521                     0.160000         0.967742      0.284946   \n",
       "32    0.000616                     0.380000         0.967742      0.306452   \n",
       "41    0.000436                     0.200000         0.774194      0.451613   \n",
       "71    0.000103                     0.500000         0.548387      0.241935   \n",
       "76    0.002916                     0.350000         0.967742      0.620968   \n",
       "...        ...                          ...              ...           ...   \n",
       "9534  0.001828                     0.414286         1.000000      0.505376   \n",
       "9537  0.000623                     1.000000         0.967742      0.516129   \n",
       "9620  0.000182                     0.200000         0.806452      0.258065   \n",
       "9642  0.000020                     0.600000         0.709677      0.032258   \n",
       "9652  0.000181                     0.750000         0.935484      0.258065   \n",
       "\n",
       "      Activity_Type  Active  \n",
       "24         0.822120       0  \n",
       "32         0.689545       0  \n",
       "41         0.706333       0  \n",
       "71         0.720934       0  \n",
       "76         0.720642       0  \n",
       "...             ...     ...  \n",
       "9534       0.793345       0  \n",
       "9537       0.815756       0  \n",
       "9620       0.741567       0  \n",
       "9642       0.721889       0  \n",
       "9652       0.502863       0  \n",
       "\n",
       "[835 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_check[df_check[\"Successful_Submission_Count\"]>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "13cf0139",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.insert(0, \"User_ID\", df_train[\"User_ID\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ecf7dc49",
   "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>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_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",
       "      <th>Active</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>0.000004</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>0.000008</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>0.000007</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>0.000007</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_00RKDLAX</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>0.000007</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>9657</th>\n",
       "      <td>ID_ZZHDXC1Q</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>0.000005</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9658</th>\n",
       "      <td>ID_ZZJKFBR5</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>0.258065</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>0.000030</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9659</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>0.000087</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9660</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>0.000008</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.967742</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9661</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>0.000034</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9662 rows × 65 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_004UKSJV      -1.0    0.0             0.0          0.0   \n",
       "2     ID_008D2J6C      -1.0    0.0             0.0          0.0   \n",
       "3     ID_00ETFE7J      -1.0    0.0             0.0          0.0   \n",
       "4     ID_00RKDLAX      -1.0    0.0             0.0          0.0   \n",
       "...           ...       ...    ...             ...          ...   \n",
       "9657  ID_ZZHDXC1Q      -1.0    0.0             0.0          0.0   \n",
       "9658  ID_ZZJKFBR5      -1.0    0.0             0.0          0.0   \n",
       "9659  ID_ZZQK0LU6      -1.0    0.0             0.0          0.0   \n",
       "9660  ID_ZZUNZJ2U      -1.0    0.0             0.0          0.0   \n",
       "9661  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",
       "9657               0.0                          0.0                0.000000   \n",
       "9658               1.0                          0.0                0.258065   \n",
       "9659               1.0                          0.0                0.935484   \n",
       "9660               0.0                          0.0                0.000000   \n",
       "9661               1.0                          0.0                0.161290   \n",
       "\n",
       "      FeatureA  FeatureB  ...  Function_DM  Function_Bus  Function_AI  \\\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",
       "9657   -0.1250   -0.0625  ...          0.0           0.0          0.0   \n",
       "9658    0.1250    0.0625  ...          0.0           0.0          0.0   \n",
       "9659    0.1875    0.0000  ...          0.0           0.0          0.0   \n",
       "9660   -0.1250   -0.0625  ...          0.0           0.0          0.0   \n",
       "9661    0.1250    0.4375  ...          0.0           0.0          0.0   \n",
       "\n",
       "      Function_ML  count_UA  Day_duration  FeatureX  FeatureY  \\\n",
       "0             0.0  0.000004      0.161290       0.0  0.333333   \n",
       "1             0.0  0.000008      0.048387       0.0  0.000000   \n",
       "2             0.0  0.000007      0.290323       0.0  0.000000   \n",
       "3             0.0  0.000007      0.322581       0.0  0.000000   \n",
       "4             0.0  0.000007      0.032258       0.0  0.333333   \n",
       "...           ...       ...           ...       ...       ...   \n",
       "9657          0.0  0.000005      0.032258       0.0  0.000000   \n",
       "9658          0.0  0.000030      0.064516       0.0  0.333333   \n",
       "9659          0.0  0.000087      0.064516       0.0  1.000000   \n",
       "9660          0.0  0.000008      0.032258       0.0  0.000000   \n",
       "9661          0.0  0.000034      0.032258       0.0  0.000000   \n",
       "\n",
       "      User_sign-in_day  Active  \n",
       "0             0.709677       0  \n",
       "1             0.903226       0  \n",
       "2             0.225806       1  \n",
       "3             0.193548       1  \n",
       "4             0.741935       0  \n",
       "...                ...     ...  \n",
       "9657          0.870968       0  \n",
       "9658          0.258065       0  \n",
       "9659          0.935484       0  \n",
       "9660          0.967742       0  \n",
       "9661          0.161290       1  \n",
       "\n",
       "[9662 rows x 65 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5dd94c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp.to_csv('train.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}