YoutubeFinalProject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"youtube.csv\", low_memory=False, sep=',')\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "...                     ...            ...   \n",
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       "\n",
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       "3      Append Two Number Columns and Sort. Excel Magi...   \n",
       "4      LET Function Advanced Array Formula for Dynami...   \n",
       "...                                                  ...   \n",
       "44256                                 KDD2016 paper 1202   \n",
       "44257                                 KDD2016 paper 1227   \n",
       "44258                                 KDD2016 paper 1236   \n",
       "44259                                  KDD2016 paper 958   \n",
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       "\n",
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       "3      Download Excel File: http://excelisfun.net/fil...             27.0   \n",
       "4      Download Excel File: http://excelisfun.net/fil...             27.0   \n",
       "...                                                  ...              ...   \n",
       "44256  Title: Sampling of Attributed Networks from Hi...             22.0   \n",
       "44257  Title: Identifying Earmarks in Congressional B...             22.0   \n",
       "44258  Title: Online Feature Selection: A Limited-Mem...             22.0   \n",
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       "\n",
       "      videoCategoryLabel  duration  ...  dimension definition caption  \\\n",
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       "...                  ...       ...  ...        ...        ...     ...   \n",
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       "0                                                    NaN             1.0   \n",
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       "\n",
       "       viewCount  likeCount  dislikeCount  favoriteCount  commentCount  \n",
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       "[44261 rows x 21 columns]"
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     "execution_count": 363,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
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       "      <th>videoCategoryId</th>\n",
       "      <th>durationSec</th>\n",
       "      <th>licensedContent</th>\n",
       "      <th>viewCount</th>\n",
       "      <th>likeCount</th>\n",
       "      <th>dislikeCount</th>\n",
       "      <th>favoriteCount</th>\n",
       "      <th>commentCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>44260.000000</td>\n",
       "      <td>44260.000000</td>\n",
       "      <td>18811.0</td>\n",
       "      <td>4.425800e+04</td>\n",
       "      <td>43402.000000</td>\n",
       "      <td>43402.000000</td>\n",
       "      <td>44260.0</td>\n",
       "      <td>34896.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.151808</td>\n",
       "      <td>808.806236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.238338e+04</td>\n",
       "      <td>538.726395</td>\n",
       "      <td>15.207755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.995931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.566852</td>\n",
       "      <td>816.988443</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.288052e+05</td>\n",
       "      <td>5939.187241</td>\n",
       "      <td>146.440021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>388.827636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.070000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>471.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.261500e+03</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1068.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.289650e+04</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>3599.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.778718e+07</td>\n",
       "      <td>432355.000000</td>\n",
       "      <td>12563.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25774.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       videoCategoryId   durationSec  licensedContent     viewCount  \\\n",
       "count     44260.000000  44260.000000          18811.0  4.425800e+04   \n",
       "mean         27.151808    808.806236              1.0  3.238338e+04   \n",
       "std           1.566852    816.988443              0.0  2.288052e+05   \n",
       "min           1.000000      0.000000              1.0  0.000000e+00   \n",
       "25%          27.000000    247.000000              1.0  3.070000e+02   \n",
       "50%          27.000000    471.000000              1.0  2.261500e+03   \n",
       "75%          28.000000   1068.000000              1.0  1.289650e+04   \n",
       "max          29.000000   3599.000000              1.0  1.778718e+07   \n",
       "\n",
       "           likeCount  dislikeCount  favoriteCount  commentCount  \n",
       "count   43402.000000  43402.000000        44260.0  34896.000000  \n",
       "mean      538.726395     15.207755            0.0     47.995931  \n",
       "std      5939.187241    146.440021            0.0    388.827636  \n",
       "min         0.000000      0.000000            0.0      0.000000  \n",
       "25%         2.000000      0.000000            0.0      0.000000  \n",
       "50%        18.000000      1.000000            0.0      4.000000  \n",
       "75%       126.000000      5.000000            0.0     21.000000  \n",
       "max    432355.000000  12563.000000            0.0  25774.000000  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 44261 entries, 0 to 44260\n",
      "Data columns (total 21 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   channelId           44261 non-null  object \n",
      " 1   channelTitle        44261 non-null  object \n",
      " 2   videoId             44261 non-null  object \n",
      " 3   publishedAt         44261 non-null  object \n",
      " 4   publishedAtSQL      44261 non-null  object \n",
      " 5   videoTitle          44261 non-null  object \n",
      " 6   videoDescription    43779 non-null  object \n",
      " 7   videoCategoryId     44260 non-null  float64\n",
      " 8   videoCategoryLabel  44260 non-null  object \n",
      " 9   duration            44260 non-null  object \n",
      " 10  durationSec         44260 non-null  float64\n",
      " 11  dimension           44260 non-null  object \n",
      " 12  definition          44260 non-null  object \n",
      " 13  caption             44260 non-null  object \n",
      " 14  thumbnail_maxres    34232 non-null  object \n",
      " 15  licensedContent     18811 non-null  float64\n",
      " 16  viewCount           44258 non-null  float64\n",
      " 17  likeCount           43402 non-null  float64\n",
      " 18  dislikeCount        43402 non-null  float64\n",
      " 19  favoriteCount       44260 non-null  float64\n",
      " 20  commentCount        34896 non-null  float64\n",
      "dtypes: float64(8), object(13)\n",
      "memory usage: 7.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['channelId', 'channelTitle', 'videoId', 'publishedAt', 'publishedAtSQL',\n",
       "       'videoTitle', 'videoDescription', 'videoCategoryId',\n",
       "       'videoCategoryLabel', 'duration', 'durationSec', 'dimension',\n",
       "       'definition', 'caption', 'thumbnail_maxres', 'licensedContent',\n",
       "       'viewCount', 'likeCount', 'dislikeCount', 'favoriteCount',\n",
       "       'commentCount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channelId                 0\n",
       "channelTitle              0\n",
       "videoId                   0\n",
       "publishedAt               0\n",
       "publishedAtSQL            0\n",
       "videoTitle                0\n",
       "videoDescription        482\n",
       "videoCategoryId           1\n",
       "videoCategoryLabel        1\n",
       "duration                  1\n",
       "durationSec               1\n",
       "dimension                 1\n",
       "definition                1\n",
       "caption                   1\n",
       "thumbnail_maxres      10029\n",
       "licensedContent       25450\n",
       "viewCount                 3\n",
       "likeCount               859\n",
       "dislikeCount            859\n",
       "favoriteCount             1\n",
       "commentCount           9365\n",
       "dtype: int64"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>UCkndrGoNpUDV-uia6a9jwVg</td>\n",
       "      <td>ExcelIsFun</td>\n",
       "      <td>oS_tUs51T0A</td>\n",
       "      <td>2020-08-17T21:23:13Z</td>\n",
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       "      <td>UCkndrGoNpUDV-uia6a9jwVg</td>\n",
       "      <td>ExcelIsFun</td>\n",
       "      <td>yvmdIX9ZsPA</td>\n",
       "      <td>2020-08-14T15:02:25Z</td>\n",
       "      <td>14/8/20 15:02</td>\n",
       "      <td>Dueling Excel #191: Net Working Hours Spanning...</td>\n",
       "      <td>Download Excel File: http://excelisfun.net/fil...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>PT17M11S</td>\n",
       "      <td>...</td>\n",
       "      <td>2d</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UCkndrGoNpUDV-uia6a9jwVg</td>\n",
       "      <td>ExcelIsFun</td>\n",
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       "      <td>2020-08-10T19:13:08Z</td>\n",
       "      <td>10/8/20 19:13</td>\n",
       "      <td>Append Two Number Columns and Sort. Excel Magi...</td>\n",
       "      <td>Download Excel File: http://excelisfun.net/fil...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
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       "      <td>2d</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>UCkndrGoNpUDV-uia6a9jwVg</td>\n",
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       "      <td>Education</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
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       "      <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>44256</th>\n",
       "      <td>UCPsUUDUlcTJuP-fRa7z85aQ</td>\n",
       "      <td>KDD2016 video</td>\n",
       "      <td>RoUrdoU_2F4</td>\n",
       "      <td>2016-06-30T17:20:11Z</td>\n",
       "      <td>30/6/16 17:20</td>\n",
       "      <td>KDD2016 paper 1202</td>\n",
       "      <td>Title: Sampling of Attributed Networks from Hi...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>PT2M57S</td>\n",
       "      <td>...</td>\n",
       "      <td>2d</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>https://i.ytimg.com/vi/RoUrdoU_2F4/maxresdefau...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44257</th>\n",
       "      <td>UCPsUUDUlcTJuP-fRa7z85aQ</td>\n",
       "      <td>KDD2016 video</td>\n",
       "      <td>XNf7gBGEq28</td>\n",
       "      <td>2016-06-30T17:20:10Z</td>\n",
       "      <td>30/6/16 17:20</td>\n",
       "      <td>KDD2016 paper 1227</td>\n",
       "      <td>Title: Identifying Earmarks in Congressional B...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>PT3M15S</td>\n",
       "      <td>...</td>\n",
       "      <td>2d</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>https://i.ytimg.com/vi/XNf7gBGEq28/maxresdefau...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44258</th>\n",
       "      <td>UCPsUUDUlcTJuP-fRa7z85aQ</td>\n",
       "      <td>KDD2016 video</td>\n",
       "      <td>LIBLD8NM04Y</td>\n",
       "      <td>2016-06-30T17:20:09Z</td>\n",
       "      <td>30/6/16 17:20</td>\n",
       "      <td>KDD2016 paper 1236</td>\n",
       "      <td>Title: Online Feature Selection: A Limited-Mem...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>PT2M51S</td>\n",
       "      <td>...</td>\n",
       "      <td>2d</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>https://i.ytimg.com/vi/LIBLD8NM04Y/maxresdefau...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44259</th>\n",
       "      <td>UCPsUUDUlcTJuP-fRa7z85aQ</td>\n",
       "      <td>KDD2016 video</td>\n",
       "      <td>_s3p4s9dUCU</td>\n",
       "      <td>2016-06-30T17:20:09Z</td>\n",
       "      <td>30/6/16 17:20</td>\n",
       "      <td>KDD2016 paper 958</td>\n",
       "      <td>Title: Scalable Betweenness Centrality Maximiz...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>PT2M19S</td>\n",
       "      <td>...</td>\n",
       "      <td>2d</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>https://i.ytimg.com/vi/_s3p4s9dUCU/maxresdefau...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44260</th>\n",
       "      <td>UCPsUUDUlcTJuP-fRa7z85aQ</td>\n",
       "      <td>KDD2016 video</td>\n",
       "      <td>l2IlkEC0-hk</td>\n",
       "      <td>2016-06-30T17:20:08Z</td>\n",
       "      <td>30/6/16 17:20</td>\n",
       "      <td>KDD2016 paper 799</td>\n",
       "      <td>Title: Identifying Earmarks in Congressional B...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>PT2M4S</td>\n",
       "      <td>...</td>\n",
       "      <td>2d</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>https://i.ytimg.com/vi/l2IlkEC0-hk/maxresdefau...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>44261 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      channelId   channelTitle      videoId  \\\n",
       "0      UCkndrGoNpUDV-uia6a9jwVg     ExcelIsFun  Z2Cy9IY7IBA   \n",
       "1      UCkndrGoNpUDV-uia6a9jwVg     ExcelIsFun  oS_tUs51T0A   \n",
       "2      UCkndrGoNpUDV-uia6a9jwVg     ExcelIsFun  yvmdIX9ZsPA   \n",
       "3      UCkndrGoNpUDV-uia6a9jwVg     ExcelIsFun  08kgJ8WM_Q0   \n",
       "4      UCkndrGoNpUDV-uia6a9jwVg     ExcelIsFun  h-qLNikIs-0   \n",
       "...                         ...            ...          ...   \n",
       "44256  UCPsUUDUlcTJuP-fRa7z85aQ  KDD2016 video  RoUrdoU_2F4   \n",
       "44257  UCPsUUDUlcTJuP-fRa7z85aQ  KDD2016 video  XNf7gBGEq28   \n",
       "44258  UCPsUUDUlcTJuP-fRa7z85aQ  KDD2016 video  LIBLD8NM04Y   \n",
       "44259  UCPsUUDUlcTJuP-fRa7z85aQ  KDD2016 video  _s3p4s9dUCU   \n",
       "44260  UCPsUUDUlcTJuP-fRa7z85aQ  KDD2016 video  l2IlkEC0-hk   \n",
       "\n",
       "                publishedAt publishedAtSQL  \\\n",
       "0      2020-08-20T20:39:38Z  20/8/20 20:39   \n",
       "1      2020-08-17T21:23:13Z  17/8/20 21:23   \n",
       "2      2020-08-14T15:02:25Z  14/8/20 15:02   \n",
       "3      2020-08-10T19:13:08Z  10/8/20 19:13   \n",
       "4      2020-08-03T22:44:45Z   3/8/20 22:44   \n",
       "...                     ...            ...   \n",
       "44256  2016-06-30T17:20:11Z  30/6/16 17:20   \n",
       "44257  2016-06-30T17:20:10Z  30/6/16 17:20   \n",
       "44258  2016-06-30T17:20:09Z  30/6/16 17:20   \n",
       "44259  2016-06-30T17:20:09Z  30/6/16 17:20   \n",
       "44260  2016-06-30T17:20:08Z  30/6/16 17:20   \n",
       "\n",
       "                                              videoTitle  \\\n",
       "0      Add 500 to All Numbers in Text String? LET or ...   \n",
       "1      Dynamic Excel Multiplication Table with Condit...   \n",
       "2      Dueling Excel #191: Net Working Hours Spanning...   \n",
       "3      Append Two Number Columns and Sort. Excel Magi...   \n",
       "4      LET Function Advanced Array Formula for Dynami...   \n",
       "...                                                  ...   \n",
       "44256                                 KDD2016 paper 1202   \n",
       "44257                                 KDD2016 paper 1227   \n",
       "44258                                 KDD2016 paper 1236   \n",
       "44259                                  KDD2016 paper 958   \n",
       "44260                                  KDD2016 paper 799   \n",
       "\n",
       "                                        videoDescription  videoCategoryId  \\\n",
       "0      Download Excel File: http://excelisfun.net/fil...             27.0   \n",
       "1      Download Excel File: http://excelisfun.net/fil...             27.0   \n",
       "2      Download Excel File: http://excelisfun.net/fil...             27.0   \n",
       "3      Download Excel File: http://excelisfun.net/fil...             27.0   \n",
       "4      Download Excel File: http://excelisfun.net/fil...             27.0   \n",
       "...                                                  ...              ...   \n",
       "44256  Title: Sampling of Attributed Networks from Hi...             22.0   \n",
       "44257  Title: Identifying Earmarks in Congressional B...             22.0   \n",
       "44258  Title: Online Feature Selection: A Limited-Mem...             22.0   \n",
       "44259  Title: Scalable Betweenness Centrality Maximiz...             22.0   \n",
       "44260  Title: Identifying Earmarks in Congressional B...             22.0   \n",
       "\n",
       "      videoCategoryLabel  duration  ...  dimension definition caption  \\\n",
       "0              Education  PT11M51S  ...         2d         hd   False   \n",
       "1              Education   PT7M59S  ...         2d         hd   False   \n",
       "2              Education  PT17M11S  ...         2d         hd   False   \n",
       "3              Education   PT5M10S  ...         2d         hd   False   \n",
       "4              Education  PT14M32S  ...         2d         hd   False   \n",
       "...                  ...       ...  ...        ...        ...     ...   \n",
       "44256     People & Blogs   PT2M57S  ...         2d         hd   False   \n",
       "44257     People & Blogs   PT3M15S  ...         2d         hd   False   \n",
       "44258     People & Blogs   PT2M51S  ...         2d         hd   False   \n",
       "44259     People & Blogs   PT2M19S  ...         2d         hd   False   \n",
       "44260     People & Blogs    PT2M4S  ...         2d         hd   False   \n",
       "\n",
       "                                        thumbnail_maxres licensedContent  \\\n",
       "0                                                    NaN             1.0   \n",
       "1                                                    NaN             1.0   \n",
       "2                                                    NaN             1.0   \n",
       "3                                                    NaN             1.0   \n",
       "4                                                    NaN             1.0   \n",
       "...                                                  ...             ...   \n",
       "44256  https://i.ytimg.com/vi/RoUrdoU_2F4/maxresdefau...             NaN   \n",
       "44257  https://i.ytimg.com/vi/XNf7gBGEq28/maxresdefau...             NaN   \n",
       "44258  https://i.ytimg.com/vi/LIBLD8NM04Y/maxresdefau...             NaN   \n",
       "44259  https://i.ytimg.com/vi/_s3p4s9dUCU/maxresdefau...             NaN   \n",
       "44260  https://i.ytimg.com/vi/l2IlkEC0-hk/maxresdefau...             NaN   \n",
       "\n",
       "       viewCount  likeCount  dislikeCount  favoriteCount  commentCount  \n",
       "0            0.0       22.0           0.0            0.0           4.0  \n",
       "1         5027.0      396.0           0.0            0.0          83.0  \n",
       "2         4668.0      206.0           1.0            0.0          66.0  \n",
       "3         5375.0      472.0           3.0            0.0         114.0  \n",
       "4         5802.0      313.0           1.0            0.0         132.0  \n",
       "...          ...        ...           ...            ...           ...  \n",
       "44256      623.0        4.0           0.0            0.0           0.0  \n",
       "44257      142.0        0.0           0.0            0.0           0.0  \n",
       "44258      364.0        1.0           0.0            0.0           0.0  \n",
       "44259     1938.0       23.0           0.0            0.0           1.0  \n",
       "44260      173.0        1.0           0.0            0.0           0.0  \n",
       "\n",
       "[44261 rows x 21 columns]"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['channelId', 'channelTitle', 'videoId', 'publishedAt', 'publishedAtSQL',\n",
       "       'videoTitle', 'videoDescription', 'videoCategoryId',\n",
       "       'videoCategoryLabel', 'duration', 'durationSec', 'dimension',\n",
       "       'definition', 'caption', 'thumbnail_maxres', 'licensedContent',\n",
       "       'viewCount', 'likeCount', 'dislikeCount', 'favoriteCount',\n",
       "       'commentCount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"channelId\", \"channelTitle\", \"videoId\", \"thumbnail_maxres\",\"licensedContent\",\n",
    "                 \"publishedAt\",\"publishedAtSQL\",\"videoTitle\",\"videoDescription\",\"duration\", \"dimension\",\n",
    "                 \"favoriteCount\"],\n",
    "                 axis=1, inplace=True) #type in the columns that I would drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['videoCategoryId',\n",
       " 'videoCategoryLabel',\n",
       " 'durationSec',\n",
       " 'definition',\n",
       " 'caption',\n",
       " 'viewCount',\n",
       " 'likeCount',\n",
       " 'dislikeCount',\n",
       " 'commentCount']"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename column\n",
    "df1 = df.rename(columns={'videoCategoryId':'categoryid', 'videoCategoryLabel':'category','durationSec':'duration',\n",
    "                            'definition':'quality', 'caption':'caption', 'viewCount':'view','likeCount':'like',\n",
    "                            'dislikeCount':'dislike', 'commentCount':'comment'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['categoryid',\n",
       " 'category',\n",
       " 'duration',\n",
       " 'quality',\n",
       " 'caption',\n",
       " 'view',\n",
       " 'like',\n",
       " 'dislike',\n",
       " 'comment']"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df1.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['categoryid' 'duration' 'view' 'like' 'dislike' 'comment']\n",
      "       categoryid  duration    view   like  dislike  comment\n",
      "0            27.0     711.0     0.0   22.0      0.0      4.0\n",
      "1            27.0     479.0  5027.0  396.0      0.0     83.0\n",
      "2            27.0    1031.0  4668.0  206.0      1.0     66.0\n",
      "3            27.0     310.0  5375.0  472.0      3.0    114.0\n",
      "4            27.0     872.0  5802.0  313.0      1.0    132.0\n",
      "...           ...       ...     ...    ...      ...      ...\n",
      "44256        22.0     177.0   623.0    4.0      0.0      0.0\n",
      "44257        22.0     195.0   142.0    0.0      0.0      0.0\n",
      "44258        22.0     171.0   364.0    1.0      0.0      0.0\n",
      "44259        22.0     139.0  1938.0   23.0      0.0      1.0\n",
      "44260        22.0     124.0   173.0    1.0      0.0      0.0\n",
      "\n",
      "[44261 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# select numeric columns\n",
    "df1_numeric = df1.select_dtypes(include='number') #creates a dataframe df_numeric using select_dtypes()\n",
    "numeric_cols = df_numeric.columns.values #just retrieve the names of the columns from df_numeric\n",
    "print(numeric_cols)\n",
    "print(df1_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dealing with Missing Data!\n",
    "#Technique #2- Creating Missing Data Heatmap\n",
    "cols = df.columns[:30] # first 30 columns\n",
    "sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(palette='colorblind')) \n",
    "plt.show()\n",
    "#or you can also define a palette on your own by giving a hexcolor code as below. \n",
    "#Below we are defining two colors yellow and blue for missing and non-missing data.\n",
    "colors = ['#000099', '#ffff00'] # specify the colours - yellow is missing. blue is not missing.\n",
    "#now we will use this palette in heatmap() of sns\n",
    "sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(colors))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "created missing indicator for: categoryid\n",
      "created missing indicator for: category\n",
      "created missing indicator for: duration\n",
      "created missing indicator for: quality\n",
      "created missing indicator for: caption\n",
      "created missing indicator for: view\n",
      "created missing indicator for: like\n",
      "created missing indicator for: dislike\n",
      "created missing indicator for: comment\n"
     ]
    }
   ],
   "source": [
    "#Dealing with Missing Data!\n",
    "#Technique 2: Creating Missing Data Distribution Plot\n",
    "# first create missing indicator for columns with missing data\n",
    "for col in df1.columns:\n",
    "    missing = df1[col].isnull() #missing will create a series with all the columns with null values\n",
    "    num_missing = missing.sum() #this will sum up all the rows that have nulls   \n",
    "    #now we want to store the columns which have missing data\n",
    "    if num_missing > 0:  #this condition is still in the for loop.\n",
    "        print('created missing indicator for: {}'.format(col)) #prints all columns which have missing data\n",
    "        df1['{}_ismissing'.format(col)] = missing \n",
    "        \n",
    "        #this will take values from missing series one by one and create a column for them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "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>categoryid</th>\n",
       "      <th>category</th>\n",
       "      <th>duration</th>\n",
       "      <th>quality</th>\n",
       "      <th>caption</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>categoryid_ismissing</th>\n",
       "      <th>category_ismissing</th>\n",
       "      <th>duration_ismissing</th>\n",
       "      <th>quality_ismissing</th>\n",
       "      <th>caption_ismissing</th>\n",
       "      <th>view_ismissing</th>\n",
       "      <th>like_ismissing</th>\n",
       "      <th>dislike_ismissing</th>\n",
       "      <th>comment_ismissing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>711.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>479.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>310.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>872.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44256</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>177.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44257</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>195.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44258</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>171.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44259</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>139.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44260</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>124.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>44261 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       categoryid        category  duration quality caption    view   like  \\\n",
       "0            27.0       Education     711.0      hd   False     0.0   22.0   \n",
       "1            27.0       Education     479.0      hd   False  5027.0  396.0   \n",
       "2            27.0       Education    1031.0      hd   False  4668.0  206.0   \n",
       "3            27.0       Education     310.0      hd   False  5375.0  472.0   \n",
       "4            27.0       Education     872.0      hd   False  5802.0  313.0   \n",
       "...           ...             ...       ...     ...     ...     ...    ...   \n",
       "44256        22.0  People & Blogs     177.0      hd   False   623.0    4.0   \n",
       "44257        22.0  People & Blogs     195.0      hd   False   142.0    0.0   \n",
       "44258        22.0  People & Blogs     171.0      hd   False   364.0    1.0   \n",
       "44259        22.0  People & Blogs     139.0      hd   False  1938.0   23.0   \n",
       "44260        22.0  People & Blogs     124.0      hd   False   173.0    1.0   \n",
       "\n",
       "       dislike  comment  categoryid_ismissing  category_ismissing  \\\n",
       "0          0.0      4.0                 False               False   \n",
       "1          0.0     83.0                 False               False   \n",
       "2          1.0     66.0                 False               False   \n",
       "3          3.0    114.0                 False               False   \n",
       "4          1.0    132.0                 False               False   \n",
       "...        ...      ...                   ...                 ...   \n",
       "44256      0.0      0.0                 False               False   \n",
       "44257      0.0      0.0                 False               False   \n",
       "44258      0.0      0.0                 False               False   \n",
       "44259      0.0      1.0                 False               False   \n",
       "44260      0.0      0.0                 False               False   \n",
       "\n",
       "       duration_ismissing  quality_ismissing  caption_ismissing  \\\n",
       "0                   False              False              False   \n",
       "1                   False              False              False   \n",
       "2                   False              False              False   \n",
       "3                   False              False              False   \n",
       "4                   False              False              False   \n",
       "...                   ...                ...                ...   \n",
       "44256               False              False              False   \n",
       "44257               False              False              False   \n",
       "44258               False              False              False   \n",
       "44259               False              False              False   \n",
       "44260               False              False              False   \n",
       "\n",
       "       view_ismissing  like_ismissing  dislike_ismissing  comment_ismissing  \n",
       "0               False           False              False              False  \n",
       "1               False           False              False              False  \n",
       "2               False           False              False              False  \n",
       "3               False           False              False              False  \n",
       "4               False           False              False              False  \n",
       "...               ...             ...                ...                ...  \n",
       "44256           False           False              False              False  \n",
       "44257           False           False              False              False  \n",
       "44258           False           False              False              False  \n",
       "44259           False           False              False              False  \n",
       "44260           False           False              False              False  \n",
       "\n",
       "[44261 rows x 18 columns]"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='index'>"
      ]
     },
     "execution_count": 384,
     "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": [
    "# then based on the indicator, plot the histogram of missing values\n",
    "ismissing_cols = [col for col in df1.columns if 'ismissing' in col] #searching for ismissing in col using list \n",
    "#comprehension and storing it in ismissing_cols\n",
    "df1['num_missing'] = df1[ismissing_cols].sum(axis=1) #create a new column 'num_missing' \n",
    "\n",
    "#using displot() from seaborn to plot a distributional summary\n",
    "sns.displot(data=df1,x='num_missing',kde=True)\n",
    "plt.show()\n",
    "\n",
    "#You can also plot the histogram using below command. try it!\n",
    "df1['num_missing'].value_counts().reset_index().sort_values(by='index').plot.bar(x='index', y='num_missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "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>categoryid</th>\n",
       "      <th>category</th>\n",
       "      <th>duration</th>\n",
       "      <th>quality</th>\n",
       "      <th>caption</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>categoryid_ismissing</th>\n",
       "      <th>category_ismissing</th>\n",
       "      <th>duration_ismissing</th>\n",
       "      <th>quality_ismissing</th>\n",
       "      <th>caption_ismissing</th>\n",
       "      <th>view_ismissing</th>\n",
       "      <th>like_ismissing</th>\n",
       "      <th>dislike_ismissing</th>\n",
       "      <th>comment_ismissing</th>\n",
       "      <th>num_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>711.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>479.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>310.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>872.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>362.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>7055.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>218.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>6283.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>710.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>9407.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>111.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>292.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>9011.0</td>\n",
       "      <td>713.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   categoryid   category  duration quality caption    view   like  dislike  \\\n",
       "0        27.0  Education     711.0      hd   False     0.0   22.0      0.0   \n",
       "1        27.0  Education     479.0      hd   False  5027.0  396.0      0.0   \n",
       "2        27.0  Education    1031.0      hd   False  4668.0  206.0      1.0   \n",
       "3        27.0  Education     310.0      hd   False  5375.0  472.0      3.0   \n",
       "4        27.0  Education     872.0      hd   False  5802.0  313.0      1.0   \n",
       "5        27.0  Education     362.0      hd   False  7055.0  544.0      2.0   \n",
       "6        27.0  Education     218.0      hd   False  6283.0  541.0      3.0   \n",
       "7        27.0  Education     710.0      hd   False  9407.0  443.0      3.0   \n",
       "8        27.0  Education     111.0      hd   False  6270.0  336.0      1.0   \n",
       "9        27.0  Education     292.0      hd   False  9011.0  713.0      7.0   \n",
       "\n",
       "   comment  categoryid_ismissing  category_ismissing  duration_ismissing  \\\n",
       "0      4.0                 False               False               False   \n",
       "1     83.0                 False               False               False   \n",
       "2     66.0                 False               False               False   \n",
       "3    114.0                 False               False               False   \n",
       "4    132.0                 False               False               False   \n",
       "5     87.0                 False               False               False   \n",
       "6    109.0                 False               False               False   \n",
       "7     88.0                 False               False               False   \n",
       "8     66.0                 False               False               False   \n",
       "9    117.0                 False               False               False   \n",
       "\n",
       "   quality_ismissing  caption_ismissing  view_ismissing  like_ismissing  \\\n",
       "0              False              False           False           False   \n",
       "1              False              False           False           False   \n",
       "2              False              False           False           False   \n",
       "3              False              False           False           False   \n",
       "4              False              False           False           False   \n",
       "5              False              False           False           False   \n",
       "6              False              False           False           False   \n",
       "7              False              False           False           False   \n",
       "8              False              False           False           False   \n",
       "9              False              False           False           False   \n",
       "\n",
       "   dislike_ismissing  comment_ismissing  num_missing  \n",
       "0              False              False            0  \n",
       "1              False              False            0  \n",
       "2              False              False            0  \n",
       "3              False              False            0  \n",
       "4              False              False            0  \n",
       "5              False              False            0  \n",
       "6              False              False            0  \n",
       "7              False              False            0  \n",
       "8              False              False            0  \n",
       "9              False              False            0  "
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10) #you can see the newly created column num_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[col].dtype #check the contents of col. It is a boolean datatype and holds true/false values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categoryid - 0%\n",
      "category - 0%\n",
      "duration - 0%\n",
      "quality - 0%\n",
      "caption - 0%\n",
      "view - 0%\n",
      "like - 2%\n",
      "dislike - 2%\n",
      "comment - 21%\n",
      "categoryid_ismissing - 0%\n",
      "category_ismissing - 0%\n",
      "duration_ismissing - 0%\n",
      "quality_ismissing - 0%\n",
      "caption_ismissing - 0%\n",
      "view_ismissing - 0%\n",
      "like_ismissing - 0%\n",
      "dislike_ismissing - 0%\n",
      "comment_ismissing - 0%\n",
      "num_missing - 0%\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "#Technique 3 to check missing data- Calculate percent of missing data in the columns\n",
    "\n",
    "for col in df1.columns:\n",
    "    pct_missing = df1[col].isnull().mean() #calculating % of missing values. calling numpy (np)\n",
    "    print('{} - {}%'.format(col, round(pct_missing*100))) #lookup format function\n",
    "print(pct_missing)\n",
    "\n",
    "#looking at the percent values you can decide which columns to delete and which to keep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0\n"
     ]
    }
   ],
   "source": [
    "#Solution 3: Impute the missing data\n",
    "# replace missing values with the median.\n",
    "med = df1['comment'].median() #calculate the median first\n",
    "print(med)\n",
    "df1['comment'] = df1['comment'].fillna(med) #create a new column with the calculated median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_missing = df1[df1['num_missing']>1].index #retrieve the index of column missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.drop(ind_missing, axis=0) #axis=0 indicates that we want to delete rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>categoryid</th>\n",
       "      <th>category</th>\n",
       "      <th>duration</th>\n",
       "      <th>quality</th>\n",
       "      <th>caption</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>categoryid_ismissing</th>\n",
       "      <th>category_ismissing</th>\n",
       "      <th>duration_ismissing</th>\n",
       "      <th>quality_ismissing</th>\n",
       "      <th>caption_ismissing</th>\n",
       "      <th>view_ismissing</th>\n",
       "      <th>like_ismissing</th>\n",
       "      <th>dislike_ismissing</th>\n",
       "      <th>comment_ismissing</th>\n",
       "      <th>num_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>711.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>479.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>310.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>872.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44256</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>177.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44257</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>195.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44258</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>171.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44259</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>139.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44260</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>124.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43401 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       categoryid        category  duration quality caption    view   like  \\\n",
       "0            27.0       Education     711.0      hd   False     0.0   22.0   \n",
       "1            27.0       Education     479.0      hd   False  5027.0  396.0   \n",
       "2            27.0       Education    1031.0      hd   False  4668.0  206.0   \n",
       "3            27.0       Education     310.0      hd   False  5375.0  472.0   \n",
       "4            27.0       Education     872.0      hd   False  5802.0  313.0   \n",
       "...           ...             ...       ...     ...     ...     ...    ...   \n",
       "44256        22.0  People & Blogs     177.0      hd   False   623.0    4.0   \n",
       "44257        22.0  People & Blogs     195.0      hd   False   142.0    0.0   \n",
       "44258        22.0  People & Blogs     171.0      hd   False   364.0    1.0   \n",
       "44259        22.0  People & Blogs     139.0      hd   False  1938.0   23.0   \n",
       "44260        22.0  People & Blogs     124.0      hd   False   173.0    1.0   \n",
       "\n",
       "       dislike  comment  categoryid_ismissing  category_ismissing  \\\n",
       "0          0.0      4.0                 False               False   \n",
       "1          0.0     83.0                 False               False   \n",
       "2          1.0     66.0                 False               False   \n",
       "3          3.0    114.0                 False               False   \n",
       "4          1.0    132.0                 False               False   \n",
       "...        ...      ...                   ...                 ...   \n",
       "44256      0.0      0.0                 False               False   \n",
       "44257      0.0      0.0                 False               False   \n",
       "44258      0.0      0.0                 False               False   \n",
       "44259      0.0      1.0                 False               False   \n",
       "44260      0.0      0.0                 False               False   \n",
       "\n",
       "       duration_ismissing  quality_ismissing  caption_ismissing  \\\n",
       "0                   False              False              False   \n",
       "1                   False              False              False   \n",
       "2                   False              False              False   \n",
       "3                   False              False              False   \n",
       "4                   False              False              False   \n",
       "...                   ...                ...                ...   \n",
       "44256               False              False              False   \n",
       "44257               False              False              False   \n",
       "44258               False              False              False   \n",
       "44259               False              False              False   \n",
       "44260               False              False              False   \n",
       "\n",
       "       view_ismissing  like_ismissing  dislike_ismissing  comment_ismissing  \\\n",
       "0               False           False              False              False   \n",
       "1               False           False              False              False   \n",
       "2               False           False              False              False   \n",
       "3               False           False              False              False   \n",
       "4               False           False              False              False   \n",
       "...               ...             ...                ...                ...   \n",
       "44256           False           False              False              False   \n",
       "44257           False           False              False              False   \n",
       "44258           False           False              False              False   \n",
       "44259           False           False              False              False   \n",
       "44260           False           False              False              False   \n",
       "\n",
       "       num_missing  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "44256            0  \n",
       "44257            0  \n",
       "44258            0  \n",
       "44259            0  \n",
       "44260            0  \n",
       "\n",
       "[43401 rows x 19 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop categories that we do not use in our analysis (only use categoryid 27.0,28.0,26.0,22.0)\n",
    "df2.drop(df2.index[(df2['categoryid'] != 27.0) & (df2['categoryid'] != 28.0) &\n",
    "                (df2['categoryid'] != 26.0) & (df2['categoryid'] != 22.0)], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.drop(df2.index[(df2['duration']==0)], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "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>categoryid</th>\n",
       "      <th>category</th>\n",
       "      <th>duration</th>\n",
       "      <th>quality</th>\n",
       "      <th>caption</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>categoryid_ismissing</th>\n",
       "      <th>category_ismissing</th>\n",
       "      <th>duration_ismissing</th>\n",
       "      <th>quality_ismissing</th>\n",
       "      <th>caption_ismissing</th>\n",
       "      <th>view_ismissing</th>\n",
       "      <th>like_ismissing</th>\n",
       "      <th>dislike_ismissing</th>\n",
       "      <th>comment_ismissing</th>\n",
       "      <th>num_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>711.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>479.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>310.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>872.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44256</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>177.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44257</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>195.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44258</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>171.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44259</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>139.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44260</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>124.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>False</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43229 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       categoryid        category  duration quality caption    view   like  \\\n",
       "0            27.0       Education     711.0      hd   False     0.0   22.0   \n",
       "1            27.0       Education     479.0      hd   False  5027.0  396.0   \n",
       "2            27.0       Education    1031.0      hd   False  4668.0  206.0   \n",
       "3            27.0       Education     310.0      hd   False  5375.0  472.0   \n",
       "4            27.0       Education     872.0      hd   False  5802.0  313.0   \n",
       "...           ...             ...       ...     ...     ...     ...    ...   \n",
       "44256        22.0  People & Blogs     177.0      hd   False   623.0    4.0   \n",
       "44257        22.0  People & Blogs     195.0      hd   False   142.0    0.0   \n",
       "44258        22.0  People & Blogs     171.0      hd   False   364.0    1.0   \n",
       "44259        22.0  People & Blogs     139.0      hd   False  1938.0   23.0   \n",
       "44260        22.0  People & Blogs     124.0      hd   False   173.0    1.0   \n",
       "\n",
       "       dislike  comment  categoryid_ismissing  category_ismissing  \\\n",
       "0          0.0      4.0                 False               False   \n",
       "1          0.0     83.0                 False               False   \n",
       "2          1.0     66.0                 False               False   \n",
       "3          3.0    114.0                 False               False   \n",
       "4          1.0    132.0                 False               False   \n",
       "...        ...      ...                   ...                 ...   \n",
       "44256      0.0      0.0                 False               False   \n",
       "44257      0.0      0.0                 False               False   \n",
       "44258      0.0      0.0                 False               False   \n",
       "44259      0.0      1.0                 False               False   \n",
       "44260      0.0      0.0                 False               False   \n",
       "\n",
       "       duration_ismissing  quality_ismissing  caption_ismissing  \\\n",
       "0                   False              False              False   \n",
       "1                   False              False              False   \n",
       "2                   False              False              False   \n",
       "3                   False              False              False   \n",
       "4                   False              False              False   \n",
       "...                   ...                ...                ...   \n",
       "44256               False              False              False   \n",
       "44257               False              False              False   \n",
       "44258               False              False              False   \n",
       "44259               False              False              False   \n",
       "44260               False              False              False   \n",
       "\n",
       "       view_ismissing  like_ismissing  dislike_ismissing  comment_ismissing  \\\n",
       "0               False           False              False              False   \n",
       "1               False           False              False              False   \n",
       "2               False           False              False              False   \n",
       "3               False           False              False              False   \n",
       "4               False           False              False              False   \n",
       "...               ...             ...                ...                ...   \n",
       "44256           False           False              False              False   \n",
       "44257           False           False              False              False   \n",
       "44258           False           False              False              False   \n",
       "44259           False           False              False              False   \n",
       "44260           False           False              False              False   \n",
       "\n",
       "       num_missing  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "44256            0  \n",
       "44257            0  \n",
       "44258            0  \n",
       "44259            0  \n",
       "44260            0  \n",
       "\n",
       "[43229 rows x 19 columns]"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0    19900\n",
       "28.0    19445\n",
       "26.0     2043\n",
       "22.0     1847\n",
       "Name: categoryid, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['categoryid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check for the missing values after cleaning\n",
    "cols = df2.columns[:30] # first 30 columns\n",
    "sns.heatmap(df2[cols].isnull(), cmap=sns.color_palette(palette='colorblind')) \n",
    "plt.show()\n",
    "#or you can also define a palette on your own by giving a hexcolor code as below. \n",
    "#Below we are defining two colors yellow and blue for missing and non-missing data.\n",
    "colors = ['#000099', '#ffff00'] # specify the colours - yellow is missing. blue is not missing.\n",
    "#now we will use this palette in heatmap() of sns\n",
    "sns.heatmap(df2[cols].isnull(), cmap=sns.color_palette(colors))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categoryid - 0%\n",
      "category - 0%\n",
      "duration - 0%\n",
      "quality - 0%\n",
      "caption - 0%\n",
      "view - 0%\n",
      "like - 0%\n",
      "dislike - 0%\n",
      "comment - 0%\n",
      "categoryid_ismissing - 0%\n",
      "category_ismissing - 0%\n",
      "duration_ismissing - 0%\n",
      "quality_ismissing - 0%\n",
      "caption_ismissing - 0%\n",
      "view_ismissing - 0%\n",
      "like_ismissing - 0%\n",
      "dislike_ismissing - 0%\n",
      "comment_ismissing - 0%\n",
      "num_missing - 0%\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "#Check to make sure we have drop all missing observations\n",
    "for col in df2.columns:\n",
    "    pct_missing = df2[col].isnull().mean() #calculating % of missing values. calling numpy (np)\n",
    "    print('{} - {}%'.format(col, round(pct_missing*100))) #lookup format function\n",
    "print(pct_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education               19900\n",
       "Science & Technology    19445\n",
       "Howto & Style            2043\n",
       "People & Blogs           1847\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>categoryid</th>\n",
       "      <th>category</th>\n",
       "      <th>duration</th>\n",
       "      <th>quality</th>\n",
       "      <th>caption</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>categoryid_ismissing</th>\n",
       "      <th>category_ismissing</th>\n",
       "      <th>duration_ismissing</th>\n",
       "      <th>quality_ismissing</th>\n",
       "      <th>caption_ismissing</th>\n",
       "      <th>view_ismissing</th>\n",
       "      <th>like_ismissing</th>\n",
       "      <th>dislike_ismissing</th>\n",
       "      <th>comment_ismissing</th>\n",
       "      <th>num_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [categoryid, category, duration, quality, caption, view, like, dislike, comment, categoryid_ismissing, category_ismissing, duration_ismissing, quality_ismissing, caption_ismissing, view_ismissing, like_ismissing, dislike_ismissing, comment_ismissing, num_missing]\n",
       "Index: []"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for duplicates\n",
    "df2[df2.index.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    43229.000000\n",
       "mean       800.184737\n",
       "std        809.089417\n",
       "min          1.000000\n",
       "25%        247.000000\n",
       "50%        468.000000\n",
       "75%       1042.000000\n",
       "max       3599.000000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.340000e+04\n",
       "mean     3.290070e+04\n",
       "std      2.309977e+05\n",
       "min      0.000000e+00\n",
       "25%      3.080000e+02\n",
       "50%      2.336000e+03\n",
       "75%      1.321050e+04\n",
       "max      1.778718e+07\n",
       "Name: view, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['view'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "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": [
    "#Check the Outliers using Histogram\n",
    "df2.view.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 283,
     "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": [
    "df2.duration.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 284,
     "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": [
    "df2.categoryid.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 286,
     "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": [
    "df2.like.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 288,
     "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": [
    "df2.dislike.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 289,
     "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": [
    "df2.comment.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar chart -  distribution of a categorical variable\n",
    "df2['categoryid'].value_counts().plot.bar() #value_counts counts frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplots - distribution of numerical variables\n",
    "sns.boxplot(data=df2,x='comment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df2,x='like')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df2,x='duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Heatmap\n",
    "plt.figure(figsize = (10,8))\n",
    "\n",
    "#Let's verify the correlation of each value\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution\n",
    "plt.figure(figsize = (12,6))\n",
    "\n",
    "plt.subplot(221)\n",
    "g1 = sns.distplot(df2['view'])\n",
    "g1.set_title(\"VIEWS DISTRIBUTION\", fontsize=16)\n",
    "\n",
    "plt.subplot(222)\n",
    "g2 = sns.distplot(df2['like'],color='green')\n",
    "g2.set_title('LIKES DISTRIBUTION', fontsize=16)\n",
    "\n",
    "plt.subplot(223)\n",
    "g3 = sns.distplot(df2['comment'], color='r')\n",
    "g3.set_title(\"COMMENT DISTRIBUTION\", fontsize=16)\n",
    "\n",
    "plt.subplot(224)\n",
    "g4 = sns.distplot(df2['duration'])\n",
    "g4.set_title(\"DURATION DISTRIBUTION\", fontsize=16)\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.2, hspace = 0.4,top = 0.9)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "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>categoryid</th>\n",
       "      <th>category</th>\n",
       "      <th>duration</th>\n",
       "      <th>quality</th>\n",
       "      <th>caption</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>categoryid_ismissing</th>\n",
       "      <th>...</th>\n",
       "      <th>ln_like</th>\n",
       "      <th>ln_dislike</th>\n",
       "      <th>ln_comment</th>\n",
       "      <th>inverse_like</th>\n",
       "      <th>inverse_dislike</th>\n",
       "      <th>sqrt_duration</th>\n",
       "      <th>view_log</th>\n",
       "      <th>like_log</th>\n",
       "      <th>dislike_log</th>\n",
       "      <th>comment_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>711.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.664583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>479.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>5.981414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.418841</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.886069</td>\n",
       "      <td>8.522579</td>\n",
       "      <td>5.981414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.418841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>5.327876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.109189</td>\n",
       "      <td>8.448486</td>\n",
       "      <td>5.327876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.189655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>310.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>6.156979</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4.736198</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>17.606817</td>\n",
       "      <td>8.589514</td>\n",
       "      <td>6.156979</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4.736198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>872.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>5.746203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>0.003195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.529646</td>\n",
       "      <td>8.665958</td>\n",
       "      <td>5.746203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.882802</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>44256</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>177.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.304135</td>\n",
       "      <td>6.434547</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44257</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>195.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.964240</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44258</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>171.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.076697</td>\n",
       "      <td>5.897154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44259</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>139.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>3.135494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.789826</td>\n",
       "      <td>7.569412</td>\n",
       "      <td>3.135494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44260</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>124.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.135529</td>\n",
       "      <td>5.153292</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43229 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       categoryid        category  duration quality caption    view   like  \\\n",
       "0            27.0       Education     711.0      hd       0     0.0   22.0   \n",
       "1            27.0       Education     479.0      hd       0  5027.0  396.0   \n",
       "2            27.0       Education    1031.0      hd       0  4668.0  206.0   \n",
       "3            27.0       Education     310.0      hd       0  5375.0  472.0   \n",
       "4            27.0       Education     872.0      hd       0  5802.0  313.0   \n",
       "...           ...             ...       ...     ...     ...     ...    ...   \n",
       "44256        22.0  People & Blogs     177.0      hd       0   623.0    4.0   \n",
       "44257        22.0  People & Blogs     195.0      hd       0   142.0    0.0   \n",
       "44258        22.0  People & Blogs     171.0      hd       0   364.0    1.0   \n",
       "44259        22.0  People & Blogs     139.0      hd       0  1938.0   23.0   \n",
       "44260        22.0  People & Blogs     124.0      hd       0   173.0    1.0   \n",
       "\n",
       "       dislike  comment  categoryid_ismissing  ...   ln_like  ln_dislike  \\\n",
       "0          0.0      4.0                 False  ...  3.091042    0.000000   \n",
       "1          0.0     83.0                 False  ...  5.981414    0.000000   \n",
       "2          1.0     66.0                 False  ...  5.327876    0.000000   \n",
       "3          3.0    114.0                 False  ...  6.156979    1.098612   \n",
       "4          1.0    132.0                 False  ...  5.746203    0.000000   \n",
       "...        ...      ...                   ...  ...       ...         ...   \n",
       "44256      0.0      0.0                 False  ...  1.386294    0.000000   \n",
       "44257      0.0      0.0                 False  ...  0.000000    0.000000   \n",
       "44258      0.0      0.0                 False  ...  0.000000    0.000000   \n",
       "44259      0.0      1.0                 False  ...  3.135494    0.000000   \n",
       "44260      0.0      0.0                 False  ...  0.000000    0.000000   \n",
       "\n",
       "       ln_comment  inverse_like  inverse_dislike  sqrt_duration  view_log  \\\n",
       "0        1.386294      0.045455         0.000000      26.664583  0.000000   \n",
       "1        4.418841      0.002525         0.000000      21.886069  8.522579   \n",
       "2        4.189655      0.004854         1.000000      32.109189  8.448486   \n",
       "3        4.736198      0.002119         0.333333      17.606817  8.589514   \n",
       "4        4.882802      0.003195         1.000000      29.529646  8.665958   \n",
       "...           ...           ...              ...            ...       ...   \n",
       "44256    0.000000      0.250000         0.000000      13.304135  6.434547   \n",
       "44257    0.000000      0.000000         0.000000      13.964240  4.955827   \n",
       "44258    0.000000      1.000000         0.000000      13.076697  5.897154   \n",
       "44259    0.000000      0.043478         0.000000      11.789826  7.569412   \n",
       "44260    0.000000      1.000000         0.000000      11.135529  5.153292   \n",
       "\n",
       "       like_log  dislike_log  comment_log  \n",
       "0      3.091042     0.000000     1.386294  \n",
       "1      5.981414     0.000000     4.418841  \n",
       "2      5.327876     0.000000     4.189655  \n",
       "3      6.156979     1.098612     4.736198  \n",
       "4      5.746203     0.000000     4.882802  \n",
       "...         ...          ...          ...  \n",
       "44256  1.386294     0.000000     0.000000  \n",
       "44257  0.000000     0.000000     0.000000  \n",
       "44258  0.000000     0.000000     0.000000  \n",
       "44259  3.135494     0.000000     0.000000  \n",
       "44260  0.000000     0.000000     0.000000  \n",
       "\n",
       "[43229 rows x 31 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since all variables are not normally distributed, we will apply transformation to achieve normal distribtution\n",
    "#Calculate Natural Log for each variables\n",
    "df2['ln_view']=np.log(df2['view'])\n",
    "df2['ln_duration']=np.log(df2['duration'])\n",
    "df2['ln_like']=np.log(df2['like'])\n",
    "df2['ln_dislike']=np.log(df2['dislike'])\n",
    "df2['ln_comment']=np.log(df2['comment'])\n",
    "\n",
    "df2 = df2.replace([np.inf, -np.inf, -0], 0)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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acV05rva2rwgjrsDjWQaHJpE5Djf2/wstbJsDbBaRp3CjQhXjksjLcMnpH0INX9hO9+D6u/9MRJ5ppUU58DEl4SaP+SnuWPZgQLnbcd1EPhKR3+LmECjADU6QjZs8psNU9S0R+TnwKxEpxDXm7cV1U7wF9wUknJMt/4mbCOmsSMTVqWgcDLZtl/AuuCRUaX4A/GsJPUC9NnNZFmIf87113w6xbo237pow4721hft+OaDcsbhKtwTXrWMTbqzXQc3sNwV3Qs+HuNkZ63BJ+cvAl4DEVuI6JSiWatyZ2a/gZrFKCbFNEYdPInM27teE1bgKsAZ30PoH0LeV56Otk7f0wn2x+nfAMiX0JDKBl1LcgfDyEPtMwFXIn3qPvxL3c+9XW3jemo3RWz+bgElkApYPxPUbL/Je3xLcSAbHhfsc4b5olWKTyNglji8cqoObrW9Dvcdb+OxMBPbgfsEb0MznPPAy1duuzfVT0P0WtXAfn/PKJOHq4fm4pLjCq0NuClXHedsUejGs9eqdKly3tbuASWHE9WBAHI3eY1sJ3E/QRFte+abnq2kSmRRcwvsGLrGuxdWtHwPX403I1szz0Z7JW6731l0S9P5o6RhdjzsGPg6MCbHPwbgGrm24Y99uXGPYhBaet5ZiLKT5Y885uJFp9nrvobW4Vu2e4T5HuJMzlW42iUzTzH7GGGOMMcaYDrI+18YYY4wxxkSIJdfGGGOMMcZEiCXXxhhjjDHGRIgl18YYY4wxxkSIJdfGGGOMMcZESJca57p3795aWFjodxjGGNNmCxcu3K2q+X7HEUtWZxtjOquW6uwulVwXFhayYMECv8Mwxpg2E5FNrZfqWqzONsZ0Vi3V2dYtxBhjjDHGmAix5NoYY4wxxpgIseTaGGOMMcaYCLHk2hhjjDHGmAix5NoYY4wxxpgIiepoISJyDnA7kAjcp6q3Ba0fA/wLmAL8VFX/FLCuCNgPNAD1qjo1mrEa05pZczcfseyKGYN9iMQY0xH3Lrz3iGXXH3O9D5EYY7qiqCXXIpII3AGcCRQD80XkRVVdEVCsFPg2cHEzuzlVVXdHK0ZjjDHGGGMiKZrdQqYD61R1g6rWAk8AMwMLqOouVZ0P1EUxDmOMMcYYY2Iimsl1AbAl4HaxtyxcCrwhIgtFpNnf60TkehFZICILSkpK2hmqMcYYY4wxHRfNPtcSYpm2YfvjVXWbiPQB/isiq1T1vSN2qHovcC/A1KlT27J/Y4Dw+lKrKpU19VTXN5KenEh6SmKswjPGd2GcP5MDPAoMxh1X/qSq/4p5oMYYEweimVwXA4MCbg8EtoW7sapu8653icjzuG4mRyTXxkSaqrKltJLFW/axfFsZK7aVs2JbOXsqag+W6ZmZwpTBeVwwsT/Zack+RmtMdIV5/sw3gRWqeqGI5AOrReQxr0ugMcZ0K9FMrucDI0VkKLAVuAy4IpwNRSQTSFDV/d7/ZwG/ilqkpttTVdaXVLBwUylFeyr56QvLAEhOFEb17cHpY/twoKaB9OREKmrqWbtrP2+u3MnZ/+89/vi5iZwwsrfPj8CYqDl4/gyAiDSdPxOYXCvQQ0QEyMKdrF4f60CNMSYeRC25VtV6EbkJeB33U+IDqrpcRG7w1t8tIv2ABUA20CgiNwPjgN7A866eJgmYpaqvRStW073tPlDDc4uKKdpTSUZKIsPzs/j81IEcMySPkX16kJLkTk0I7D5y0qh8tpRW8t+VO7nmX/O47bMT+PzUQc3dhTGdWajzZ2YElfkH8CLu18kewBdVtTE24RljTHyJ6jjXqvoK8ErQsrsD/t+B6y4SrByYGM3YjAGYvXoXf397LYkJwsxJA5gyOI/kxISwxq8e1DODF755PDc8spAfPPMJiQnCZ6eEejsb06mFc/7M2cAS4DRgOO48mfdVtfyInbkT1K8HGDzYxok3xnQ9UU2ujYlnry3bwU2zFtGnRypXHVdITvqhvtOhTnIMJSs1ifuvncq1D8znh898Qt/sNI4fYV1ETJcSzvkzXwZuU1UF1onIRmAMMC94Z3YSujGmq7Ppz023tHBTKd95YjETBuZw3YnDDkus22LW3M08u3ArZ47rS8/MFK5/eAE7y6sjHK0xvjp4/oyIpODOn3kxqMxm4HQAEekLjAY2xDRKY4yJE5Zcm25nZ3k1X39kIf1z0rj/mmmkJXd8WL205EQunz6Y2oZGbn5iCY2N1iBnugZVrQeazp9ZCTzVdP5M0zk0wP8BnxGRT4G3gB/Z7LrGmO7KuoWYbqW+oZFvPb6YipoGnrj+WHpmpkRs332z07jg6AE8v3grs+Zt5spjh0Rs38b4KYzzZ7bhRnUyxphuz1quTbdy06zFzNtYygVH92fexr1h960O19QheRw/ohe3vbqKHWXWPcQYY4zpbiy5Nt3GhpIDvLlyJ+P6ZzNpUG5U7kNE+O0lE6htaOSPr6+Oyn0YY4wxJn5ZtxDTLTQ2Krc8+ylJicJFkwbgjaEeFR+u28OxQ3vy3KJi+uekMSA3Payh/YwxxhjT+VnLtekWHpu7iXlFpZw/YUBMpis/eVQf0lMSeWXZdtzoZMYYY4zpDiy5Nl3e1n1V3PbqKk4c2Zspg3Njcp/pKYmcNqYPG0oqWLNzf0zu0xhjjDH+s+TadGmqyk+e+xQFfnvJhKh2Bwk2fWhPemWm8OqyHdQ32EzQxhhjTHdgybXp0p5fvJV315Two3PGMKhnRkzvOykhgbPH92PX/hqeX7w1pvdtjDHGGH9Ycm26rJL9Nfzq5RVMHZLHVT6NOT1+QDYFuen89c211NQ3+BKDMcYYY2LHkmvTZX35X/M4UF3PiSPzeWL+loiPaR0OEeGscX3Zuq+KJ+dvifn9G2OMMSa2LLk2XdJry7azbFs5p4/pQ36PVF9jGdEnixlDe/K3t9ZRWVvvayzGGGOMiS5Lrk2Xs6+ylv/993IG5KRxwsh8v8NBRPjB2aPZfaCGhz7a5Hc4xhhjjIkiS65Nl6Kq/Pi5T9lXWctnpwwkMSF2o4O0ZGphT04dnc/d766nrKrO73CMMcYYEyWWXJsu5emFxby6bAffP2s0A3LT/Q7nMP9z1mjKquq47/0NfodijDHGmCix5Np0GZv2VPDLF5dz7LCeXHfiML/DOcysuZv5pLiMCQU53PPuBu59zxJsY4wxpiuy5Np0CTX1DXzniSUkJgh/+cKkuOkOEuyMsX2pb2zkvyt2+B2KMcYYY6LAkmvT6akqP31+GUu27OP3lx4dd91BAuX3SOUzw3uzoGgvnxaX+R2OMcYYYyLMkmvT6f31zbU8s7CY08b0YW9lHbPmbvZlTOtwnTamDxmpSfzixWWoqt/hGGOMMSaCLLk2nZaq8tc313D7W2s5ZnAep43p43dIYUlLTuSc8X1ZtHkfLyyxadGNMcaYriTJ7wCMaY/91XX87wvLeGHJNj53zEAmDcolQeKzn3UokwfnsW7XAX73yirOGNuXHmnJfodkjDHGmAiw5NrEjaauHA2NSoK4yVeumDH4sDLVdQ385LlPeXPlTvZX13PmuL5MHpSLdKLEGiBBhF/NPIqL7/yQP72+ml/OPMrvkIwxxhgTAZZcm7jw3poSHp2zifUlB6ipbyQxQchOS+L5xcX0y0knLSmBHeXVLNq0l4raBgbkpvGlGUMY1DPD79Dbbfm2co4d2ouHP95EekoSg3tmHPFlwhhjjDGdiyXXxhdNrdS19Y08u6iYT7eWkZ2WxNEDc8lOS6K2oZHyqjpEhE+K91FT10jPzBQumVJASmIiw/MzO11rdShnjuvL8m1lvLB4K988dYTf4RhjjDGmgyy5Nr6pqKnnoY+L2Lq3ijPG9uGkkfkkJR5+jm2oltx4HgmkrdKSE7loYgGPzt3EB+t2c9VxQ/wOyZgjiMg5wO1AInCfqt4WoswpwF+BZGC3qp4cwxCNMSZuWHJtfFFT38BDHxexo6yaK48dwtj+2X6H5JtxA7IZ1z+bt1ftZNOeCob0yvQ7JGMOEpFE4A7gTKAYmC8iL6rqioAyucCdwDmqullEOsfQPcYYEwWWXJuYa2hUnpi3ha17q1pNrLtSK3VLLpw4gL++uYafvbCMh78yvUt0eTFdxnRgnapuABCRJ4CZwIqAMlcAz6nqZgBV3RXzKI0xJk7YONcm5v70xmpW79zPRZMGdOsW60A56cmcNa4v76/dzb+XbPM7HGMCFQBbAm4Xe8sCjQLyRGS2iCwUkatjFp0xxsQZS65NTL38yTbumr2e6YU9mTG0l9/hxJUZw3oxaVAu//fyCvZV1vodjjFNQv2MEjy1aBJwDHA+cDbwvyIyKuTORK4XkQUisqCkpCSykRpjTByw5NrEzMrt5fzg6U+YOiSPCyb29zucuJMgwokje7O3spavPrQg7qdxN91GMTAo4PZAIPjnlWLgNVWtUNXdwHvAxFA7U9V7VXWqqk7Nz8+PSsDGGOMnS65NTOw5UMPXHl5AdnoSd145haQEe+uF0j8nnRNG5LNw0142lBzwOxxjAOYDI0VkqIikAJcBLwaV+TdwoogkiUgGMANYGeM4jTEmLliGY6Lu4Y+LuPSuj9hRVs2lUwby5go716klp43pQ15GMi8s2Up9Q6Pf4ZhuTlXrgZuA13EJ81OqulxEbhCRG7wyK4HXgE+Aebjh+pb5FbMxxvgpqsm1iJwjIqtFZJ2I3BJi/RgR+VhEakTk+23Z1nQOqspLS7dRtKeSS6cMZGBe551RMVZSkhKYOamA3Qdq+XjDHr/DMQZVfUVVR6nqcFX9jbfsblW9O6DMH1V1nKoepap/9S1YY4zxWdSS64CxUc8FxgGXi8i4oGKlwLeBP7VjW9MJPPRREfOL9nLKqHwmDsr1O5xOY1TfHozu24O3V+1i94Eav8MxxhhjTJii2XJ9cGxUVa0FmsZGPUhVd6nqfKCurdua+Pf+2hJ+9fIKxvbP5oxxff0Op9M5b0J/6hoa+fMba/wOxRhjjDFhCiu5FpFnReR8EWlLMh7O2Kgd3taGdYpPG0oO8M3HFjGyTw++cMxAEmxSlDbL75HKccN68eT8zazYVu53OKaLaGd9bowxJkzhVq534WbgWisit4nImDC2CWds1A5va8M6xZ+yqjque3gBSYkJ3HfNVFKTE/0OqdM6bUxfctKT+dXLy1EN9+NjTIvaU58bY4wJU1jJtaq+qapfAqYARcB/ReQjEfmyiCQ3s1k4Y6M2pyPbGh81NCrffnwxm/dUcueXpjCop53A2BHpKYl876zRzNlQyuvLd/gdjukC2lmfG2OMCVPYPwuKSC/gWuA6YDFwO65y/m8zm4QzNmpzOrKt8dEfXlvFu2tK+OXM8Rw7zGZgjITLpw1idN8e/OaVlVTXNfgdjukC2lGfG2OMCVNSOIVE5DlgDPAIcKGqbvdWPSkiC0Jto6r1ItI0Nmoi8EDT2Kje+rtFpB+wAMgGGkXkZmCcqpaH2rbdj9LExPOLi7nnvQ1ceexgvjRjiN/hdBlPLSjm+BG9eeDDjXzvySWcMDKfK2YM9jss00m1pz43xhgTvrCSa9yEAK8ELhCRVFWtUdWpzW3kbfNK0LLAcVF34Lp8hLWtiV9Lt+zjR89+ytDemYzum23TdkfYiD5ZjOyTxTurS5ha2NPvcEzn1q763BhjTHjC7Rby6xDLPo5kIKbz2rW/musfWUCfHqlcPn0wiQk2Mkg0nD2+H1V1Dby31kbFMR1i9bkxxkRRiy3XXreNAiBdRCZzaBSPbMDOVOvmZs3dTKMqD3y4kdKKWm44eThZqeH+GGLaakBuOkcPzOHDdbvZVV5Nn+w0v0MynYjV58YYExutZUJn4056GQj8JWD5fuAnUYrJdCKzV5ewoaSCz04uoH9Out/hdHlnju3Lsq1l3P7WWn5zyQS/wzGdi9XnxhgTAy0m16r6EPCQiFyqqs/GKCbTSWzcXcFbK3cyaVAuxwzJ8zucbqFXVirTh/bkyflbuOHk4TbUoQmb1efGGBMbrXULuVJVHwUKReR7wetV9S8hNjPdwL7KWp6cv5memSnMnDgAsRkYY+bkUX1YtGkfd727nt9a67UJk9XnxhgTG62d0JjpXWcBPUJcTDd164vLOVBTz2XTBtsMjDGWk57MF6YN5OkFW9i2r8rvcEznYfW5McbEQGvdQu7xrn8Zm3BMZ/Dash28sGQbp43pQ0Ge9bP2wzdOGcGT87dw97vr+dXMo/wOx3QCVp8bY0xshDUUn4j8QUSyRSRZRN4Skd0icmW0gzPxp7Silp+98Cnj+mdzyuh8v8Pptgpy07l0ykCemL+FneXVfodjOhGrz40xJrrCHef6LFUtBy4AioFRwA+iFpWJW794cTllVXX8+QsTSUoI9+1jIm3W3M0MzMugvqGR7z25xCbtMW1h9bkxxkRRuIMSJ3vX5wGPq2qpncDWvcyau5k1O/fz0tJtnD62D4s37/M7pG6vZ2YKEwfmMr9oL6eN6et3OKbzsPrcGGOiKNymx5dEZBUwFXhLRPIB+y26G6lraOSlpdvolZnCySOtO0i8OH5Eb2obGplfVOp3KKbzsPrcGGOiKKzkWlVvAY4DpqpqHVABzIxmYCa+vLe2hD0VtVw0aQBJidYdJF4MyE1naO9MPt6wh7qGRr/DMZ2A1efGGBNdbZmreixufNTAbR6OcDwmDm3aU8G7q0uYUJDDyD42Yle8OWFEbx6Zs4lXl+3gookD/A7HdA5WnxtjTJSElVyLyCPAcGAJ0OAtVqwy7hb+7+WVJCQI503o73coJoTR/XrQKzOF+9/fwIVH97cJfUyLrD43xpjoCrfleiowTlU1msGY+LOgqJQ3V+7krHF9yUlPbn0DE3MJIhw/ojcvLt3Gwk17mVrY0++QTHxrc30uIucAtwOJwH2qelsz5aYBc4AvquozkQjWGGM6m3A7zy4D+kUzEBN/VJXfv7aK3lmpfGZ4b7/DMS2YMjiP7LQkHvyoyO9QTPxrU30uIonAHcC5wDjgchEZ10y53wOvRyhOY4zplMJtue4NrBCReUBN00JVvSgqUZm4MHt1CfOL9vJ/M8eTaGNax7WUpAQuPWYgj87ZRMn+GvJ7pPodkolfba3PpwPrVHUDgIg8gTsBckVQuW8BzwLTIh6xMcZ0IuEm17dGMwgTfxobXav1kF4ZXDZ9ME8vKPY7JNOKL80Ywr8+LOLphVu48ZQRfodj4tetbSxfAGwJuF0MzAgsICIFwCXAaVhybYzp5sJKrlX1XREZAoxU1TdFJAPX9850US99so1VO/Zz+2WTSLah9zqFeRtLGdo7k3++t4HstGQSRLhixmC/wzJxph31eagzZIP7a/8V+JGqNrR2Qq2IXA9cDzB4sL0/jTFdT1hZk4h8DXgGuMdbVAC8EKWYjM8em7OJ215dRX5WKvur621q7U5kxtCe7K2sY92uA36HYuJUO+rzYmBQwO2BwLagMlOBJ0SkCPgccKeIXBxqZ6p6r6pOVdWp+fk2IZUxpusJt0nym8DxQDmAqq4F+kQrKOOv9SUVbC+r5sSRvUmwYd06lXEDsslMSWTuhj1+h2LiV1vr8/nASBEZKiIpwGXAi4EFVHWoqhaqaiEucb9RVV+IQuzGGBP3wk2ua1S1tumGN/GADcvXRX2wroSs1CQmDsr1OxTTRkkJCUwt7MmqHfvZV1nb+gamO2pTfa6q9cBNuFFAVgJPqepyEblBRG6IerTGGNPJhHtC47si8hMgXUTOBG4EXopeWMYvq3fsZ83OA5w5rq/1te6kphX25L01JSzYtNfvUEx8anN9rqqvAK8ELbu7mbLXRihOY4zplMLNnm4BSoBPga/jKtmfRSso459/vr+B5ERhxlCbiKSz6pmZwsi+WcwvKqWuodHvcEz8sfrcGGOiKNzRQhpF5AXgBVUtiW5Ixi+7yqv595KtTB3Sk4yUcH/UMPFoxtBePDJnE2+t3MU5R9n8T+YQq8+NMSa6Wmy5FudWEdkNrAJWi0iJiPw8NuGZWHpi/hbqGpTPDO/ldyimg0b17UFOejKPzd3kdygmTlh9bowxsdFat5CbcWeVT1PVXqraEzd5wPEi8t1oB2dip6FReXL+Fk4Y0ZteWTa7X2eXmCBMK8zj/bW7Kdpd4Xc4Jj7cjNXnxhgTda0l11cDl6vqxqYF3hS4V3rrTBfx3poStu6rsklHupCpQ3qSmCDMmmfjlBvA6nNjjImJ1pLrZFXdHbzQ66eXHJ2QjB8em7uZ3lkpnDG2r9+hmAjJTk/mrHF9eXrBFqrrGvwOx/jP6nNjjImB1pLrlgbKtUF0u4gdZdW8vWonn586iJQkG36vK/nSjCHsrazj1WXb/Q7F+M/qc2OMiYHWhoSYKCLlIZYLkBaFeEyMzZq7mbdW7aRRISM50aY672I+M7wXQ3tn8ticzVwyeaDf4Rh/WX1ujDEx0GJyraqJsQrE+KNRlQVFexnRJ8tOZOyCnpi/hTH9evDqsh385Y019MtJs3713ZTV505pVSkrSlYwtvdYRMTvcIwxXZD1Aejm1uzcT1lVHdMKbdKYruqYwXkkJQhzN+7xOxRjfLVw20Im3zOZ2+feznub3vM7HGNMF2XJdTc3b2MpWalJjOuf7XcoJkoyUpOYUJDDki37qKm3ExtN99SojVz61KWoKqN6jeKJ5U+wYe8Gv8MyxnRBUU2uReQcEVktIutE5JYQ60VE/uat/0REpgSsKxKRT0VkiYgsiGac3dW2fVWs3rGfY4bkkZhgP492ZTOG9qSmvpGlW8r8DsUYX7y36T02lW3itjNu48apN5KRnMGbG970OyxjTBcUteRaRBKBO4BzgXHA5SIyLqjYucBI73I9cFfQ+lNVdZKqTo1WnN3Zk/O3oGBdQrqBQT0z6JedxtyNe1BVv8MxJuYeWfoIWSlZXDzmYtKT0zmm/zF8svMTquur/Q7NGNPFRLPlejqwTlU3qGot8AQwM6jMTOBhdeYAuSLSP4oxGU99QyNPzN/MyD5Z9MxM8TscE2UiwnHDerG9rJq5G0v9DseYmKqqq+KZlc9w6dhLyUjOAGDagGnUNdbxyc5PfI7OGNPVRDO5LgC2BNwu9paFW0aBN0RkoYhcH7Uou6m3Vu1iZ3kNM4Zaq3V3MWlwLhkpidz/wcbWCxvThby36T3Ka8q57KjLDi4b3nM4eWl5zNs6z8fIjDFdUTST61CdeIN/j26pzPGqOgXXdeSbInJSyDsRuV5EFojIgpKSkvZH2808Nncz/bLTGN3PTmTsLpITE5gxtCdvrtxJ0e4Kv8MxJmY+2PwBiZLICYNPOLgsQRKY3G8yq3avoq6hzsfojDFdTTST62JgUMDtgcC2cMuoatP1LuB5XDeTI6jqvao6VVWn5ufnRyj0rm3znkreW1PCZdMH2YmM3cyMYb1IShAe/KjI71CMiZkPt3zIpH6TyErJOmz5mN5jqGusY+M++zXHGBM50Uyu5wMjRWSoiKQAlwEvBpV5EbjaGzXkWKBMVbeLSKaI9AAQkUzgLGBZFGPtVmbN20xignDZNJtMpLvJTkvmwqMH8PSCLZRXW2ud6frqGuqYUzyH4wcdf8S6kb1GIgirdq/yITJjTFcVteRaVeuBm4DXgZXAU6q6XERuEJEbvGKvABuAdcA/gRu95X2BD0RkKTAP+I+qvhatWLuTmvoGnl6whdPH9KFfjs143B195YShVNQ28OS8La0XNqaTW7xjMVX1VYd1CWmSkZzBkJwhrN6z2ofIjDFdVYvTn3eUqr6CS6ADl90d8L8C3wyx3QZgYjRj665eX76TPRW1fOnYIX6HYnxyVEEOxw3rxX0fbOCq44aQlmyzYpuu68PNHwJw/OAjW67BdQ15Y8MbVNRWkJmSGcvQjDFdlM3Q2M08NmcTg3tmcOKI3n6HYnx002kj2FlewzMLi/0OxZiomrN1DoNzBjOgx4CQ60f3Hk2jNvLB5g9iHJkxpquy5Lob+et/1zB3Yynj+mfzxPwtzJq72e+QjA9mzd1M0e4KBuWl86c3VvPIx5v8DsmYqFm8fTHH9D+m2fUjeo4gURJ5e+PbMYzKGNOVWXLdjczdWEqiCFOG5PkdivGZiHDamD7sq6xjyZZ9fodj4pyInCMiq0VknYjcEmL9l0TkE+/ykYjERbe+/TX7WVu6lsn9JjdbJiUxhWF5w3i7yJJrY0xkWHLdTZRX17Fw816OHphDVmpUu9qbTmJU3x4MyElj9updNDTalOgmNBFJBO7AzTkwDrhcRMYFFdsInKyqRwP/B9wb2yhDW7pzKQCT+zefXIPrGrJo+yL2Vu2NRVjGmC7Okutu4qn5W6itb+Qzw62vtXFEhFNG92FPRS0vLt3qdzgmfk0H1qnqBlWtBZ4AZgYWUNWPVLUpM52Dm7PAd4u3LwZoseUaYEyvMTRqI+9tei8WYRljujhrwuwGGhqVBz8qorBXBgV56X6HY+LIuAHZDMhJ40+vr+Hco/rbyCEmlAIgcNzGYmBGC+W/Crwa1YjCtGjHIvpk9mn2ZMYmQ/OGkp6Uztsb32bmmEPfG+5deGQD/PXHXB/xOI0xXYu1XHcD/12xk+K9VdZqbY6QIMK5E/qzdV8V//qwyO9wTHwKNY1ryH5EInIqLrn+UbM7E7leRBaIyIKSkpIIhRja4u2LmdxvMiItz0SblJDEiUNOtH7XxpiIsOS6G/jXhxspyE1nbP9sv0MxcWh4fhanj+nDne+sY8+BGr/DMfGnGBgUcHsgsC24kIgcDdwHzFTVPc3tTFXvVdWpqjo1Pz8/4sE2qamvYXnJ8la7hDQ5rfA0lu1axs4DO6MWkzGme7DkuotbtrWMuRtLufYzhSQmtNx6Y7qvH583lsq6Bv765lq/QzHxZz4wUkSGikgKcBnwYmABERkMPAdcpaprfIjxCMtLllPfWN/qyYxNTht6GgCzi2ZHMSpjTHdgyXUXd+97G8hKTeIL0wa1Xth0WyP6ZHHF9MHMmreZ1Tv2+x2OiSOqWg/cBLwOrASeUtXlInKDiNzgFfs50Au4U0SWiMgCn8I9qOlkxin9p4RVfnL/yeSk5th418aYDrMTGruoWXM3U1pRy0tLt3HCyN7855Ptfodk4tx3zxzFfz7dzg+f/YTnvvEZ+6XDHKSqrwCvBC27O+D/64DrYh1XSxbvWEyPlB4MyxsWVvmkhCROLjzZ+l0bYzrMWq67sPfXlpCQIBxvJzKaVsyau5nXlu3gzHF9WbplHzfNWuR3SMZ0yKLti5jUbxIJEv5h7sxhZ7KudB0rS1ZGMTJjTFdnyXUXtb+6joWb9jJ5UC7Z6cl+h2M6iaMLchjbrwf/XbGTot0VfodjTLs0NDawdOfSsE9mbPK5cZ8jQRJ47NPHohSZMaY7sOS6i/p4/R4aGpWTRkbvbHzT9YgIMycVkJQo/PDZT2zmRtMprS1dS2VdZdj9rZv0y+rHGcPOYNans1CN7Hv/3oX3HnExxnRN1ue6C9pfXcecjXsYNyCb3j1S/Q7HdDLZ6clcMGEAzywq5q7Z67jptJF+h2RMmxycmTHMkUICfWnCl7jmhWv4aMtHYZW3iWaMMcEsue6C7nt/I9V1jZwyuo/foZhOavLgXNbu2s+f31hDWVU9Q3tnAnDFjME+R2ZM6+Zvm096Ujpje49t87aXjLmEb6d+m9s+vI0LR10Yheha5key3nSfB2oPsLV8K3uq9jAkZwgllSVU1VdRXV9NelI6uWm55KXlMTRvKKN6jWJUr1HkpuVGNTZjOiNLrruY0opa7nt/A0cNyKYg16Y6N+0jIlw8qYDivVU8tWAL3zp1BBmpVl2YzmHe1nlM6T+F5MS2n2/SI7UHPz7hx9zy1i2M7jWaUb1GRSFC/+2v2c+i7YuYv20+Tyx7gk1lm9hdufvgekHISM4gJTGF5IRk6hrrDibagYbmDuWYAcdwTH93mdJ/Cr0yesX64RgTV+xo2cXcNXsdVXUNnDG2r9+hmE4uNTmRy6YP5u531/P0wmKuOm6I3yEZ06r6xnoWbV/E14/5erv38e0Z3+bv8/7O48se5/vHfZ/MlMwIRhh7+6r38cnOT1i6YykLty9k/rb5rCxZiXqz2PdK78WQ3CGcNPgkBuUMIj8jn7z0PJISjkwR6hrq2FO1h50HdrLtwDY2l21mdtFsnlnxzMEyQ3KGcMyAY5jSb8rBxDs/087/Md2HJdddyPayKh76eBOXTB5In+w0v8MxXUBBbjrnT+jPi0u38ebKnVx5rCXYJr4t37WcqvoqphdMb/c+0pPTeWDmA5w/63z+OvevXHX0VQzOiY8uUarKgdoD7K3ey96qvS1el1aVMn/bfEqrSg9u3yezD9MGTOML477AtIJpTBswjedXPR/2/ScnJtMvqx/9svoxkYkHl1fUVrC5fDOb920mKTGJRdsX8dzK5w6uH5Q9iCn9p7gWbi/h7ptljUCma7Lkugv5+9vrUFVuPmMk76/d3foGxoRhxtCebNtXxezVJfznk+2cf3R/v0Myplnzts4DYFrBtA7t56zhZ3H9Mddz/6L7+c37vyE7NZvs1GzumH8HqkqjNqIopVWlqCqKHrz+3Qe/c+tVSU1KJT0pnQO1B1wXi8RkUhJTSElMYfH2xaQnp5ORnEF6UjqN2shHxR9R21BLXUMd1fXVVNZV8sDiBw4my/uq91HfWN9s3AmScLBvdF56HiPyRlAwpICB2QMZmD2QnNQcRNwEUcXlxRSXF3foeWqSmZLJ2N5jD/ZzP33o6VTWVbKlbAubyjaxuWwzc4rn8O/V/z64zcieIzlt6GmcNvQ0Ti081Vq3TZdhyXUXsWpHOU/O38KXZgxmUM8Mv8MxXYiIcNHEAezaX8P3n15KYe8Mxg/I8TssY0Kat3UeeWl5DM8b3uF9Tew7kdvOuI05xXMoLi/mQO0BCnMLSZAEEiQBEWHj3o2ICIIccQ1uzO3ahlrSk9OpbailqqaKuoY6ahtq2bB3A5V1lVTWVdKojYBLjpuS79TEVDKSM2jURnqk9KBfZj8yUjLISM4gMzmTjOSMwy7fnPZNeqT2OGziHD+H/MtIzmB079GM7j364LKquiq2lG+haF8Ra/as4aGlD3HPwnsAmDpgKhePvphLxl7C2N5jD34JMKazseS6C1BVfvHv5fRIS+K7Z3TNk2+Mv5ISE7hixmAe/LCIL/9rPs/c8BkG97IvcSb+fFz8MdMLpkcsMctIzuC0oacdvB08ckdHktemfakqdY11JEgCDyx+oN37e3L5k+3eNlbSk9MPjjRy1vCzaGhsYHPZZlbuXsmOAzv42Ts/42fv/IyRPUdy8ZiLuXjMxRw78Ng2zbRpjN/s3doFPLdoK3M3lvKDs0eTl5nidzimi8pOS+bhr06ntqGRqx6Yy6791a1vZEwM7Tywk+Ulyzm18FS/Q2kTESElMSXkCYRdXWJCIkPzhnLeyPP4yuSv8Pszfs8VE64gOTGZv3z8F45/4HgK/lLAjf+5kTc3vEldQ53fIRvTqu73Se5idpVX89MXPmVIzwxUYdbczX6HZLqwUX178K9rp/Gl++Zy9f3zeOL6Y8nNsC90Jj7MLpoNwKlDO1dybQ7JTcvl5CEnc/KQk6mqq+LTXZ+yeMdi7l98P3ctuIvM5Ew+P/7zfHbMZzlz+JmkJdnJ+yb+WHLdiTU2Kj969hPqG5RLpwwkwfqnmRiYPDiPe646hq8+uIDP3f0xD31luo2pbuLCO0XvkJ2a3eZpz018Sk9OZ3rBdKYXTKe2oZYVJStYvH0xz698ngeXPEhWShbnjTyPS8deyrkjzqVHag+/QzYGsOS6U7vvgw28s7qEC4/ub9Ocm5gI/GXk6uOG8OjcTXz2zg958MvTGds/28fIjIG3N77NSUNO6pbdK7q6lMQUJvWbxKR+k6hvrGf17tUs3rGYV9e+ylPLnyIlMYWThpzE+SPP5/yR5zOy10i/QzbdmPW57qQ+WrebP7y2mnPG9+PYYTYblom9YflZXH+iG5HhC3d/zKufbvc5ItOdbSnbwtrStZ2uv7Vpu6SEJMb3Gc+VR1/JH878A/9z3P/w7enfZmv5Vr77+ncZ9Y9RjPz7SG5+7WbeWP8GNfU1fodsuhlLrjuhdbsOcMOjCxmWn8kfPn+0DVdkfNMvJ43nbzyeYfmZfOOxRfz838uormvwOyzTDTXNEHjhqAt9jsTEUoIkMKrXKEb2GsnNx97Mb077DZcddRlpiWncOf9Ozn70bHJuy2HS3ZO48rkrIzautzEtsd/OOpnNeyq58r65pCQlcP8108hOS/Y7JNPNzV5dwqXHDCQrNYmHP97EG8t3ct81UzmqwMbCNrHz5PInmdxvcqfqDuDnGNRdVe+M3pxaeCqnFp5KbUMtq3ev5tNdn/Lprk9ZunMpj336GEf3PZpzR5zLuSPO5TODPkNyoh1HTWRZct2JrC85wGfv/Ija+ka+duIwm4XRxI2khATOP3oAw/KzeGHxVmbe8SHXnTiU754xirTkRL/DM13cxr0bmbt1Lr8/4/d+h2LiSEpiChP6TmBC3wmoKtsPbCczOZNX1r3Cnz/+M7//8Pf0SOnBGcPO4JwR53Da0NMYnjfcfg02HWbJdScxZ8MebnxsEfWNyldPGEq/HBt+yMSfsf2zKeyVyZqd+7nn3Q28vHQ7Pzh7NBdNHEBCgh2wTHTM+nQWAF8Y/wWfIzHxSkQY0GMAAJcfdTkXj76Y1XtWs2zXMhZsW8Dzq54HoG9mX04YfMLBy8S+E61l27SZJddxTlW5+90N/PH1VRT2zuSSSQX0yrKRQUz8Sk9JZOKgXHqkJfHKp9u5+ckl/PH11fziwnGcPrYviZZkmwiqrKvk9rm3c+awMynMLfQ7HNNJpCenHxx9RFXZcWAHa0vXsq50He9uepdnVz4LuBk6jx14LMcWHMu0gmlMGzCNguwCn6M38c6S6zi2bV8VP//3Mt5cuYvzj+7P7y89mheXbPM7LGPCMiw/ixtPHcHSLfv478qdXP/IQgb1TOea4wq54OgB9uuLiYi7F9xNSWUJvzj5F36HYjopEaF/j/7079Gfk4acBMDeqr2s37uedaXrWLtnLbOLZtOojQD0z+p/MNGeNmAak/pNom9WXz8fgokzllzHoeq6Bu57fwO3v7UWVbjg6P4cN6yXJdam00kQYfLgPI4emMuK7eV8tH43v/7PSn79n5UMykvni9MGMbWwJ0cPzCEjxaoj0zZby7fyuw9+x+lDT+f4wcf7HY7pQvLS85iaPpWpA6YCUNtQy5byLWzat4mifUXMLZ7Li6tfPFg+PyOfo/ocxYQ+Eziqz1GM7zOeET1HkJ+Rb324u6GoHs1E5BzgdiARuE9VbwtaL97684BK4FpVXRTOtl1Ryf4anltUzIMfFbG9rJrxA7I576j+5GXa9NKmc0tMECYU5DChIIdd5dWs2F7O8m3l/OmNNQfXj8jPYkSfLIbnZzK8TxbD87MY2juTzFRLuv3Wkbo8Wmrqa/j805+nqq6Kv537t2jelTGkJKYwPG84w/OGH1xWVVfFprJNbC3fytb9W9m4byPzts6joq7iYJmslCy3XU+37eCcwfTPcq3kA3oMoF9WP5vCvQuK2lFLRBKBO4AzgWJgvoi8qKorAoqdC4z0LjOAu4AZYW7bqdXWN7K9rIr1JQeYX7SXeRtLWbJlHw2NynHDevGnz09k055Kv8M0JuL6ZKfRJzuNU0b3obKmni17K9lUWsmOsmrmbNjDq8u206iHyuf3SGVQXjqDe2YwqOmSl8Ggnun0zEwhPTnRWoaiqCN1ebRiWl+6niueu4J5W+fxxKVPMC5/XLTuyphmpSenM6b3GMb0HnNwWaM2sqdyD9sPbKekooSSyhJ2V+7moy0f8eLqF6lvrD9iP7lpueSl5ZGblnvEJSc1h6yULLJSsshMyXTXyZkhl2UkZ1hdGCei2SQ0HVinqhsAROQJYCYQWCHPBB5WVQXmiEiuiPQHCsPYtsP2V9exo6yaBlUaGpXGRg79f3CZ0qBKo0KjKnjX3r/e/0pDo/u/abuGRuVATT3lVfWUV9dRXlVHeXUdew7UsnVfFTvKq1EvgUhKEPrnpHHiyN5MHJhL3+w0S6xNt5CRmsToftmM7ndo6vT6hkb2VNRSsr+G3QdqKK2opbSilo27S9hXWYcG7SMlMYGcjGRy0pPJTU8mKy2J9ORE0g5eEg7eTvduJyYkkJQgJAZckhKEBO/a3U4gIcENMxhYJjFBSBCh6RgWeCgTgeTEBIb0yoz6cxdD7a7LVTWi03ZuKdvCt179Fi+teYnM5Eye+txTfH785yN5F8Z0SIIkkJ+ZT35m/hHrGrWRA7UHKKspo6zau9S4S1VdFZV1lWwp38LqPasP3q5pCH92SUHITMkkMzmTzJRMUhNTSU1Kbfk6aFlKYgpJCUkkSiJJCUnu/4TEw5a1dLspuRekzf+D6//ekf/be98je40kKSFyKXE0k+sCYEvA7WKObMkIVaYgzG077J3VJXz78cWR3u0RUpISDh7UM1KS6J+Txtj+2eRlJNMzM5WC3HRSkmyyTGMAkhIT6JudRt/sI38qbWhUyqrqKK2oZW9lLZW1DVTVNlBVV09lbQOllbVsL6umrqHRu+jB/xuDs/IoGdo7k3e+f0ps7iw2OlKXRzS5zkvPY3nJcn50/I+4cdqNDMweGMndGxNVCZJAdmo22anZDMoeFNY2jdpITX0NtQ21VNdXU9tQS01DzaH/62uobqimtj5oeUMN9Y311DfWs792P3ur9lLXWHdwWeAlcHl3teN/dkT0pNRoJtehfpsIPrw1Vyacbd0ORK4HrvduHhCR1WFH2DG9gXibxSUeY4L4jCseY4L4jMtiCl/vTbBbftCubYdEOJZI6UhdfuTOIlBn/87764Aj3j9f5+thbRhuuXaKx/d1PMYE8RlXPMYEFler+t3ar+nftsTUbJ0dzeS6GAj8ajYQCB7uorkyKWFsC4Cq3gvEfA5ZEVmgqlNjfb8ticeYID7jiseYID7jspjCF69xdVBH6vIj+FVnB4rX1yke44rHmCA+44rHmMDiaotIxRTNvgjzgZEiMlREUoDLgBeDyrwIXC3OsUCZ10cvnG2NMcZEX0fqcmOM6Xai1nKtqvUichPwOm74pgdUdbmI3OCtvxt4BTd00zrc8E1fbmnbaMVqjDEmtI7U5cYY0x1FdQBZVX0FV+kGLrs74H8FvhnutnHG1581mxGPMUF8xhWPMUF8xmUxhS9e4+qQjtTlcSpeX6d4jCseY4L4jCseYwKLqy0iEpOoxugUemOMMcYYY7o4G//NGGOMMcaYCLHkugUico6IrBaRdSJyS4j1IiJ/89Z/IiJTYhDTIBF5R0RWishyEflOiDKniEiZiCzxLj+PQVxFIvKpd38LQqz347kaHfAcLBGRchG5OahM1J8rEXlARHaJyLKAZT1F5L8ista7zmtm2xbfg1GI648issp7jZ4Xkdxmtm3x9Y5wTLeKyNaA1+i8ZraN9XP1ZEBMRSKypJlto/JcmbaL5nukvcKp0/0iIokislhEXvY7libiJih6xqunVorIcX7HBCAi3/Vev2Ui8riI+DKneUeONzGOKaxjTazjClj3fRFREendrp2rN8OgXQ6/4E7cWQ8Mww0NuBQYF1TmPOBV3BivxwJzYxBXf2CK938PYE2IuE4BXo7x81UE9G5hfcyfqxCv5w5gSKyfK+AkYAqwLGDZH4BbvP9vAX7fnvdgFOI6C0jy/v99qLjCeb0jHNOtwPfDeH1j+lwFrf8z8PNYPld2afNrGNX3SAfiarVO9zG27wGzYn08aSWmh4DrvP9TgNw4iKkA2Aike7efAq71KZZ2HW98iCmsY02s4/KWD8KdwL2pvXW3tVw37+CUv6paCzRN+Rvo4JS/qjoHaJq+PWpUdbuqLvL+3w+sxH2w413Mn6sgpwPrVXVTDO8TAFV9DygNWjwTd5DAu744xKbhvAcjGpeqvqGqTdN0zcGNVxwzzTxX4Yj5c9VERAT4AvB4pO7PREVU3yPtFa91uogMBM4H7vM7liYiko1LiO4HUNVaVd3na1CHJAHpIpIEZNDMOO/R1oHjTUxj8vtY01xcnv8H/JBmJsIKhyXXzWtuOt+2lokaESkEJgNzQ6w+TkSWisirIjI+BuEo8IaILBQ3A1swX58r3Ni8zSU/sX6uAPqqNw6wd90nRBm/n7Ov4H5tCKW11zvSbvJ+PnygmZ80/XyuTgR2quraZtbH+rkyofn9eWpVK3V6rP0Vl2A0+hxHoGFACfAvr7vKfSKS6XdQqroV+BOwGdiOG+f9DX+jOkw4xxs/tXSsiSkRuQjYqqpLO7IfS66bF9EpfyNNRLKAZ4GbVbU8aPUiXPeHicDfgRdiENLxqjoFOBf4poicFLTez+cqBbgIeDrEaj+eq3D5+Zz9FKgHHmumSGuvdyTdBQwHJuEOXH8OUca35wq4nJZbrWP5XJnm+fkeaVUrdXqsY7kA2KWqC/2MI4Qk3M/4d6nqZKAC183BV94X/pnAUGAAkCkiV/obVecQxrEmlrFkAD8FOnzulSXXzYvolL+RJCLJuEr4MVV9Lni9qpar6gHv/1eA5HZ3yg+Tqm7zrncBz+N+gg3ky3PlORdYpKo7g1f48Vx5djZ1i/Gud4Uo49f76xrgAuBL6nVACxbG6x0xqrpTVRtUtRH4ZzP35ddzlQR8FniyuTKxfK5Mi/ysg1rUWp3ug+OBi0SkCNd95jQRedTfkAD3GharalPL/jO4ZNtvZwAbVbVEVeuA54DP+BxToHCONzEXzrEmxobjviAt9d77A4FFItKvrTuy5Lp5cTnlr9e/835gpar+pZky/bxyiMh03Ou8J4oxZYpIj6b/cScqBJ996+f0yM22LMb6uQrwInCN9/81wL9DlAnnPRhRInIO8CPgIlWtbKZMOK93JGMK7Jt/STP3FfPnynMGsEpVi0OtjPVzZVrk13ukReHU6bGmqj9W1YGqWoh7nt5WVd9bYlV1B7BFREZ7i04HVvgYUpPNwLEikuG9nqfj+s7Hi3CONzEVzrEm1lT1U1Xto6qF3nu/GHey8Y727MwuzZ9Jeh7uzO31wE+9ZTcAN3j/C3CHt/5TYGoMYjoB91PmJ8AS73JeUFw3ActxZ8PPAT4T5ZiGefe11LvfuHiuvPvNwCXLOQHLYvpc4RL77UCd92H9KtALeAtY61339MoOAF5p6T0Y5bjW4fqlNr237g6Oq7nXO4oxPeK9Zz7BHST6x8Nz5S1/sOm9FFA2Js+VXdr1OkbtPdKBmELW6X7HFRDfKcTXaCGTgAXe8/UCkOd3TF5cvwRW4b48PwKk+hRH2Mcbn2MKeazxO66g9UW0c7QQm6HRGGOMMcaYCLFuIcYYY4wxxkSIJdfGGGOMMcZEiCXXxhhjjDHGRIgl18YYY4wxxkSIJdfGGGOMMcZEiCXXxhhjjDHGRIgl16bLE5EDEd7fKyKSG8l9GmOMOVJ7628RmS0iU73/XxGRXBEpFBGbxMlEXZLfARjT2ajqeX7HYIwxJjxNdbY1iphYsZZr022IyClea8YzIrJKRB5rmvo8RNlzReSpoG1f8v4vEpHe3v9Xisg8EVkiIveISKKIfEFE/uKt/46IbPD+Hy4iH0T/kRpjTNfSlvo7xLYH6+yAZcNEZLGITPPq5tdEZKGIvC8iY6LzKEx3Ycm16W4mAzcD43BTUx/fTLn/AseKSKZ3+4vAk4EFRGSst/x4VZ0ENABfAt4DTvSKnQjsEZEC3DTH70fqgRhjTDcTbv3dIhEZDTwLfFlV5wP3At9S1WOA7wN3RiRa021ZtxDT3cxT1WIAEVkCFAJHtCarar2IvAZcKCLPAOcDPwwqdjpwDDDfa0BJB3ap6g4RyRKRHsAgYBZwEi7Rfi4aD8oYY7qBsOrvVuQD/wYuVdXlIpIFfAZ4OqAhPDUi0Zpuy5Jr093UBPzfQMufgSeBbwKlwHxV3R+0XoCHVPXHIbb9GPgysBrXWv0V4Djgf9oZtzHGdHdtqb+bUwZswbV6L8f9gr/P+/XRmIiwbiHGNG82MAX4GkFdQjxvAZ8TkT4AItJTRIZ4697D/bz4HrAYOBWoUdWyaAdtjDGmWbXAxcDVInKFqpYDG0Xk8wDiTPQzQNP5WXJtTDNUtQF4GTjXuw5evwL4GfCGiHyC66fd31v9Pq5LyHvefrbQ9p8vjTHGRJiqVgAXAN8VkZm4c2W+KiJLca3ZM/2Mz3R+oqp+x2CMMcYYY0yXYC3XxhhjjDHGRIid0Gi6PRF5HhgatPhHqvq6H/EYY4wJj9XfJh5ZtxBjjDHGGGMixLqFGGOMMcYYEyGWXBtjjDHGGBMhllwbY4wxxhgTIZZcR4GIHCciT4nINhGpFZE9IvJfEblGRBKDyk4TkWdFZKeI1IhIkYjcKSIFIfY7W0RURD5q5n4f9NYXBy0v8pbPama7pv1+ELRcW7hcHGL/vwqx71+LiHr/X9vKPpsut7bw3M4OjrOZcpeJyLsisk9EKkXkUxH5iYikN1O+t4j8xit3QESqRWS9iDwsIqeEcX+3Bj2G/SKyVkRmicjZIcoXeuWuC1p+sYi8JyK7RKRKRDaJyAsico63vkjCeA69ssHPd633mH4rImlB93uKV+aMFh5TvRfP/cHvz5Zel4A4RoTYZ3OXa71tVUR+HWKf7fncPBxi3XXeusJQsZuuLcRnpMJ7Lz0vIl8QkYSg8kd8ToLWF4nIoy3sv9nPYMA2g0SkwSvbO2D5bAnvs1PYUpzShroxYJ9fCbHuUREpavEJxupssTq7aZ/dqs620UIiTERuBv4CvA38CNgE5AFnAXcB+4B/e2WvAv6Fm1zkO8A2YCzwQ9zMf2eo6idBd7EfOE5ERqjquoD7zQA+660PZT9wsYj0CJzGW9yMgie1sN2DwD0hlq8Osey7IvJ3VS1pZl//wU0B3mQKcAfwbWB+wPLDvhy0lYjcg5tV8UHgD0Al7jH+GPccnOHNytVU/ijgddx05v8AFgB1wGjgSuAdEemnqjvDuPsTcNPyZuDOYP8c8Jq4A+41qtrYQtzfBm4HHgD+CFQAw4HzgdOA14BLgNSAze4EEoGvtxDT53HPaQ9v+x97/38rjMcT+JiSgXHAL4FjRGRKS4+nGffhHkeT83ET8TTF2GR9czto5+cG4Esicps3+Y8xgZref6nAYNz78nHgehG5UFWrIrT/cD6DV+MavhKAy4G/e8tvBLIDyv0vMA24KGj77UBhqCDaWjcG+IWIPKqqtS09yPayOvsIVmc7nbfOVlW7ROiCqwwagb81s344cLT3/2igGngGSAgq1wtYB6wBkgOWz8a9OdcCtwZtcyUuQX4aKA5aVwQ8C5QB1wat+xmwAXgX+CBonQK/DuNxF3mxVQN/Dlr3a/c2C7ndKd59nNGG53h2cJxB66/19vmdEOumATXAvwKWJXvP8xogv5l9XgH0bCWuW737TQqx7rveuv8JWFboLbsuYNlm4Plm9p/QzPJmn4+A52JE0PL/4g5eCQHLjngtmntMwHXe8rEdiaO1daHeg7T/c7MY2Ak828xjKQz3PWiXrnNp5b15Ka4+/3vAshbrLFxd+Ghr+w/1GQxYtxr4FNcws6CF2B8kqK5vKU7aWDd6yxWXxCrwraB1jwJFYTzHzdYN7YkLq7Nbeo1DPiaszo75xbqFRNYtQCnu29gRVHW9Hvp2djPu2+u3NOibpKruAX4CjMS1Rgd7BJdMB7oaeA73zTmUKlyCfVXQ8qu8/XV0TMZiXMv8jaF+5omhH+Gmr/1b8ApVnQ/cD1wlIgO8xZfinucfaTMt7qo6S1VL2xuQqv4/XEVxcytFewI7mtlHW1sbWrIISAd6t1awGU0tSMmRCadNbqZ9n5sK4LfAZ0VkagziNJ2cqj6L+5Xxa94vg5EU8jMoIscBo4CHcfXyMSIyPkL32da6scl84AXgp1F4HtoTl9XZbWd1doxZch0h4vpSnwK8oarVYWxyOq5VYnsz6/+DazU5LcS6R4BhIvIZ774HePs7on9SkIeBU0RkoLfdsbiK/JEWthERSQq+NFP2t0A97ufKmPOehzHAS+p9xQ3hRdwH/WTv9um4n89ea6Z8pLwKDBSRwS2UmQdcIyI/EJFRUYylEPcrxp4wyyd6r3u6iByDqwyXA8uiFF9LOvK5uRvX0nREf0BjmvEK7if9SB/cCwn9GbwG9/59jEP1+dUdvbN21o2Bfgbk47rwRYzV2WErxOrsTsWS68jpjftmuSnM8oNwPyGGpKoVQIlXLnjdRlz3kKZK90pcH6Z3WrnPd4EtHGr1vhr4SAP6bofwE1xftsMuEnCiTUBcJcBfga+IyPBWYomGpueqqIUyTeuayg4ESjSoT6WIJAR9oZAOxrbZu+7fQpkbcD+R/QFYLSK7ReRxETmrg/fdVNHmiTsx6VLgZ6raEOb21bjXvRLXtzEVmBnhlplwdeRzUwP8CjhbRE6KVoCmSwnncxuOVj+DIpIKfBF4U1W3qeoaYA5wpQSdWNkO7akbD1LV5cAs4IciktPBWDoal9XZrbM622eWXMe3liqHh4EveBXyVbh+fi1+cLyWgUdxP7Gl4Cry1lq7H8D1ewu+7Gum/J9wfb9/2cp+oyGcyjS4THPbvMLhXyi+2oG4Au+n2e433sF0Mq6F5jfAEtzJLK+LyM86cN+rcI+hFPcT6z2q+o82bH8s7jWfAXwB93PdGyLStwMxRVNL74MHcf37fhObUEwn1+rnNkzhfAZnArkcXic/BAwAQo5O0gbtqRuD/QLIAn7QwVjacp+hylid3Tqrs31myXXk7MH1ax4SZvlimjmjG0BEMnGt4VuaKfIUkAb8HDiK1pPkJg/jzh7+BZAJPNlK+e2quiDEpT5UYVUtw32LvzyCfQXD1fRcFbZQpun12RJwnS9HDvf0LUKfid9eTd/Km/tpDABVbVDV91T1Z6p6BjAMd3LTL0Qkr533fQnusZwHvInrF9+Wn5oXeq/5PFV9Gne2+FDgewFl6nE/3YaSGFCmozr0ufFafn4OnCAi50YgHtO1BX9um97DLb3XQ73Pw/kMXoNraXxHRHJFJBd3MmGdt64j2lM3HkZVN+ASve+ISH4H4+lIXFZnt87qbJ9Zch0hXrI5GzjTa01uzVvAVBFp7ien83Gvz9vN3F85ri/aLbj+TCvDjHMNMNfb7iVV3RfOdm30d2AXMe4npapbcWfaX9jCT4IX4frrvevdfhtXkZwTtK+1qroAV0lGwnnAZlVt7stSSKq6DTcUUhLupI/2WOZVtK8CF+BaAf7oVWptpm54q93A0QGLd+Fa2EIZgOtTt7s99xekQ58bz1O4FqZfE17Lmem+zsf9xL7Qu73Luz7ive6di9IHN8JBsBY/g16L4lm44eC2Anu9ywbcSWiXiEiP9j6IdtaNofwfrr78SXtjiUBcVme3kdXZsWfJdWTdhhta5o+hVorIUBFpenPfjjfMU3B/OhHpiTs5cB1uBJDm/AN4CddS3BZ/8LZry89MYVPVStyH4GLct+9Y+iMwnhDjgYrINNxPhY95FSC4EVTWA7+PYGtM8P1+F5iEG/+8pXJH9DnzjPGuQ56V3hZeH7Yf4JKAG9uzD6+S7I3rJ9fkHWBw8Fnd3gHzEmC+qh5oV9CH6/Dnxuse9TPcOOuXRiAm0wWJyGdxid3dXp0GbhjUYkKP4nQBkEIr57408xm8EpeMfQM4NehyM+58ns+3/9EAba8bQ8W+DTc3wTdwfZ8jwersFlid3TnrbJtEJoJU9T0R+R7wFxEZi+srtBk3iczpuPEZrwA+UdWVIvJ13Dfct0TkbtzPT2NwQ/nlAmeqal0L9/cB7sTGtsb5HC0n7YEKxI0qEmxTC2f/AvwT+D6uNSbSeonI50Is/0RV7xc3ispfRWQiriKuAk704lmGG8QeAFWt9Q6irwNLROQO3NBTtUA/Dn2Qm5tkJ9gMEWnAddkZhpuQ4Fxc38kjhpoKskxE3gGeBzbiJow4D3fSzFOqurmljcOlqi+KyHzg+yLyj+ATg0JoekwJuJ9of4BrSbo7oMyjuIPjqyLyG1zrUW/gelxryREznrUz9g5/brz9/EdEPoxUXKbTm+SdpJ2Cm0TmAlwy+1/cBB6AO8iLyI+BR0TkWdxJfuW40UR+gmt9e721Owv+DOJOLt+I61t7WB9fEXkf996+BncOTLu0tW5swW24z/XJhH8Cv9XZHWB1diesszUOBtvuahfgM7jJXLZz6KSEN3CtE8GDqB+L+2CW4CqHTbgPwKAQ+51NC4Pxe2UeJPQkMo+2st0R+8adyNHc5fut7R93MFAiP4lMODFdAbyHO/BV4SronwEZzew3H/idV64S91PwelwFe1IYcd0aFEsF7tv4LODsEOULOXJCghtwXX02efdfgRtr9YdASrivW8C6a2l+IoCzvHXfbe61CPGYGnGj0rwETA+xz564g1ER7n2/D3cAPLGF563ZGAPeg0dMZEQEPje45KDpsRW29/Nul857CXj/NV2qvPfS87jkWprZ7kJcN4X93vtvLa4FLr2Z/bf4GfSu/7eFOH/jff6GBix7kDZMIhOwLuy6sYXP3y+8dUVhPMezsTo7+PmwOruL19niBWyMMcYYY4zpIOtzbYwxxhhjTIRYcm2MMcYYY0yEWHJtjDHGGGNMhPiSXIvIAyKyS0RCznMvIqeISJmILPEuP491jMYYY4wxxrSVX0PxPYgbY7mlWQXfV9ULYhOOMcYYY4wxHedLcq1uPOjCSO+3d+/eWlgY8d0aY0zULVy4cLeqRmVSjHhldbYxprNqqc6O50lkjhORpbgxGr+vqstb26CwsJAFCxZEPzJjjIkwEQl3Qo4uw+psY0xn1VKdHa/J9SJgiKoeEJHzgBeAkaEKisj1uBmFGDx4cMwCNMYYY4wxJlhcjhaiquXqzWmvqq8Ayd7UtKHK3quqU1V1an5+t/pF1RhjjDHGxJm4TK5FpJ+IiPf/dFyce/yNyhhjjDHGmJb50i1ERB4HTgF6i0gx8AsgGUBV7wY+B3xDROqBKuAytXnajTHGGGNMnPNrtJDLW1n/D9xQfcYYY3wmIucAtwOJwH2qelsz5aYBc4AvquozMQzRGGPiRrye0Bj/7r33yGXXXx/7OIwxJopEJBG4AzgTKAbmi8iLqroiRLnfA6/HPkpjuo5ZczcfsezzUweyv7qenpkpPkRk2sqSa2OMMS2ZDqxT1Q0AIvIEMBNYEVTuW8CzwLTYhmdM17a0eB93vLOOrfuqyO+RynfPGMXl0wfhnZpm4lBcntBojDEmbhQAWwJuF3vLDhKRAuAS4O7WdiYi14vIAhFZUFJSEtFAjelqPt1axpPzt9AzM4Vbzh3DsN6Z/OT5T7nxsUVU1zX4HZ5phiXXxhhjWhKqeSz4BPO/Aj9S1VaP9jZ8qjHh2b2/hmcXFTMoL53PTx1IdloyF04cwDnj+/Hqsh18+V/z2V9d53eYJgRLro0xxrSkGBgUcHsgbubcQFOBJ0SkCDfa050icnFMojOmi3pl2XYEuGLGEJISXLqWIMJJo/L5/DEDmVdUyhX/nMueAzX+BmqOYMm1McaYlswHRorIUBFJAS4DXgwsoKpDVbVQVQuBZ4AbVfWFmEdqTBexaU8Fq3bs5+RR+eSkJx+xfvLgPP559TGs2bmfz9/9MVv3VfkQpWmOJdfGGGOapar1wE24UUBWAk+p6nIRuUFEbvA3OmO6pjdW7KRHahKfGR5ycmoAThvTl0evm8HuAzVceudHrN25P4YRmpZYcm2MMaZFqvqKqo5S1eGq+htv2d3epF/BZa+1Ma6Nab/NpZVs3F3BiaPySUlqPk2bNXcza3ce4JrPFFJRU89F//iQj9bvjmGkpjmWXBtjjDHGxIn315aQnpzItMK8sMr3z0nn6ycPJystiSvvm8s9767HJrX2l41zbYwxxhgTBzaUHGDFtnJOHp1PalJi2Nv1zEzhxpOH8+yiYn736ipeXLqNz00ZSGpyIlfMGBzFiE0o1nJtjDHGGBMH7nl3A4kJwnHDerV529TkRC6fPphzj+rHim3l3Dl7Pbv320gifrDk2hhjjDHGZ9vLqnhucTHHDMmjR9qRI4SEQ0Q4cWQ+XzlhKBW19dz17noWFJVGOFLTGkuujTHGGGN8dv/7G2lUOHFkxydXGp6fxTdOHk5GSiJX3DeXVz/dHoEITbgsuTbGGGOM8dHeilpmzdvMhUf3p2dmSkT22SsrlRtOHs5RA7K5cdYi7nt/Q0T2a1pnybUxxhhjjI8e/ngTlbUN3HDK8IjuNzM1iVlfO5Zzxvfj1/9ZyW9fWWkjicSAJdfGGGOMMT7ZX13Hgx9t5PQxfRjTLzvi+09LTuSOK6Zw9XFDuPe9Dfzq5RWWYEeZDcVnjDHGGOOT+z/YyN7KOr5zxsio3UdCgvDLi8aTlJDAAx9uZHh+FlceOyRq99fdWXJtjDHGGOOD0opa7nt/I+eM78fRA3Ojch+z5m4++P+w/ExG9c3iF/9ezvayagpy0w+us/GwI8e6hRhjjDHG+ODbjy+moqae0f16MGvu5sMS4WhIEOGLUweTnpLIy0u3WfeQKLHk2hhjjDEmxraXVTFnwx4mD86lb3ZazO43PSWRM8f2ZVNpJcu2lcfsfrsTS66NMcYYY2Lsb2+tQxVOH9M35vd9TGEefbNTeXPlTmu9jgJLro0xxhhjYqh4byVPL9jC1MI88iI0rnVbJIhw4oh8SvbXsGF3Rczvv6uz5NoYY4wxJobueGc9CSKcMrqPbzFMGJhDRkoiH6/f41sMXZUl18YYY4wxMdLUav3FaYPISU/2LY7kxASmFfZk5fZyyqrqfIujK7Kh+Dqbe+89ctn118c+DmOMMca02Z2z1yMC3zhlOLNXl/gayzGD83h3TQmfFu/zNY6uxpJrY4wxxpgomzV3M3sra3lynutr7XdiDdC7RyoDctNYWlzmdyhdinULMcYYY4yJgQ/W7Qbg5FH5PkdyyMSBuWzdV8VGO7ExYiy5NsYYY4yJspr6BhZt2suEgTnkZsR+hJDmTCjIAeDlpdt8jqTrsOTaGGOMMSbKlmzZR019I8cO7el3KIfJzUhhcM8MXlu+w+9QugxLro0xxhhjokhVmbuhlAE5aQzqmeF3OEcY1z+b5dvK2VJa6XcoXYIl18YYY4wxUbRiezk7yquZWtgTEfE7nCOMH5ANwBsrdvocSddgybUxxpgWicg5IrJaRNaJyC0h1s8UkU9EZImILBCRE/yI05h49eKSbSTIof7N8aZXVipj+vXg9WXWNSQSLLk2xhjTLBFJBO4AzgXGAZeLyLigYm8BE1V1EvAV4L6YBmlMHGtsVF5cuo1RfXuQmRq/IyCfPb4f8zeVUrK/xu9QOj1Lro0xxrRkOrBOVTeoai3wBDAzsICqHlBV9W5mAooxBoD5RaVsL6tm4qBcv0Np0dnj+6EKb660riEdZcm1McaYlhQAWwJuF3vLDiMil4jIKuA/uNZrYwyuH3NKYgJj+vXwO5QWje3fg8E9M3jdRg3pMEuujTHGtCTU2VdHtEyr6vOqOga4GPi/Zncmcr3XL3tBSYn/M9QZE02qylsrd3Lc8F6kJiX6HU6LRISzx/flw3W7Ka+u8zucTs2Sa2OMMS0pBgYF3B4INDvbhKq+BwwXkd7NrL9XVaeq6tT8/PiZpc6YaFhfUkHRnkpOH9vH71DCcs5R/ahrUN5ZtcvvUDo1X5JrEXlARHaJyLJm1ouI/M07M/0TEZkS6xiNMcYAMB8YKSJDRSQFuAx4MbCAiIwQb3wxr75OAfbEPFJj4sxbXv/l08Z0juR68qA88nukWteQDvKr5fpB4JwW1p8LjPQu1wN3xSAmY4wxQVS1HrgJeB1YCTylqstF5AYRucErdimwTESW4EYW+WLACY7GdFuzV5cwpl8PBubF38QxoSQkCGeN68vs1SVU1zX4HU6n5Uty7f1sWNpCkZnAw+rMAXJFpH9sojPGGBNIVV9R1VGqOlxVf+Mtu1tV7/b+/72qjlfVSap6nKp+4G/ExvivqraBhZv2cuLIkD2k4tbZ4/tRWdvA+2t3+x1KpxWvAy42d3b6dn/CMcYYY4wJ3/yiUmobGjl+ROdIrmfN3QxAfWMjackJ3PnOOs4c19fnqDqneE2uwzo7HdyZ57iuIwwePDiaMRljjDHGtGrW3M28umw7iSIU7a5k1r7NfocUtqSEBI4akMMnxWVU1NTH9cQ38SpeRwsJ++x0O/PcGGOMMfFmfckBBvfKICUpXlOt5k0enEdtQyOv2XTo7RKvr/iLwNXeqCHHAmWqal1CjDHGGBP3Kmvr2b6vmuH5mX6H0i6FvTLomZnCMwuL/Q6lU/KlrV9EHgdOAXqLSDHwCyAZ3EkywCvAecA6oBL4sh9xGmOMMca01aY9lSgwtHeW36G0i4gwZXAub67cxcbdFQzt3Tm/JPjFl+RaVS9vZb0C34xROMYYY4wxEVO0p4LEBGFgXrrfobTb1MKevLumhIc+KuLWi8b7HU6n0uFuISLyrIicLyLx2sXEGGOMx+psY6Jv055KBuamk5zYeT9m2WnJXDhxAE8t2EJZlU2H3haReNXvAq4A1orIbSIyJgL7NMYYEx1WZxsTRdV1DWzdW8WQXp2/K8VXjh9KZW0Dj83d5HconUqHk2tVfVNVvwRMAYqA/4rIRyLyZRFJ7uj+jTHGRI7V2cZE15It+2hQpbB355iVsSVHFeRwyuh87n1vA+XV1nodroj8XiEivYBrgeuAxcDtuIr7v5HYvzHGmMixOtuY6Jm/0U1APaRn52+5Bvj+WaPZV1nHfe9t8DuUTiMSfa6fA94HMoALVfUiVX1SVb8FdM7TZI0xpouyOtuY6Jq/aS99s1NJT0n0O5SIOKogh/Mn9Ofe9zewpbTS73A6hUi0XN+nquNU9XdNY1GLSCqAqk6NwP6NMcZEjtXZxkRJQ6OyaNNeCrtAf+tAPzl/LAki/Pzfy3ADupmWRCK5/nWIZR9HYL/GGGMiz+psY6Jk5fZyDtTUd4mTGcFN4z5r7mbeXV3CqaP78M7qEr7/9FK/w4p77R7nWkT6AQVAuohMBsRblY37udEYY0ycsDrbmOhbUOT6Wxf26nofqeOG92LljnJeXLqNr500jDH9sv0OKW51ZBKZs3EnxAwE/hKwfD/wkw7s1xhjTORZnW1MlM0v2ktBbjq5GSl+hxJxCSJ8ceog/vH2Oq57aAHP3fgZ+vRI8zusuNTu5FpVHwIeEpFLVfXZCMZkjDEmwqzONia6VJX5RaV8Zngvv0OJmh5pyVx13BD+9WER1z4wn0evm0HPzK73RaKjOtIt5EpVfRQoFJHvBa9X1b+E2MwYY4wPrM42Jro2l1aya38NUwt7+h1KVA3My+Duq47haw8v4Iv3fMwjX51BvxxrwQ7UkRMam3rrZwE9QlyMMcbED6uzjYmi+UV7AZjWxZNrgK17q7j62CFsLq3knNvf4+9vrfU7pLjSkW4h93jXv4xcOMYYY6LB6mxjomtBUSk56cmM7JPFwk17/Q4n6oblZ3HdCcP410cbuevd9Uwf2pMZw7pul5i2iMQkMn8QkWwRSRaRt0Rkt4hcGYngjDHGRJbV2cZEx7yiUqYOySMhQVov3EUU5KVzw0nDyUhJ5Mr75/LEvM1+hxQXIjHO9VmqWg5cABQDo4AfRGC/xhhjIs/qbGMibM+BGjaUVDBtaNfvEhKsd49UvnHyCI4b3ptbnvuU/31hGTX1DX6H5auODMXXJNm7Pg94XFVLRbrPtzZjjOlkrM42JsIO9bfO8zkSf6SnJHLm2L40NiqPzNnEWyt38tkpA/n+2aP9Ds0XkUiuXxKRVUAVcKOI5APVEdivMcaYyLM625gIW1BUSkpSAkcV5Pgdim8SE4TzJvRnaO9Mnlu8lTtnr2NHeTVXHTuECQU53aq7TIeTa1W9RUR+D5SraoOIVAAzOx6aMcaYSLM625jIm19UyqRBuaQmJfodiu/G9s/mu70yeGvVLl7+ZBvPLCwmOy2JAbnp5PdIpaKmnuy0ZAbkpjM8P4v0lESumDHY77AjKhIt1wBjcWOnBu7v4Qjt2xhjTGS1qc4WkXOA24FE4D5VvS1o/ZeAH3k3DwDfUNWlkQ3ZmPgza+5mausb+XRrGSeNzGfWXDuhDyAjJYkLjx7A6aP7sHrnfjaVVrK/up6NuyvYX13P/uo6GhWSEoQJBTlMLcxjVN+uMyJoh5NrEXkEGA4sAZp6sCuWXBtjTNxpa50tIonAHcCZuBMg54vIi6q6IqDYRuBkVd0rIucC9wIzovMIjIkvW/ZW0qhQ2Duz9cLdTEZqEpMH5zF58OF90esbGtm6r4qlxftYtHkfZ//1Pc6b0J9vnjKCcQOyfYo2ciLRcj0VGKeqGoF9GWOMia621tnTgXWqugFARJ7AdSM5mFyr6kcB5ecAAyMUqzFxr2h3BQIM7pnhdyidRlJiAkN6ZTKkVyZnjOnL3qpaHvywiP98sp2RfbKYUJBDeXU9/XPSGJ6fRUqSG9yus3QfiURyvQzoB2yPwL6MMcZEV1vr7AJgS8DtYlpulf4q8Gr7QjOm89m0p5J+OWmkJVt/6/bISE3iupOGcd0Jw3jpk238d8VOPt6wh+1l7jzr5EThtNF9OHFUvs+Rhi8SyXVvYIWIzANqmhaq6kUR2LcxxpjIamudHeoU/5Ct3iJyKi65PqG5OxeR64HrAQYP7hytUMY0p6FR2VxayZQh3XMIvkjKy0zh6uMKufq4QgAe/LCILXsr+Xj9Hl5fsZOt+6q4YvrgTjHqSCSS61sjsA9jjDGxcWsbyxcDgwJuDwS2BRcSkaOB+4BzVXVPcztT1XtxfbKZOnWqdSc0ndr2sipqGxop7GVdQjoi1ImgKUkJDM/PYljvTN5bU8LrK3byj3fW8e3TR/oQYdtEYii+d0VkCDBSVd8UkQzcGeXGGGPiTDvq7PnASBEZCmwFLgOuCCwgIoOB54CrVHVNlEI3Ju4U7akEYEgvO5kxWkSEk0bls3N/Df/vzTUcP6I3x8T5LwUdnv5cRL4GPAPc4y0qAF7o6H6NMcZEXlvrbFWtB24CXgdWAk+p6nIRuUFEbvCK/RzoBdwpIktEZEG04jcmnmzaU0FeRjI56cmtFzbtJiLMnDSA/KxU/u/lFTQ2xvePXh1OroFvAscD5QCquhboE4H9GmOMibw219mq+oqqjlLV4ar6G2/Z3ap6t/f/daqap6qTvMvUKD8GY3ynqhTtqaTQWq1jIjUpkR+cPZolW/bx0idH9EyLK5FIrmtUtbbphjcpQXx/pTDGmO7L6mxjImB9SQUVNfWWXMfQpVMGMqZfD25/a21ct15HIrl+V0R+AqSLyJnA08BLEdivMcaYyLM625gImLPBnbc7LN+S61hJSBBuPHUEG0oqeGPFTr/DaVYkkutbgBLgU+DrwCvAzyKw3/hVXg7LloHNm2OM6Xy6X51tTBR8vGEPOenJ9MxM8TuUbmPW3M2UVdbRMzOFX/9nBY/N2eR3SCFFYrSQRhF5AXhBVUs6HlIn8K1vwcMPw9e/DlOm+B2NMcaErVvW2cZEmKoyd8MehvbORCT+x13uShIThONH9OalpdvYuq8q5DB+fs/k2O6Wa3FuFZHdwCpgtYiUiMjPIxdeHFq9Gh59FBIS4PHHobLS74iMMaZV3bbONiYK1pccYPeBWob1ti4hfpg8KJfkRGHexlK/QwmpI91CbsadcT5NVXupak/clLjHi8h3IxFcXPr1ryEtDb75Tdc95P33/Y7IGGPCcTPdsc42Jgo+Xu/6Ww+15NoXacmJHD0wl0+Ky6iua/A7nCN0JLm+GrhcVTc2LVDVDcCV3rquRxVefhkuuwyOOgr69oX16/2OyhhjwtH96mxjomTOhlIG5KRZf2sfTS/sSW1DI59uLfM7lCN0JLlOVtXdwQu9PnxdczT1DRtg3z6YMcPdLiyEjRvtxEZjTGfQ/epsY6JAVZmzYQ/HDutl/a19NDAvnZ6ZKSzrYsl1bTvXdV4LF7rrY45x10OHuq4he/f6E88nn8Dbb1vXFGNMOLpfnW1MFKzbdYA9FbUcO6yX36F0ayLCUQNyWF9ygMraer/DOUxHkuuJIlIe4rIfmBCpAOPKwoWQkuK6hIBruQYoKop9LMXFcMcd8OSTcNFFdmKlMaY13a/ONiYKPvbGtz5uuCXXfjuqIJtGhZXb9/sdymHanVyraqKqZoe49FDVFn9iFJFzRGS1iKwTkVtCrD9FRMpEZIl3iY+z2RcsgAkTIDXV3R44EJKSXNeQWHv/fXff113nuqo88UTsYzDGdBodqbONMYfM2bCHgtx0Bual+x1Kt1eQm05uejLLt8VX15BITCLTJiKSCNwBnAuMAy4XkXEhir6vqpO8y69iGmQoqrBo0aEuIQDJyS7BjnVyXVsLc+e6MbanToXx4+Guu2IbgzHGGNONzJq7mUfnbOKdVSX0zU7l8Xlb/A6p2xMRRvfrwYaSChriaDr0mCfXwHRgnapuUNVa4Algpg9xtE3TyYyByTXA4MGwbVtsY1m8GKqq4MQTQQRuuMG1qn/6aWzjMMYYY7qR4r1VVNU1MLJvD79DMZ7h+VnUNjRSvDd+usf6kVwXAIFf94q9ZcGOE5GlIvKqiIyPTWgtWLnSXU8I6prYpw9UVMCBA7GLZdUqyMqCkSPd7Zned5PZs2MXgzHGGNPNrNm5HwFG5mf5HYrxNI01vmF3hc+RHOJHch1q3JrgtvxFwBBVnQj8HXih2Z2JXC8iC0RkQUlJFGfyXbfOXTcltE369XPXO3dG776DrV8Pw4e7VmuAQYNcC/oHH8QuBmOMMaabWbtzPwPz0slITfI7FOPJTE2if04a60ti2MjZCj+S62JgUMDtgcBh/SpUtVxVD3j/vwIki0jvUDtT1XtVdaqqTs3Pz49WzLB2LeTkQK+gs4P79HHXu3ZF774DHTjgEvlhww5ffvzxLrm2MbeNMcaYiKusqad4bxWjrEtI3BnWO5PNeyqpa2j0OxTAn+R6PjBSRIaKSApwGfBiYAER6SfeyOwiMh0X556YRxpo3ToYMeJQa3GT3r0hISF2LdcbNrjr4OT6hBNc328/hgU0xhhjurhVO/ejwOh+llzHm6G9s6hvVLburfI7FMCH5FpV64GbgNeBlcBTqrpcRG4QkRu8Yp8DlonIUuBvwGWqPjfJrlt3ZJcQgMREyM+PXXK9fr1L5pvG2G5ywgnu2rqGGGOMMRG3Yls52WlJDMi1IfjizcCe7jXZui8+kmtfOg15XT1eCVp2d8D//wD+Eeu4mlVb61qEr7gi9Po+fWLbcj14sJvMJtD48ZCdDR9+CFddFZtYjDHGmG6gqraBtbv2M2VwHgk25XncyU5LJjstKW6Saz+6hXQ+mzZBY6PrFhJK376uz3VjlPv6NDa6WIJbrcG1oE+demiKdmOMMcZExPtrS6hrUMYPyPE7FNOMgryMuBmOz5LrcDSNFNJScl1X56Ykj6YNG6Cmxo0OEsqUKfDJJy4WY4wxxkTEa8t2kJaccHDYNxN/CnLT2X2gluq6Br9DseQ6LGvXuuuWkmuANWuiG8eSJe66peS6thaWL49uHMYYY0w3UVXbwOvLd3DUgBwSE6xLSLxqmo4+HrqGWHIdjnXroEePQ8PuBYtlcp2QAAMGhF7fNHvkokXRjcMYY4zpJt5cuZOK2gYmDsr1OxTTggLvRNN4GDHEkutwNDcMX5OcHEhNjU1y3a8fJCeHXj9ihPsSYP2ujTERJCLniMhqEVknIreEWD9GRD4WkRoR+b4fMRoTLf9espV+2WnWJSTOZaYmkZuRzLYyS647h6bkujkirlU7Fsl1c11CwLVqT55sLdfGmIgRkUTgDuBcYBxwuYiMCypWCnwb+FOMwzMmqnYfqGH26hIumjTARgnpBPplp7GjrNrvMCy5blV9PWzc2HJyDa5ryOrV0YujpAS2bm05uQbX73rpUhe3McZ03HRgnapuUNVa4AlgZmABVd2lqvMBO5vadClPLyimvlH5wtRWjr0mLvTPSWP3gRrfT2q05Lo1mze7RDWc5LqoyI3mEQ1Ll7rrgQNbLnfMMVBVBatWRScOY0x3UwBsCbhd7C0zpktrbFSemL+ZGUN7MqJPlt/hmDD0y0mnUWHdrgO+xmHJdWtaG4avSd++bhzqpunJI621kUKaTJnirq3ftTEmMkL9Ft7uGXNF5HoRWSAiC0pKSjoQljHR9dH6PWzaU8kVMwb7HYoJU7/sNABW7djvaxyWXLemaRi+UFOfB4r2iCFN/a2zWvn2PHo0ZGRYv2tjTKQUA4Hf6gcC29q7M1W9V1WnqurU/Pz8DgdnTLQ88OFGemelcM5R/fwOxYSpV1YKSQnCqu3lvsbhy/Tnncq6dS5Z7dfKh6tpmL5oJteTJrVeLjHRlbOWa2NMZMwHRorIUGArcBlwhb8hGRNdf31zDW+v2sXpY/vw7MKtfodjwpQgQt/sNGu5jnutDcPXJCMjeiOGNPWhDie5BtfveskSaPB/liJjTOemqvXATcDrwErgKVVdLiI3iMgNACLST0SKge8BPxORYhHJ9i9qYzrmw3V7SEoQZgzt5Xcopo365aSxaoe/LdeWXLemtWH4Ao0aFZ0RQ5Yvd4lyuMn1lClQURH9oQGNMd2Cqr6iqqNUdbiq/sZbdreq3u39v0NVB6pqtqrmev/7e3Qzpp12lFWzaPNeJg/OIyvVfuDvbPplp7H7QC0l+6M0wEQYLLluSUODO0GxLcl1NBLappMZ29JyDbBgQeRjMcYYY7qwu99dj6py8ig7J6Az6pfTdFKjf9/vLbluSVER1Na6pDkco0fDzp1QVhbZOBYvdjMvFhaGV37cOFf+ww8jG4cxxhjThe0qr+bxeZuZPCiPnpkpfodj2qG/N2LISh9ParTkuiVNXTzGjAmvfFMS3jTCSKTMnQtTp7oZGMORmAjHHw/vvx/ZOIwxxpgu7Pa31tLQqJwy2lqtO6uM1CT6Zaexart/JzVact2SpuR69Ojwyjcl15HsGlJZ6SaQOfbYtm134omwYgXs2RO5WIwxxpguakPJAZ6Yv4UrZgymV1aq3+GYDhjTvwcrfRwxxJLrlqxaBT17Qu/e4ZUfPtyNKhLJ5HrhQjdD5HHHtW27E0901x98ELlYjDHGmC7qt6+sIjUpgW+d1sq8FibujemXzbpd+6lraPTl/i25bsnq1eG3WgOkprp+0ZEcMWTOHHfd1pbradNcPO+9F7lYjDHGmC7onVW7eHPlTr512kjye1irdWc3tn8P6hqUDSUVvty/JdctWb06/P7WTSI9YsjHH7sW8bbOZJaWBjNmwNtvRy4WY4wxpot58MMi/ufppfTOSiUzNZFZczf7HZLpoDH93DD7fo0YYgM4NqesDHbsaFvLNbjyH34Iqs1PPHPvvUcuu/76I5epupbr009vWwxNLrwQfvADN5zgsGHt24cxxhjThf13xQ5KK2q57oShJIU7cICJa8PyM0lOFFZu38/MSbG/f3sXNaetJzM2OeooOHDAJbQdtWoVbN8OJ5zQvu0vvdRdP/tsx2MxxhhjupiP1+/ho/V7OHZYL4blZ/kdjomQ5MQERvTp4VvLtSXXzVm1yl23NbmeNs1dz5vX8Rheftldn3de+7YfOtRNKGPJtTHGGHOYkv01fPuJxfTKSuWc8f38DsdE2Nj+PXwbjs+S6+YsWeL6LY9s41nD48dDenrkkuuJE2HQoPbv49JL3TjZ69d3PB5jjDGmC6itb+Rbjy9if3UdV0wfTEqSpUNdzdh+2ewor2ZvRW3M79veTc1ZtMgltklt7JaenAxTpnQ8uS4tdX23L7igY/u5+mo3ashvf9ux/RhjjDFdgKry838vY86GUn732QkHp8s2XcuY/j0AWOXDeNeWXIfS2OimHJ8ypX3bT5/ukvO6uvbH8J//QEMDnH9++/cBUFAAX/86PPSQtV4bY4zp1mbN3cxXHpzPE/O3cMrofKpq/RkH2URf04ghfkyDbsl1KBs2QHm566/cHtOnQ3U1LFvWvu1V4fbboU8fNzvjvfceurTHLbe4FvXrr4fa2P88YowxxvhNVXl71U7eWV3C1CF5nDm2r98hmSjK75FK76wUX05qtOQ6lIUL3XVHWq4B3n+/fdvPnu1iOPNMiMSwQP37wz33uDGvr7kG9vs3JagxxhgTa/UNjfzypRW8uXIXkwflcvHkAqS54XJNlzG2fzafFJfF/H5tnOtQFi1yLb3jx7dv+6FDYdw4eOYZ+Pa327ZtbS387/9C375tn/K8JVdfDdu2wU9+4pL3K65wLfNz50JmpkviGxrc5ZxzXMt7RQVUVrrrt9+GmhoXX22tKzdxouvPnZ0N/fq5y6BB7vHn5kYudmOMMaaddpZX872nlvDhuj0cP7wX507oT4Il1t3C9MKe/OXNNeyrrCU3IyVm92vJdSgLFsCECZDSzhdCBL74Rbj1ViguhoEDw9uushJuuMGdyPjII+52JN1yC5x2Gvz85/CPfzTfReSPf2x+H0lJLqFOTITly13CXRFietG8PJdkh7oUFrqRWIwxxpgoaWxUHp+/mdteXUVtfSN//NzR1DWo32GZGJoxrBeqMG9jKWfFcLhFS66DVVa65PaGGzq2ny9+EX7xC3j6afjudw9fpwqffOIu27e7Eyjvv99NXFNWBr/6FVx5Zfh9rMOd8RFcl5XXXnOPc8sWd78VFS6mxMRDl5SUQ5fU1EP/h+qm0tDg+qiXlblRTnbvdpc9e9xz+Z//uJbwQP37H55wDx7sWr2bLtnZ4T12Y4wxJsi6XQf46kPz2bSnkmH5mVw8qcAS625o4qAcUpMSmGvJtc9mz3atseee27H9jB4Nkya5vs5f/zpkZLjlS5fCbbdBUZFbNnCgS17z8uCzn3V9ok8+uYMPIgwZGS7GESM6vq/ERBd/Xp5rlQ523XWwc6c7UXTjRnd57TWXgK9e7RJyDar08vNdt5xx49z1+PHu14SePTserzHGmC6ptr6Ru2av54531pGYIFw6pYApg/Osf3U3lZqUyOTBuczduCem92vJdbDXXnOTwEQiwf3DH+Dss+FrX3N9nR95BP7yF7f/a66BGTNcYgrNtzS3V1tas6PtvvsOv923r3v8TRoaYN8+2LvXJdqlpbBrF2zaBHPmHN7qnZvrWt8nTDh0GTvWPafGGGO6rQVFpfz4uU9Zu+sAF00cwPgB2fRIS/Y7LOOzGUN78fe311JWWUdORmzeD5ZcB3v1VTj11Mj0CT7zTNc15NZbYdYs1xf7yitdi3ZWVsf331btHcov2hIToVcvdwmm6hLvbdtg61Z3WbnSnWBZX+/KiLiZNMeNc4n2uHHuMnq0O1nTGGNMl1W0u4I//3cNLy3dRkFuOv+6dhqnjunDrLmb/Q7NxIFTx/Th9rfW8try7Xxx2uCY3Kcl14FWrIB16+A734ncPn/+czjrLFi71o3OMX58/Ca58UjkUJeTwNFbGhqgpORQwr11q2vlfvFF14e9SWHhoYR76NBDfboHDoTevd3+jTHGdCqqyvJt5Tw6ZxPPLCxGBE4Znc/Jo/LZXlZtibU5aOLAHIb1zuS5RVstufbF//t/rsX6i1+M3D5F3JB6kRxWz7jW7qbh/wIn+6mvd11Ktm93CfSKFYdaumtqDt9HWpor05RsBybeTf/n5VkCbowxPqusrWdDSQWrd+zn4w17+HDdbraXVZOalMAVMwZTkJtuXUBMSCLCZ6cU8Kc31rCltJJBPTOifp+WXDfZvh0efhi++lV3Mp3pnJKSYMAAdwHXv/vUU11r9v79h/p179176LJ5MyxZ4kY7aQyaCrfppNNQiXfT7ZwcS8CNMSYC9lXWsmJ7Oet3HWB9SQXrSw6wftcBtpUdOvcmPTmR4fmZzBjaiwkFOaSnJPoYsekMZk5yyfXj8zbzw3PGRP3+fEmuReQc4HYgEbhPVW8LWi/e+vOASuBaVV0U1aB+/nPX6vm970X1boxPEhJcEpyTE3pEE3CJdVnZ4Yl3aanr871hg5s1s6zsyJFNsrJabv0eONCGFjSdWlzW2aZTU1V2ltewfFsZy7eVs2yru966r+pgmcyURHIzUuiTncb4ghx6Z6XSp0cq+T1SbRIY0yaDemYwc9IA7nt/I5ceM5Dh+dE97y3mybWIJAJ3AGcCxcB8EXlRVVcEFDsXGOldZgB3edfR8Y9/uBEtfvSjyAxNZzqnhIRD/bub09DQfAK+di3Mm+fG/A5OwLOz3S8iubnNX3JyXKLeo0fo6/ZOamRMB8RlnW3inqpSVdfA7v217Nxfzc7yanaUVVO0p4K1Ow+wbGsZFbUNAAjQKyuFAbnpTCjIoX9uGn17pNEjLcmG0DMR89Pzx/LOql384OmlPPiV6WRHsRuRHy3X04F1qroBQESeAGYCgRX1TOBhVVVgjojkikh/Vd0e0Ui2bHETvDz7LFxwAfzmNxHdvemCEhPdWNstjbcdOLRgYDeUigqoqnJjnFdVuYl8qqqO7AvenJSUQ8l2ZqYbfrDpkpYW/u1Q65KS3GNLSGj9uqV1diDsiuKnzm6FqlLXoNQ2NFJb30hdQyMJIqQmJ5CalEBKYkKnSNZUlYZGpb5RqWtopKHRPa76xkbqG9zy+obGQ8sa1S1vaKSuUWlodOsaGpWkBCE9JZG05ETSklz3iZr6Bmrq3XNUUVtPeVU9+6vrKK+uC/i/noqa+oPPZW194+H/1zdS0+CeYwGSEhNIThCSEhNoaFQqauuPaGMASEtOoE+PNMb2z6ZfThoFuen0y04jNdm6dpjo6tMjjd9cMoHvPrmES+74kJvPGMVJI/OjMjyfH8l1AbAl4HYxR7ZwhCpTAES2op47180e+Otfw/e/f2jMaWM6oqWhBUOpr3dJdlOiXV195HXwsqbLgQNuGvu6uiMvtbVH9iGPNpEjE/HAdYHXoZa1d120y7dlX4WF8OabdCFxU2fv2l/N5+/+mPoGl3Q2JZ/1AUlmqIQuUFOymZggJCUIiQlCogiJiYIgKIf2EbgvVUU5tPywcoeVDV6uR5Q5uK+gcg2qB5NnPySIm3TDJePuy0hSQgKJCUJKUgIZKYnudqJ77pqeP8VNNd7QqDQoJAqkJCWSkpRAVmoS2WlJ9EhPJjs1ifSUxE7xBcd0TRdOHEB+j1T+56mlfOvxxQDkpCfz2s0n0j8ncvNl+JFch/pUBdck4ZRxBUWuB5pmRzkgIqvbHNHPfuYubdMb2H3Ykq9/vc13HZFt2+/Ix9D52GOID+4xqLqW+4YGv+Npj8i8DuvXt7cFf0iH7zs64q/Obpuu8PlqD3vc3Ys97gBfauNOBtzarvtuts72I7kuBgYF3B4IbGtHGQBU9V4g5gNHi8gCVZ0a6/uNJHsM8cEeQ3zoCo8hSjp1nd1dX1d73N2LPe74ktB6kYibD4wUkaEikgJcBrwYVOZF4GpxjgXKYt13zxhjDGB1tjHGtEnMW65VtV5EbgJexw3r9ICqLheRG7z1dwOv4IZ0Wocb1unLsY7TGGOM1dnGGNNWvoxzraqv4CrjwGV3B/yvwDdjHVcbdYU5zO0xxAd7DPGhKzyGqOjkdXZ3fV3tcXcv9rjjiGhrp1YbY4wxxhhjwuJHn2tjjDHGGGO6JEuu20FEzhGR1SKyTkRu8TuethKRQSLyjoisFJHlIvIdv2NqLxFJFJHFIvKy37G0hzfZxjMissp7PY7zO6a2EpHveu+jZSLyuIik+R1Ta0TkARHZJSLLApb1FJH/isha77qFqTpNvOvs9XR7dKW6vT06+/GgvbrCcaQ94vnYY8l1GwVMBXwuMA64XETG+RtVm9UD/6OqY4FjgW92wsfQ5DvASr+D6IDbgddUdQwwkU72WESkAPg2MFVVj8Kd8HaZv1GF5UHgnKBltwBvqepI4C3vtumEukg93R5dqW5vj85+PGivTn0caY94P/ZYct12B6cCVtVaoGkq4E5DVber6iLv//24D2KBv1G1nYgMBM4H7vM7lvYQkWzgJOB+AFWtVdV9vgbVPklAuogkARk0M75xPFHV94DSoMUzgYe8/x8CLo5lTCaiOn093R5dpW5vj85+PGivLnQcaY+4PfZYct12zU3z2ymJSCEwGZjrcyjt8Vfgh0CM5/iOmGFACfAv76fM+0Qk0++g2kJVtwJ/AjbjprouU9U3/I2q3fo2jc3sXffxOR7Tfl2qnm6PTl63t8df6dzHg/bq9MeR9oj3Y48l120X9jS/8U5EsoBngZtVtdzveNpCRC4AdqnqQr9j6YAkYApwl6pOBiroZF0RvH7JM4GhwAAgU0Su9DcqY7pOPd0enblub48ucjxor05/HGmPeD/2WHLddmFP8xvPRCQZV/k+pqrP+R1POxwPXCQiRbiffE8TkUf9DanNioFiVW1qWXoGV0l2JmcAG1W1RFXrgOeAz/gcU3vtFJH+AN71Lp/jMe3XJerp9ugCdXt7dIXjQXt1heNIe8T1sceS67YLZyrguCYiguuftVJV/+J3PO2hqj9W1YGqWoh7Dd5W1bj51hoOVd0BbBGR0d6i04EVPobUHpuBY0Ukw3tfnU7nPZnmReAa7/9rgH/7GIvpmE5fT7dHV6jb26MrHA/aq4scR9ojro89vszQ2Jk1NxWwz2G11fHAVcCnIrLEW/YTbxY2E1vfAh7zEoANdLJpo1V1rog8AyzCjVSwmDidMSuQiDwOnAL0FpFi4BfAbcBTIvJVXMX9ef8iNB3RRerp9rC6vXvq1MeR9oj3Y4/N0GiMMcYYY0yEWLcQY4wxxhhjIsSSa2OMMcYYYyLEkmtjjDHGGGMixJJrY4wxxhhjIsSSa2OMMcYYYyLEkmtjjDHGGGMixJJr0+mIyAG/Y4g2EblYRMb5HYcxxkRSJOpvETlFRF6ORDze/m4WkYyA26+ISG6k9m+6H0uujYlPFwOWXBtjTAeJ01K+czNwMLlW1fNUdV+04zJdlyXXptPyWi9mi8gzIrJKRB7zpkFtrvw0EflIRJb+//buJ0SrKg7j+PdBF1NiC4sswVIjrTRzNCvKiqEWtSj6R2FGzMYgigpRImpRkKsWQZH9MUqrRS3SUiIywrB/01g6pqYlpQORixZlaqU587R4z9R1nNEXfctm5vnAwL3nnnN+570M5z3c93e4ktoljZTUJOllSRslrZfUUuq2SnpL0kpJ2yXdK2leqdMmaVSp96GkJyWtkbSlxFgmaZukxyux7ygxOyQ9L2lYKd8jaWEZU5uk0ZIuBa4Hnij1z/p372RExH/rKObva0q9j4GbKuWPSppfOd8kaVz52yJpEbW3+I2V9KykLyRtlvRYqX8fMAZYLWl1Kdsh6ZRyPK/0uUnSA6Wsp+/Fpa9Vkk5o/F2KgSqL6xjomqk9dTgPmEDt9b+HKK+FfQO43/YFwNXA78A9ALbPB2YDSyU1lWZTgNuBi4CFwG+2m4HPgDsr3e+3fQXwHPB26XMK0CrpZEnnArcBl9meBnQBc0rbEUBbGdMaYK7tT4EVwALb02x/d/S3JyLif6ve+bsJWAxcB1wOnFZn/5OAV2w32+4EHrZ9ITAVuFLSVNtPAT8CLbZbesWdQe1V4hcDlwBzJTWXy2cDz9ieDPwC3FznmGIIyOI6Brp22z/Y7gY6gHH91JsE7LS9FsD2r7YPALOAV0vZVqATmFjarLa92/ZPwC5gZSnf2CvOikr5Zts7be8DvgfGAlcBM4C1kjrK+YTSZj/Qkzv45WHGHxEx2NQ7f58DbLe9zbaB1+rsv9N2W+X8VknrgPXAZI6cejcLWG57r+09wDJqi3vKeDrKcebuOMjw4z2AiGO0r3LcRf//0wLcT3k9fXdXzrt7xdnXR51qPQFLbT/UR4w/y5cFHH78ERGDTb3zN/Q9fwMc4OAHhU2V4709B5LGA/OBmbZ/lrSkV92+1Pv90AUkLST+lifXMVRsBcZImglQ8q2HU0vFmFPKJgJnAN80OPYHwC2STi1xRkk68whtdgMjGzyOiIiBaCswvrL/ZHbl2g5gOoCk6cD4fvo4idpie5ek0cC1lWv9zbdrgBsknShpBHAj8NHRfogYOrK4jiHB9n5qec9PS9oAvE/tqcUiYJikjdRysltLSkcjY38NPAKskvRViX36EZq9DiwoGyizoTEihizbfwB3Ae+UDY2dlctvAqNKyt3dwLf99LGBWjrIZuAl4JPK5ReAd3s2NFbarAOWAO3A58CLttc34CPFIKd/fpGOiIiIiIhjkSfXERERERENks1TMehIWs6heXcP2n7veIwnIiLqk/k7BoOkhURERERENEjSQiIiIiIiGiSL64iIiIiIBsniOiIiIiKiQbK4joiIiIhokCyuIyIiIiIa5C/mRxsfLOKsnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Transformed Distribution\n",
    "plt.figure(figsize = (12,6))\n",
    "\n",
    "plt.subplot(221)\n",
    "g1 = sns.distplot(df2['ln_view'])\n",
    "g1.set_title(\"VIEW LOG DISTRIBUTION\", fontsize=16)\n",
    "\n",
    "plt.subplot(222)\n",
    "g2 = sns.distplot(df2['ln_like'],color='green')\n",
    "g2.set_title('LIKES LOG DISTRIBUTION', fontsize=16)\n",
    "\n",
    "plt.subplot(223)\n",
    "g3 = sns.distplot(df2['ln_comment'], color='r')\n",
    "g3.set_title(\"COMMENT LOG DISTRIBUTION\", fontsize=16)\n",
    "\n",
    "plt.subplot(224)\n",
    "g4 = sns.distplot(df2['ln_duration'])\n",
    "g4.set_title(\"DURATION LOG DISTRIBUTION\", fontsize=16)\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.2, hspace = 0.4,top = 0.9)\n",
    "\n",
    "plt.savefig('transformation.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>categoryid</th>\n",
       "      <th>category</th>\n",
       "      <th>duration</th>\n",
       "      <th>quality</th>\n",
       "      <th>caption</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>categoryid_ismissing</th>\n",
       "      <th>...</th>\n",
       "      <th>ln_like</th>\n",
       "      <th>ln_dislike</th>\n",
       "      <th>ln_comment</th>\n",
       "      <th>inverse_like</th>\n",
       "      <th>inverse_dislike</th>\n",
       "      <th>sqrt_duration</th>\n",
       "      <th>view_log</th>\n",
       "      <th>like_log</th>\n",
       "      <th>dislike_log</th>\n",
       "      <th>comment_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>711.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.664583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>479.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>5.981414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.418841</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.886069</td>\n",
       "      <td>8.522579</td>\n",
       "      <td>5.981414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.418841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>5.327876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.109189</td>\n",
       "      <td>8.448486</td>\n",
       "      <td>5.327876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.189655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>310.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>6.156979</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4.736198</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>17.606817</td>\n",
       "      <td>8.589514</td>\n",
       "      <td>6.156979</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4.736198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Education</td>\n",
       "      <td>872.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>5.746203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>0.003195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.529646</td>\n",
       "      <td>8.665958</td>\n",
       "      <td>5.746203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.882802</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>44256</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>177.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.304135</td>\n",
       "      <td>6.434547</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44257</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>195.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.964240</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44258</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>171.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.076697</td>\n",
       "      <td>5.897154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44259</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>139.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>3.135494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.789826</td>\n",
       "      <td>7.569412</td>\n",
       "      <td>3.135494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44260</th>\n",
       "      <td>22.0</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>124.0</td>\n",
       "      <td>hd</td>\n",
       "      <td>0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.135529</td>\n",
       "      <td>5.153292</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43235 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       categoryid        category  duration quality caption    view   like  \\\n",
       "0            27.0       Education     711.0      hd       0     0.0   22.0   \n",
       "1            27.0       Education     479.0      hd       0  5027.0  396.0   \n",
       "2            27.0       Education    1031.0      hd       0  4668.0  206.0   \n",
       "3            27.0       Education     310.0      hd       0  5375.0  472.0   \n",
       "4            27.0       Education     872.0      hd       0  5802.0  313.0   \n",
       "...           ...             ...       ...     ...     ...     ...    ...   \n",
       "44256        22.0  People & Blogs     177.0      hd       0   623.0    4.0   \n",
       "44257        22.0  People & Blogs     195.0      hd       0   142.0    0.0   \n",
       "44258        22.0  People & Blogs     171.0      hd       0   364.0    1.0   \n",
       "44259        22.0  People & Blogs     139.0      hd       0  1938.0   23.0   \n",
       "44260        22.0  People & Blogs     124.0      hd       0   173.0    1.0   \n",
       "\n",
       "       dislike  comment  categoryid_ismissing  ...   ln_like  ln_dislike  \\\n",
       "0          0.0      4.0                 False  ...  3.091042    0.000000   \n",
       "1          0.0     83.0                 False  ...  5.981414    0.000000   \n",
       "2          1.0     66.0                 False  ...  5.327876    0.000000   \n",
       "3          3.0    114.0                 False  ...  6.156979    1.098612   \n",
       "4          1.0    132.0                 False  ...  5.746203    0.000000   \n",
       "...        ...      ...                   ...  ...       ...         ...   \n",
       "44256      0.0      0.0                 False  ...  1.386294    0.000000   \n",
       "44257      0.0      0.0                 False  ...  0.000000    0.000000   \n",
       "44258      0.0      0.0                 False  ...  0.000000    0.000000   \n",
       "44259      0.0      1.0                 False  ...  3.135494    0.000000   \n",
       "44260      0.0      0.0                 False  ...  0.000000    0.000000   \n",
       "\n",
       "       ln_comment  inverse_like  inverse_dislike  sqrt_duration  view_log  \\\n",
       "0        1.386294      0.045455         0.000000      26.664583  0.000000   \n",
       "1        4.418841      0.002525         0.000000      21.886069  8.522579   \n",
       "2        4.189655      0.004854         1.000000      32.109189  8.448486   \n",
       "3        4.736198      0.002119         0.333333      17.606817  8.589514   \n",
       "4        4.882802      0.003195         1.000000      29.529646  8.665958   \n",
       "...           ...           ...              ...            ...       ...   \n",
       "44256    0.000000      0.250000         0.000000      13.304135  6.434547   \n",
       "44257    0.000000      0.000000         0.000000      13.964240  4.955827   \n",
       "44258    0.000000      1.000000         0.000000      13.076697  5.897154   \n",
       "44259    0.000000      0.043478         0.000000      11.789826  7.569412   \n",
       "44260    0.000000      1.000000         0.000000      11.135529  5.153292   \n",
       "\n",
       "       like_log  dislike_log  comment_log  \n",
       "0      3.091042     0.000000     1.386294  \n",
       "1      5.981414     0.000000     4.418841  \n",
       "2      5.327876     0.000000     4.189655  \n",
       "3      6.156979     1.098612     4.736198  \n",
       "4      5.746203     0.000000     4.882802  \n",
       "...         ...          ...          ...  \n",
       "44256  1.386294     0.000000     0.000000  \n",
       "44257  0.000000     0.000000     0.000000  \n",
       "44258  0.000000     0.000000     0.000000  \n",
       "44259  3.135494     0.000000     0.000000  \n",
       "44260  0.000000     0.000000     0.000000  \n",
       "\n",
       "[43235 rows x 31 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,9))\n",
    "\n",
    "plt.subplot(211)\n",
    "g = sns.countplot('videoCategoryLabel', data=df, palette=\"Set1\")\n",
    "g.set_xticklabels(g.get_xticklabels(),rotation=45)\n",
    "g.set_title(\"Counting the Video Categories \", fontsize=15)\n",
    "g.set_xlabel(\"\", fontsize=12)\n",
    "g.set_ylabel(\"Count\", fontsize=12)\n",
    "\n",
    "plt.subplot(212)\n",
    "g1 = sns.boxplot(x='category', y='view', data=df2, palette=\"Set1\")\n",
    "g1.set_xticklabels(g.get_xticklabels(),rotation=45)\n",
    "g1.set_title(\"Views Distribution by Category Names\", fontsize=20)\n",
    "g1.set_xlabel(\"\", fontsize=15)\n",
    "g1.set_ylabel(\"Views\", fontsize=15)\n",
    "\n",
    "plt.subplots_adjust(hspace = 0.9, top = 0.9)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.997000e+04\n",
       "mean     4.499237e+04\n",
       "std      2.756281e+05\n",
       "min      0.000000e+00\n",
       "25%      1.055000e+03\n",
       "50%      4.942000e+03\n",
       "75%      2.258550e+04\n",
       "max      1.778718e+07\n",
       "Name: view, dtype: float64"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['view'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hd    36258\n",
       "sd     6971\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df2['view'], df2['category'], marker='o', color='green')\n",
    "ax.set_title('Scatterplot of Video Category on View Count')\n",
    "ax.set_ylabel('Category', fontsize=14)\n",
    "ax.set_xlabel('View', fontsize=14)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ehfkYd0taWNb7tgmuModk071ADM8hcdAxa1mZNZyngNdExKHAS4B5ko4APgxcFxEHAdfl50g6GJgPHALMA86SNJT3dTawGDgo3+bl9EXAIxFxIHAm8Jm8r6nAqcDhwFzg1GJgM6vLc0jM2qa0gBPJ4/np5HwL4FhgaU5fChyXHx8LXBoRT0XEKmAlMFfSPsBuEXFTRARwYVWeyr6uAF6baz/HAMsiYkNEPAIsYzhImdXnOSRmbVNqH46kIUm3A+tIAeAW4LkR8QBAvt87bz4duL+QfU1Om54fV6ePyBMRW4BHgWkN9lVdvsWSVkhasX79+nG8U+sbnkNi1jalBpyI2BoRLwFmkGorL26wuWrtokH6WPMUy7ckIuZExJy99tqrQdFsYHgOycTkgR49qSuj1CLi18ANpGatB3MzGfl+Xd5sDbBfIdsMYG1On1EjfUQeSTsAuwMbGuzLrDHPIZl4PNCjZ5U5Sm0vSc/Jj3cGXgf8HLgKqIwaWwh8PT++CpifR57NJg0OWJ6b3TZKOiL3z5xclaeyr+OB63M/z7XA0ZL2yIMFjs5pZqObvQCOWw1v35buHWzar501Eg/06FllTvzcB1iaR5pNAi6PiG9Kugm4XNIi4D7gBICIuFPS5cBdwBbgPRGxNe/rFOACYGfgmnwDOA/4sqSVpJrN/LyvDZI+Adyatzs9IjZ09N2aWXPaffkCD/ToWUoVAKs2Z86cWLFiRbeLYdb/rpyVm7+qTJmZapTd3p+1qlafOeCVBsys29pdI/FAj57lgGNm3dXuoece6NGzvHinmXXXoWeM7MOB8ddIvFhoT3INx8y6yzWSgeEajpl1n2skA8E1HDMzK4UDjpmZlcIBx8zMSuGAY2ZmpXDAMTOzUjjgmJlZKRxwzMysFA44Zp3ii4CZjeCJn2ad0O4l9836gGs4Zp3Q7YuAuXZlPcg1HLNO6OZFwFy7sh7lGo5ZJ7R7yf1WdLt2ZVaHA45ZJ3TzImC+xLL1KAccs07o5pL73axdmTXgPhyzTunWkvuduKCZWRu4hmPWb3xBM+tRruGY9SNf0Mx60JhqOJL2lHS4pJ3aXSAz62OeHzTQWgo4knaVdDmwDrgRmJ7Tz5F0WvuLZ2Z9ozI/aNO9QAzPD3LQGRit1nA+QwoyLwWeLKR/E3hLuwplZn3I84MGXqt9OG8G3hIRt0uKQvrPgOe3r1hm1nc8P2jgtVrD2QN4uEb6rsDW8RfHzPqW5wcNvFYDzq2kWk5FpZbzx6Q+HTOz2rq5+oL1hFab1D4KXCvpkJz3r/LjucAr2104M+sjlWHad3wsNaNN2T8FGw/fHhgtBZyIuFHSK4APAL8EXgvcBrw8In7SgfJZI6su8j+vTSyeHzTQWgo4kl4WEbcCC2u8dlJEfKVtJbPGvAS9mU0wrfbhfEvSC6sTJb0D+GJ7imRN6Ychpp4EaDZQWg04nwW+I2m/SoKkk4FzgLe1s2A2iok+xNSTAM0GTksBJyI+A1wOLMvL2ywkBZv/FxHf7EQBrY6JPsS0H2poZtaSltdSi4gPADcBtwBnAcdHxLfaXTAbxUQfYjrRa2itcvOh2eiDBiS9tUby1aQRapcAz6psExH/1t7iWV0TfYjplP1zc1qN9H7jAR5mACgiGm8gbWtyXxERQw32sx9wIfA8YBuwJCL+UdJU4DJgFrCa1Dz3SM7zEWARaRWD90XEtTn9MOACYGdS8PvziIi8evWFwGGkFRHeFhGrc56FwN/k4nwyIpY2ejNz5syJFStWNPnWrWXVX8KQamj9eN2WK2fVCa4z4bjVZZfGrNNU74VRm9QiYlKTt7rBJtsCvD8iXgQcAbxH0sHAh4HrIuIg4Lr8nPzafOAQYB5wlqTKMc4GFgMH5du8nL4IeCQiDgTOJC02Sg5qpwKHkyapnippj9Heu3XQIF0kbNCaD83qKO0CbBHxAPBAfrxR0s9IK08fCxyVN1sK3AD8dU6/NCKeAlZJWgnMlbQa2C0ibgKQdCFwHHBNznNa3tcVwD9LEnAMsCwiNuQ8y0hB6pKOvWEb3aBMAhyk5kOzBprpw/kr4KyI+E1+XFdEfK6Zg0qaBfwOaeDBc3MwIiIekLR33mw6cHMh25qctjk/rk6v5Lk/72uLpEeBacX0GnmK5VpMqjmx//7+MrA2OfSM2s2HE2WAh1mbNFPDeS+p5vGb/LieAEYNOJKeDXwN+IuIeCxVQGpvWucY9dLHmmc4IWIJsARSH069gpnV1Wi5oYk6wMOsTUYNOBExu9bjolxj+fvR9iVpMinYXFQY0fagpH1y7WYf0tVEIdVC9itknwGszekzaqQX86yRtAOwO7Ahpx9VleeG0cpr1pLRRqM5wNiAa3keTh27A3/QaIPcl3Ie8LOqprerGF6bbSHw9UL6fEk7SZpNGhywPDe/bZR0RN7nyVV5Kvs6Hrg+0jC8a4GjJe2RBwscndOsG/p1Toons5o1VNqgAeBI4B3ATyTdntM+CnwauFzSIuA+4ASAiLhT0uXAXaQRbu+JiMpF3k5heFj0NfkGKaB9OQ8w2EAa5UZEbJD0CdL1fABOrwwgsJL185wUj0Yza2jUeThN7UQ6FLitiaHRE4bn4XRIP89J6ef3Zta8sc/DsQHVqWavfq4FTPTlhsw6rKkmNUlXjbLJbm0oi/WKTjZ79fOcFI9GM2uo2T6ch5t4fdU4y2K9olHn93i/PPt9TopHo5nV1VTAiYh3dbog1kM62ezlWoDZwCpzlJpNFJ1u9nItwGwgedCAbc+d32bWAQ44tr1BWsnZzErjJjWrzc1eZtZmruFYfyhjuZx+XZLHrCSu4djEV8ZyOf28JI9ZSVzDsfHr9i//MhbN9MKcZuPmGo6NTy/88i9juZx+XpLHrCSu4dj49MIv/3rzg9q5XE4ZxzDrcw44Nj698Mu/jHlDnptkNm4OODY+vfDLv4x5Q56bZDZubbkeTj/y9XCaVN2HA+mXv7+MzQaVr4djHeJf/mbWJI9Ss/HzqgRm1gTXcKw3dHsuj5l1nGs41n29MJfHzDrONRzrvl6Yy2NmHeeAY+0z1maxRnN53NRm1jfcpGbtMZ5msXpXGN1xqpvazPqIaziDrl01iPE0i9WbxR+4qc2sjzjgDLJKrWTTvUAM1yDGEnTGs8RNvbk8mzc0t083u5lNCF5poI6BWGngylm1m7I0BLEtNXUdekZzzVf19jVlJhy3ur3lK+6z3koHsxfC2qtTcGrlfZjZeHmlAauhXu0jttJyjacTi1s2s896TXkrz2lPzc3M2sYBZ5A1s8Bms30mnVjippl91m2yq6q5u+/HrOvcpFbHQDSp1WqOqknw9m2lFGlUqy5KgaPSVLb5cdj8cJOZe+h9mPUvN6lZDdU1CA3V3m4slxroREd+rUEOWzfS9MfYF0sz6yoHnEE3e0HqgH/7NjhiaXv6Ydo5+q2oVn/Ntqeby+uLpZl1nQOODWtXP0ynlqqp21/TqJnMl0ww6xVeacBGaselBjp12el6KxJoKI+sq95+HEOyzaztXMOx9uvUZafrDZM+YHH7h2SbWds54Nj2xtvh34k5ObB9k9+O02DSzmnOzaSd03M3oZn1LAccG6kdHf6dvOx0ZZDDy78MW5/MQ6Ij3W99MqUft9rBxqwHeR5OHQMxD6eWesvJQAocvbJETCeW0jGzduj+PBxJ50taJ+mnhbSpkpZJujvf71F47SOSVkr6haRjCumHSfpJfu0LkpTTd5J0WU6/RdKsQp6F+Rh3S1pY0luemBp17PfSEjGdGphgZh1TZpPaBcC8qrQPA9dFxEHAdfk5kg4G5gOH5DxnSc/MSjwbWAwclG+VfS4CHomIA4Ezgc/kfU0FTgUOB+YCpxYDm1UZrWO/enhzt1Zq7tTABDPrmNICTkR8D6heb/5YYGl+vBQ4rpB+aUQ8FRGrgJXAXEn7ALtFxE2R2gIvrMpT2dcVwGtz7ecYYFlEbIiIR4BlbB/4rKJWh3+1Si2iUxM8m9GpgQlm1jHdHjTw3Ih4ACDf753TpwP3F7Zbk9Om58fV6SPyRMQW4FFgWoN9bUfSYkkrJK1Yv379ON5WF423xjGiw7+OSi2iUxM8m9HJgQlm1hG9OvGzVqdTNEgfa56RiRFLgCWQBg2MXsweM57LPBdVJn/Wu9ZMpRbRSj9K9aKb7Rh80I5JqmZWmm7XcB7MzWTk+3U5fQ2wX2G7GcDanD6jRvqIPJJ2AHYnNeHV21f/aXeNY7RaRLP9KN1sejOzntHtgHMVUBk1thD4eiF9fh55Nps0OGB5bnbbKOmI3D9zclWeyr6OB67P/TzXAkdL2iMPFjg6p/WfTozcKi7uWT2/pdl+lG42vZlZzyitSU3SJcBRwJ6S1pBGjn0auFzSIuA+4ASAiLhT0uXAXcAW4D0RzyyWdQppxNvOwDX5BnAe8GVJK0k1m/l5XxskfQK4NW93ekRUD17oD/XWGps8Nc9byc1Z+76hPZdfruQZranMQ5jNDE/8rGtCTvys1eeiySA1XsZ/aEpnO9w9SdNskHR/4qeVoFafy+TdRr9mTKebt2oOtVaqaVV0az6PmZWmV0ep2VhVj9y6uMnfFPWWs2mH2Qtg/Q/TIpvPDBAMWLUU9joyPW3H6Doz62mu4fS7Vmbed7JWsfZqthuNXqlZeVCB2UBwwOl3zawcUNHJocqNBg54UIHZQHDAGQSTdi4+qb9dM7WKsfa1NJqz43XRzAaCA04/q4xa2/zwcJqGYNKO9fM0qlWMZwJnozk7XhfNbCA44PSzWn0jsRmGdk2Bp5ZGtYrx9LU0WrXA66KZDQSPUutn9Worz9R4xIiO/NFqFePta2m09pnXRTPre67h9LNR+0AKwWbytOFaRb1+mnr70yTPmzGzUTng9LNWRqhtezLdN+qnqbe/2OrFOM1sVG5S63dDO2/f71LL1k1w00mpb+eZZesKr93xseFlaG5eWH8bN4uZWR2u4fSrSk3l6YdH37aoOpBUVPppZi+A2NZ4GzOzGhxwJrJGc2JqjSgbj2L/zY5TR9/GzKyKA06vGm2C5WhzYtpZ2yiOXlt1EWx+bPttJu3oeTNm1pAvT1BHVy9PUO/SzsW5KVfsWb+5bMpM2Pz4yAmfrdJQajqrvsZNvUsNTJ4GJzw09uOZWb+oe3kCDxroBasuGnkRsy2P159gWRm23KhvZtO9jVcTGE0xuFXKdtM76l/gDWBzf17TzszaxwGn26prM40uE1BpJrvlj0ff72jXwKlnyszhGk3NslVNFn0mn/tvzKwxB5xueaZW08J1aCpf6tue6EyZdpw28gqcNQceBC2vUGBmhgcNdMeIDv8mlfGlvvmxkYMT6g48CK97ZmYt86CBOjo6aKBex3vRpF3gWXumL/0ddoEtTwBRe2JmO02ZOVzLqVfO4jZmZiPVHTTgGk43NDVkeXOq0Rz4J2kQQaUJq5PBBkaWzZcNMLM2ch9ONzQa7VWx7em01EzZip3/lWay4gi64hBpM7MWOOB0w6FnbD/PphfUqr34sgFm1iZuUuuG2Qtg2su7XYqRNASzF6bHY7mEtJnZKFzD6Yblfwrrrut2KUaKrXDPefDLc9NVQWF4uRxwLcfMxs01nLKtughWntPtUlBzIMm2p4eDTUWtS0iPts6bmVkNruF0WvWyNU89RM2Z+r2sOHKt1uoDrgWZWRNcw+mkVRfBze8auaLz1g6tEtCKKTNbW4qmuG2t1Qdq1YKquVZkNvAccDppxZ9v30TVdYJ931B7js2kHUGTR6ZVj1yrN4eo0dyi0S6lYGYDwQGnk8ZzeYCOCVi1ND2cu2TkEjWHnw9H/GvjZWvq1Ywa1ZjGWisys77iPpxBVPmyP2517X6XRn0xteYQjbb6wFhqRWbWd1zD6aQdp3W7BPVtundsfSqzF2xfMxpt8c6x1IrMrO+4htMpy/+08UXSuk7Dy+u0OtKs1dUHxlIrMrO+4xpOJyz/U1h5drdLMYqqodmd7FMZS63IzPqOazid0GvBpjL6bLQh2Z3sU/GabGYDzzWcdvvGId0uwfY0ublLTrtPxcw6yDWcdlp1EWy8q9ul2F4zk03dp2JmHTZQNRxJ8yT9QtJKSR9u+wG6cf2adtCQ+1TMrOMGJuBIGgL+BXg9cDBwoqSDu1uqHjA0BY5Y6mBjZh03MAEHmAusjIh7IuJp4FLg2C6Xqbt2nOaajZmVZpD6cKYD9xeerwEOL24gaTGwGGD//fusA33SjjC0K2ze4EtFm1lXDFLAqXEBmJGTUSJiCbAEYM6cORPsGgJVJk+Dyc8eviyCA4yZddkgBZw1wH6F5zOAtV0qS+uGdkmTMydPTaHz6VxTefaBsO56RsTOoSkw5x8dYMyspwxSwLkVOEjSbOBXwHzg7W09wtsDLq5VkWrWJNj1hfD4L9IlnzUEByyGuWc1zlZ9kTfXZsysBw1MwImILZL+DLgWGALOj4g7236gt3ehJc6z+M1sAhiYgAMQEVcDV3e7HGZmg2iQhkWbmVkXOeCYmVkpHHDMzKwUDjhmZlYKRUzs+Y2dImk9cO84drEn8FCbitNJE6Wc4LJ2isvafhOlnND+sj4UEfNqveCA0yGSVkTEnG6XYzQTpZzgsnaKy9p+E6WcUG5Z3aRmZmalcMAxM7NSOOB0zpJuF6BJE6Wc4LJ2isvafhOlnFBiWd2HY2ZmpXANx8zMSuGAY2ZmpXDAaZGkeZJ+IWmlpA/XeF2SvpBf/7GklzabtwtlXZDL+GNJN0o6tPDaakk/kXS7pBU9UNajJD2ay3O7pL9rNm/J5fxgoYw/lbRV0tT8Wtnn9HxJ6yT9tM7rPfFZbaKcvfQ5Ha2sPfE5bbKs5X9WI8K3Jm+kyxr8Eng+sCNwB3Bw1TZvAK4hXSbtCOCWZvN2oayvAPbIj19fKWt+vhrYs4fO61HAN8eSt8xyVm3/JuD6bpzTfLxXAi8Fflrn9V75rI5Wzp74nDZZ1q5/Tpsta9W2pXxWXcNpzVxgZUTcExFPA5cCx1ZtcyxwYSQ3A8+RtE+TeUsta0TcGBGP5Kc3k66C2g3jOTdlntdWj3UicEmHyjKqiPgesKHBJj3xWR2tnD30OW3mnNZT9v9/q2Ut5bPqgNOa6cD9hedrcloz2zSTt51aPd4i0q/digC+I+lHkhZ3oHxFzZb15ZLukHSNpENazNsOTR9L0hRgHvC1QnKZ57QZvfJZbUU3P6fN6vbntCVlflYH6gJsbVDr+tHV48rrbdNM3nZq+niSXk36R/7dQvKREbFW0t7AMkk/z7+YOqGZst4GzIyIxyW9AbgSOKjJvO3SyrHeBPwwIoq/MMs8p83olc9qU3rgc9qMXvictqq0z6prOK1ZA+xXeD4DWNvkNs3kbaemjifpt4FzgWMj4uFKekSszffrgH8nNQl0rawR8VhEPJ4fXw1MlrRnM3nLLGfBfKqaKEo+p83olc/qqHrkczqqHvmctqq8z2onO6367UaqEd4DzGa44++Qqm1+n5EdscubzduFsu4PrAReUZW+C7Br4fGNwLwul/V5DE9Ungvcl89xaee12WMBu5Paznfp1jktHHcW9Tu4e+Kz2kQ5e+Jz2mRZu/45bbas3fisukmtBRGxRdKfAdeSRp2cHxF3SvqT/Po5wNWk0T8rgU3Auxrl7XJZ/w6YBpwlCWBLpFVjnwv8e07bAbg4Ir7d5bIeD5wiaQvwJDA/0n9Eaee1yXICvAX4TkQ8Uche6jkFkHQJadTUnpLWAKcCkwtl7YnPahPl7InPaZNl7frntIWyQsmfVS9tY2ZmpXAfjpmZlcIBx8zMSuGAY2ZmpXDAMTOzUjjgmJkZMPqCn1XbnllY/PO/Jf161DwepWbWeyQdBfwnsFdEPNTd0tigkPRK4HHSGnsvbiHfe4HfiYg/bLSdazhmJZP0DUn/Uee1F0kKYCdgH+DhWtuZdULUWPBT0gGSvp3XVfu+pBfWyNrU4p8OOGblOxd4jaRZNV5bBNwLLIuI/wk3QVj3LQHeGxGHAR8Aziq+KGkmaQWF60fbkQOOWfm+BTxIntlfIWky8A7gfOCVkiKvw1V5/RWSvitpk6RfSTpb0m75tddL2ihph/z8oJz/7EL+MyQtK+H9WZ+Q9GzS9Yi+Kul24IukmnfRfOCKiNg62v4ccMxKFhFbgKXAOyUV/wffBOwJ/Gt1Hkm/BXwHuAo4FHgr8BJScAL4PvAsYE5+fhTwEPDqwm6OAm5oy5uwQTEJ+HVEvKRwe1HVNtst/tloZ2ZWvvNIi1K+rpC2iLSu1f01tv8gcFlEfDYi7o6IW4BTgD+QtHekFYpvYzjAHAX8MzBT0j75micvwwHHWhARjwGrJJ0Az1yWvHiJ7xcAewA3NbM/BxyzLoiIu4HvAX8IIGlf4BhS/04thwEnSXq8cgN+mF87IN/fQAo0AK8irQS9PKcdCWzOz81qygt+3gS8QNIaSYuABcAiSXcAdzLySqUnApc229fo1aLNuudc4EuSpgLvJI0OuqrOtpPy9mfWeO1X+f4G4D2SDgZ2BX6U014NrAdujIjNbSq79aGIOLHOS/PqbH9aK/t3wDHrniuAfwJOItV0LmwQEG4jXT9lZYP9fZ80nPpDwA8iYqukG0ijjNaRLkdg1jVuUjPrkoh4ErgYOI3ULHZeg80/A8yVdI6k35F0oKQ3SvpiYX+VfpyTSJNGITWP7AccjvtvrMsccMy661xSp+uNEfGzehtFxI+BV5Ku4Phd0hUjP0UaXl30n6QLfN2Q8/0GuBl4CvffWJd5aRszMyuFazhmZlYKBxwzMyuFA46ZmZXCAcfMzErhgGNmZqVwwDEzs1I44JiZWSkccMzMrBT/C1StI0WVe6TcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df2['view'], df2['like'], marker='o', color='orange')\n",
    "ax.set_title('Scatterplot of Like Count on View Count')\n",
    "ax.set_ylabel('Like', fontsize=14)\n",
    "ax.set_xlabel('View', fontsize=14)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatterplot\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df2['view'], df2['comment'], marker='o', color='green')\n",
    "ax.set_title('Scatterplot of Comment Count on View Count')\n",
    "ax.set_ylabel('Comment', fontsize=14)\n",
    "ax.set_xlabel('View', fontsize=14)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc9ed0e8190>"
      ]
     },
     "execution_count": 210,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864.625x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.relplot(\n",
    "    data=df2,\n",
    "    x=\"like\", y=\"view\", col=\"caption\",\n",
    "    hue=\"category\", style=\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc9fa037c70>"
      ]
     },
     "execution_count": 236,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864.625x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.relplot(\n",
    "    data=df2,\n",
    "    x=\"comment\", y=\"view\", col=\"quality\",\n",
    "    hue=\"category\", style=\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 234,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.lmplot(data=df2, x=\"comment\", y=\"view\", hue=\"category\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 235,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.lmplot(data=df2, x=\"like\", y=\"view\", hue=\"category\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 225,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.lmplot(data=df2, x=\"ln_comment\", y=\"ln_view\", hue=\"category\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7ff927e1de50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=df1,x='comment',y='view',hue='category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.get_dummies(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename column\n",
    "df4 = df3.rename(columns={'category_Howto & Style':'category_Howto_Style', 'category_People & Blogs':\n",
    "                         'category_People_Blogs','category_Science & Technology':\n",
    "                         'category_Science_Technology'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['categoryid', 'duration', 'view', 'like', 'dislike', 'comment',\n",
       "       'categoryid_ismissing', 'category_ismissing', 'duration_ismissing',\n",
       "       'quality_ismissing', 'caption_ismissing', 'view_ismissing',\n",
       "       'like_ismissing', 'dislike_ismissing', 'comment_ismissing',\n",
       "       'num_missing', 'ln_view', 'ln_duration', 'ln_like', 'ln_dislike',\n",
       "       'ln_comment', 'inverse_like', 'inverse_dislike', 'sqrt_duration',\n",
       "       'category_Autos & Vehicles', 'category_Comedy', 'category_Education',\n",
       "       'category_Entertainment', 'category_Film & Animation',\n",
       "       'category_Gaming', 'category_Howto_Style', 'category_Music',\n",
       "       'category_News & Politics', 'category_Nonprofits & Activism',\n",
       "       'category_People_Blogs', 'category_Pets & Animals',\n",
       "       'category_Science_Technology', 'category_Sports',\n",
       "       'category_Travel & Events', 'quality_hd', 'quality_sd', 'caption_0',\n",
       "       'caption_True'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>categoryid</th>\n",
       "      <th>duration</th>\n",
       "      <th>view</th>\n",
       "      <th>like</th>\n",
       "      <th>dislike</th>\n",
       "      <th>comment</th>\n",
       "      <th>num_missing</th>\n",
       "      <th>view_ismissing</th>\n",
       "      <th>like_ismissing</th>\n",
       "      <th>dislike_ismissing</th>\n",
       "      <th>comment_ismissing</th>\n",
       "      <th>category_Education</th>\n",
       "      <th>category_Howto_Style</th>\n",
       "      <th>category_People_Blogs</th>\n",
       "      <th>category_Science_Technology</th>\n",
       "      <th>quality_hd</th>\n",
       "      <th>quality_sd</th>\n",
       "      <th>caption_False</th>\n",
       "      <th>caption_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>5027.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44256</th>\n",
       "      <td>22.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44257</th>\n",
       "      <td>22.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44258</th>\n",
       "      <td>22.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44259</th>\n",
       "      <td>22.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44260</th>\n",
       "      <td>22.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>44094 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       categoryid  duration    view   like  dislike  comment  num_missing  \\\n",
       "0            27.0     711.0     0.0   22.0      0.0      4.0          0.0   \n",
       "1            27.0     479.0  5027.0  396.0      0.0     83.0          0.0   \n",
       "2            27.0    1031.0  4668.0  206.0      1.0     66.0          0.0   \n",
       "3            27.0     310.0  5375.0  472.0      3.0    114.0          0.0   \n",
       "4            27.0     872.0  5802.0  313.0      1.0    132.0          0.0   \n",
       "...           ...       ...     ...    ...      ...      ...          ...   \n",
       "44256        22.0     177.0   623.0    4.0      0.0      0.0          0.0   \n",
       "44257        22.0     195.0   142.0    0.0      0.0      0.0          0.0   \n",
       "44258        22.0     171.0   364.0    1.0      0.0      0.0          0.0   \n",
       "44259        22.0     139.0  1938.0   23.0      0.0      1.0          0.0   \n",
       "44260        22.0     124.0   173.0    1.0      0.0      0.0          0.0   \n",
       "\n",
       "       view_ismissing  like_ismissing  dislike_ismissing  comment_ismissing  \\\n",
       "0               False           False              False              False   \n",
       "1               False           False              False              False   \n",
       "2               False           False              False              False   \n",
       "3               False           False              False              False   \n",
       "4               False           False              False              False   \n",
       "...               ...             ...                ...                ...   \n",
       "44256           False           False              False              False   \n",
       "44257           False           False              False              False   \n",
       "44258           False           False              False              False   \n",
       "44259           False           False              False              False   \n",
       "44260           False           False              False              False   \n",
       "\n",
       "       category_Education  category_Howto_Style  category_People_Blogs  \\\n",
       "0                       1                     0                      0   \n",
       "1                       1                     0                      0   \n",
       "2                       1                     0                      0   \n",
       "3                       1                     0                      0   \n",
       "4                       1                     0                      0   \n",
       "...                   ...                   ...                    ...   \n",
       "44256                   0                     0                      1   \n",
       "44257                   0                     0                      1   \n",
       "44258                   0                     0                      1   \n",
       "44259                   0                     0                      1   \n",
       "44260                   0                     0                      1   \n",
       "\n",
       "       category_Science_Technology  quality_hd  quality_sd  caption_False  \\\n",
       "0                                0           1           0              1   \n",
       "1                                0           1           0              1   \n",
       "2                                0           1           0              1   \n",
       "3                                0           1           0              1   \n",
       "4                                0           1           0              1   \n",
       "...                            ...         ...         ...            ...   \n",
       "44256                            0           1           0              1   \n",
       "44257                            0           1           0              1   \n",
       "44258                            0           1           0              1   \n",
       "44259                            0           1           0              1   \n",
       "44260                            0           1           0              1   \n",
       "\n",
       "       caption_True  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "...             ...  \n",
       "44256             0  \n",
       "44257             0  \n",
       "44258             0  \n",
       "44259             0  \n",
       "44260             0  \n",
       "\n",
       "[44094 rows x 19 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting started with regression\n",
    "import patsy                           # provides a syntax for specifying models  \n",
    "import statsmodels.api as sm           # provides statistical models like ols, gmm, anova, etc...\n",
    "import statsmodels.formula.api as smf  # provides a way to directly spec models from formulas\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ols.formula.api as smf  # provides a way to directly spec models from formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_model3 = smf.ols('ln_view ~ category_Howto_Style + category_People_Blogs + category_Science_Technology + ln_comment + ln_duration + ln_like + quality_hd + caption_True', data=df4) \n",
    "view_model2 = smf.ols('ln_view ~ category_Howto_Style + category_People_Blogs + category_Science_Technology + ln_duration + ln_like + quality_hd + caption_True', data=df4) \n",
    "view_model1 = smf.ols('ln_view ~ category_Howto_Style + category_People_Blogs + category_Science_Technology', data=df4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "results1 = view_model1.fit()\n",
    "results2 = view_model2.fit()\n",
    "results3 = view_model3.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                ln_view   R-squared:                       0.273\n",
      "Model:                            OLS   Adj. R-squared:                  0.273\n",
      "Method:                 Least Squares   F-statistic:                     5433.\n",
      "Date:                Tue, 25 May 2021   Prob (F-statistic):               0.00\n",
      "Time:                        12:12:26   Log-Likelihood:                -94509.\n",
      "No. Observations:               43400   AIC:                         1.890e+05\n",
      "Df Residuals:                   43396   BIC:                         1.891e+05\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Intercept                       8.9934      0.015    596.529      0.000       8.964       9.023\n",
      "category_Howto_Style           -0.9010      0.050    -18.167      0.000      -0.998      -0.804\n",
      "category_People_Blogs          -1.4653      0.052    -28.219      0.000      -1.567      -1.364\n",
      "category_Science_Technology    -2.7358      0.021   -127.305      0.000      -2.778      -2.694\n",
      "==============================================================================\n",
      "Omnibus:                     1060.757   Durbin-Watson:                   0.583\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1140.391\n",
      "Skew:                           0.397   Prob(JB):                    2.33e-248\n",
      "Kurtosis:                       2.996   Cond. No.                         5.90\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                ln_view   R-squared:                       0.273\n",
      "Model:                            OLS   Adj. R-squared:                  0.273\n",
      "Method:                 Least Squares   F-statistic:                     5433.\n",
      "Date:                Tue, 25 May 2021   Prob (F-statistic):               0.00\n",
      "Time:                        09:36:09   Log-Likelihood:                -94509.\n",
      "No. Observations:               43400   AIC:                         1.890e+05\n",
      "Df Residuals:                   43396   BIC:                         1.891e+05\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Intercept                       8.9934      0.015    596.529      0.000       8.964       9.023\n",
      "category_Howto_Style           -0.9010      0.050    -18.167      0.000      -0.998      -0.804\n",
      "category_People_Blogs          -1.4653      0.052    -28.219      0.000      -1.567      -1.364\n",
      "category_Science_Technology    -2.7358      0.021   -127.305      0.000      -2.778      -2.694\n",
      "==============================================================================\n",
      "Omnibus:                     1060.757   Durbin-Watson:                   0.583\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1140.391\n",
      "Skew:                           0.397   Prob(JB):                    2.33e-248\n",
      "Kurtosis:                       2.996   Cond. No.                         5.90\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                ln_view   R-squared:                       0.844\n",
      "Model:                            OLS   Adj. R-squared:                  0.844\n",
      "Method:                 Least Squares   F-statistic:                 2.929e+04\n",
      "Date:                Tue, 25 May 2021   Prob (F-statistic):               0.00\n",
      "Time:                        12:48:33   Log-Likelihood:                -61143.\n",
      "No. Observations:               43400   AIC:                         1.223e+05\n",
      "Df Residuals:                   43391   BIC:                         1.224e+05\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Intercept                       6.6845      0.034    194.438      0.000       6.617       6.752\n",
      "category_Howto_Style           -0.1857      0.027     -6.887      0.000      -0.239      -0.133\n",
      "category_People_Blogs           0.0342      0.025      1.351      0.177      -0.015       0.084\n",
      "category_Science_Technology    -0.2117      0.012    -17.417      0.000      -0.236      -0.188\n",
      "ln_comment                     -0.1809      0.005    -36.490      0.000      -0.191      -0.171\n",
      "ln_duration                    -0.1310      0.005    -26.209      0.000      -0.141      -0.121\n",
      "ln_like                         1.0177      0.004    276.179      0.000       1.011       1.025\n",
      "quality_hd                     -1.0178      0.014    -74.248      0.000      -1.045      -0.991\n",
      "caption_True                   -0.1766      0.019     -9.345      0.000      -0.214      -0.140\n",
      "==============================================================================\n",
      "Omnibus:                      275.627   Durbin-Watson:                   0.788\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              340.651\n",
      "Skew:                           0.121   Prob(JB):                     1.07e-74\n",
      "Kurtosis:                       3.360   Cond. No.                         58.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 229,
     "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": [
    "results1.resid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 230,
     "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": [
    "results2.resid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 231,
     "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": [
    "results3.resid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          OLS regression model with transformed data\n",
      "===============================================================\n",
      "                            view_model1 view_model2 view_model3\n",
      "---------------------------------------------------------------\n",
      "Intercept                   8.99***     6.68***     6.68***    \n",
      "                            (0.02)      (0.03)      (0.03)     \n",
      "ln_comment                                          -0.18***   \n",
      "                                                    (0.00)     \n",
      "quality_hd                              -1.09***    -1.02***   \n",
      "                                        (0.01)      (0.01)     \n",
      "ln_duration                             -0.11***    -0.13***   \n",
      "                                        (0.01)      (0.00)     \n",
      "ln_like                                 0.92***     1.02***    \n",
      "                                        (0.00)      (0.00)     \n",
      "category_Howto_Style        -0.90***    -0.18***    -0.19***   \n",
      "                            (0.05)      (0.03)      (0.03)     \n",
      "category_Science_Technology -2.74***    -0.30***    -0.21***   \n",
      "                            (0.02)      (0.01)      (0.01)     \n",
      "caption_True                            -0.22***    -0.18***   \n",
      "                                        (0.02)      (0.02)     \n",
      "category_People_Blogs       -1.47***    0.06**      0.03       \n",
      "                            (0.05)      (0.03)      (0.03)     \n",
      "R-squared                   0.27        0.84        0.84       \n",
      "R-squared Adj.              0.27        0.84        0.84       \n",
      "===============================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.iolib.summary2 import summary_col #create regression table for your report\n",
    "\n",
    "table = summary_col(\n",
    "    [results1, results2, results3], #results of the fitted models\n",
    "    model_names = ['view_model1', 'view_model2','view_model3'], #models fitted above\n",
    "    stars=True, #level of significance\n",
    "    regressor_order = ['Intercept', 'How To & Styles category', 'People & Blogs category', 'Science & Technology category',\n",
    "                       'Log of Comment','Squaroot of Duration','Log of Like','Quality','Caption'], #order of explanatory vars in the table\n",
    "    float_format='%0.2f', \n",
    "    drop_omitted = False #\n",
    "    )\n",
    "\n",
    "table.add_title('OLS regression model with transformed data')\n",
    "#table.add_text('any other table info...') #add other table info to be printed with the table\n",
    "\n",
    "print(table.as_text())\n",
    "\n",
    "# The 'w' is for 'write'.\n",
    "fout = open('table1.txt', 'w')\n",
    "fout.write(table.as_text()) #can export as text, latex or html\n",
    "fout.close()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
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 "nbformat_minor": 4
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