Predicting_YouTube_Dislikes_(bag_of_words_model).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ramtiin/Predicting-YouTube-Dislikes-using-Machine-Learning/blob/main/Predicting_YouTube_Dislikes_(bag_of_words_model).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook uses a different technique called **bag-of-words (BoW)** instead of VADER and sentiment analysis and showed a lower MAE of around 2,700 which is more accurate."
      ],
      "metadata": {
        "id": "XHLgF1bRBw_Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fnT9ONz7A5i3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_IWxiNIU_SJ2"
      },
      "source": [
        "**Downloading the dataset:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxGEjcSo_YhV"
      },
      "source": [
        "For downloading datasets from Kaggle, you will need an API key.\n",
        "\n",
        "To find your API credentials, follow these steps:\n",
        "\n",
        "1.   After signing in to https://kaggle.com/, select \"My Account\" from the menu on the top right. \n",
        "2.   Click \"Create New API Token\" under the \"API\" section. This will download a file kaggle.json.\n",
        "\n",
        "Then upload kaggle.json file to this notebook and run the following codes:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jiZyXR6AlHO",
        "outputId": "f5938ac3-157e-480a-f7a7-f27987683d4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading youtube-dislikes-dataset.zip to /content\n",
            " 78% 46.0M/58.8M [00:00<00:00, 93.7MB/s]\n",
            "100% 58.8M/58.8M [00:00<00:00, 108MB/s] \n"
          ]
        }
      ],
      "source": [
        "! pip install -q kaggle\n",
        "\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "#! kaggle datasets list\n",
        "\n",
        "! kaggle datasets download -d dmitrynikolaev/youtube-dislikes-dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "pBwfPoKhBSgk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d618715e-dae2-4abe-aee7-f7275838c778"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  youtube-dislikes-dataset.zip\n",
            "  inflating: video_IDs/unique_ids_CA.txt  \n",
            "  inflating: video_IDs/unique_ids_GB.txt  \n",
            "  inflating: video_IDs/unique_ids_GB_CA_US.txt  \n",
            "  inflating: video_IDs/unique_ids_US.txt  \n",
            "  inflating: video_IDs/unique_ids_from_kaggle.txt  \n",
            "  inflating: youtube_dislike_dataset.csv  \n"
          ]
        }
      ],
      "source": [
        "! unzip youtube-dislikes-dataset.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-b6nQwloBYpX"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"youtube_dislike_dataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "TFqX3ZlFM0vy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "537710b1-87b1-48cc-894c-e93bd078e7ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          video_id                                              title  \\\n",
              "0      --0bCF-iK2E               Jadon Sancho  Magical Skills & Goals   \n",
              "1      --14w5SOEUs                 Migos - Avalanche (Official Video)   \n",
              "2      --40TEbZ9Is         Supporting Actress in a Comedy: 73rd Emmys   \n",
              "3      --4tfbSyYDE            JO1'YOUNG (JO1 ver.)' PERFORMANCE VIDEO   \n",
              "4      --DKkzWVh-E                       Why Retaining Walls Collapse   \n",
              "...            ...                                                ...   \n",
              "37417  zzd4ydafGR0  Lil Tjay - Calling My Phone (feat. 6LACK) [Off...   \n",
              "37418  zziBybeSAtw  PELICANS at LAKERS | FULL GAME HIGHLIGHTS | Ja...   \n",
              "37419  zzk09ESX7e0                 [MV]  (MAMAMOO) - Where Are We Now   \n",
              "37420  zzmQEb0Em5I              FELLIPE ESCUDERO- Master Podcast  #12   \n",
              "37421  zzxPZwaA-8w  Gareth Bale brace secures dramatic comeback on...   \n",
              "\n",
              "                     channel_id          channel_title         published_at  \\\n",
              "0      UC6UL29enLNe4mqwTfAyeNuw             Bundesliga  2021-07-01 10:00:00   \n",
              "1      UCGIelM2Dj3zza3xyV3pL3WQ              MigosVEVO  2021-06-10 16:00:00   \n",
              "2      UClBKH8yZRcM4AsRjDVEdjMg     Television Academy  2021-09-20 01:03:32   \n",
              "3      UCsmXiDP8S40uBeJYxvyulmA                    JO1  2021-03-03 10:00:17   \n",
              "4      UCMOqf8ab-42UUQIdVoKwjlQ  Practical Engineering  2021-12-07 13:00:00   \n",
              "...                         ...                    ...                  ...   \n",
              "37417  UCEB4a5o_6KfjxHwNMnmj54Q               Lil Tjay  2021-02-12 05:03:49   \n",
              "37418  UCWJ2lWNubArHWmf3FIHbfcQ                    NBA  2021-01-16 05:39:05   \n",
              "37419  UCuhAUMLzJxlP1W7mEk0_6lA                MAMAMOO  2021-06-02 09:00:10   \n",
              "37420  UC8NjnNWMsRqq11NYvHAQb1g         Master Podcast  2020-10-20 20:59:30   \n",
              "37421  UCEg25rdRZXg32iwai6N6l0w      Tottenham Hotspur  2021-05-23 21:00:31   \n",
              "\n",
              "       view_count    likes  dislikes  comment_count  \\\n",
              "0         1048888    19515       226           1319   \n",
              "1        15352638   359277      7479          18729   \n",
              "2          925281    11212       401            831   \n",
              "3         2641597    39131       441           3745   \n",
              "4          715724    32887       367           1067   \n",
              "...           ...      ...       ...            ...   \n",
              "37417   120408275  2180780     35871          81360   \n",
              "37418     2841917    20759      1049           2624   \n",
              "37419    13346678   720854      4426          90616   \n",
              "37420      252057    19198      1234           1471   \n",
              "37421     2252090    34063       868           2004   \n",
              "\n",
              "                                                    tags  \\\n",
              "0      football soccer ftbol alemn Bundesliga season ...   \n",
              "1      Migos Avalanche Quality Control Music/Motown R...   \n",
              "2                                                          \n",
              "3      PRODUCE101JAPAN              JO1   TheSTAR STA...   \n",
              "4      retaining wall New Jersey highway Direct Conne...   \n",
              "...                                                  ...   \n",
              "37417  Lil Tjay Steady Calling My Phone Calling My Ph...   \n",
              "37418  NBA G League Basketball game-0022000187 Lakers...   \n",
              "37419  MAMAMOO  WAW  WAW MAMAMOO WAW Where Are We Now...   \n",
              "37420  master masterpodcast lord lord vinheteiro z z ...   \n",
              "37421  Spurs Tottenham Hotspur   Tottenham Leicester ...   \n",
              "\n",
              "                                             description  \\\n",
              "0      Enjoy the best skills and goals from Jadon San...   \n",
              "1      Watch the the official video for Migos - \"Aval...   \n",
              "2      Hannah Waddingham wins the Emmy for Supporting...   \n",
              "3      JO1'YOUNG (JO1 ver.)' PERFORMANCE VIDEO\\n\\n---...   \n",
              "4      One of the most important (and innocuous) part...   \n",
              "...                                                  ...   \n",
              "37417  Official video for \"Calling My Phone\" by Lil T...   \n",
              "37418  PELICANS at LAKERS | FULL GAME HIGHLIGHTS | Ja...   \n",
              "37419  [MV]  (MAMAMOO) - Where Are We Now\\n\\nInstagra...   \n",
              "37420  DOCTOR HAIR\\nhttps://www.thedoctorhair.com/?fb...   \n",
              "37421  Two minute highlights from Tottenham Hotspur's...   \n",
              "\n",
              "                                                comments  \n",
              "0      Respect to Dortmund fans,must be sad losing hi...  \n",
              "1      Migos just makes me want to live my live to th...  \n",
              "2      Hannah's energy bursts through any screen. Wel...  \n",
              "3      youngVer><  REN is really PERFECT. It's not ju...  \n",
              "4       Keep up with all my projects here: https://pr...  \n",
              "...                                                  ...  \n",
              "37417  'DESTINED 2 WIN' OUT NOW !! https://liltjay.ln...  \n",
              "37418  Montrezl Harrell is going crazy with the rebou...  \n",
              "37419  I honestly do not know why this song hit so ha...  \n",
              "37420  Foi um prazer passar esta tarde com vocs debat...  \n",
              "37421  Thank you Kane for everything you have given t...  \n",
              "\n",
              "[37422 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52bb4aca-2c27-4f59-96e4-ce3efd57bf78\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>video_id</th>\n",
              "      <th>title</th>\n",
              "      <th>channel_id</th>\n",
              "      <th>channel_title</th>\n",
              "      <th>published_at</th>\n",
              "      <th>view_count</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment_count</th>\n",
              "      <th>tags</th>\n",
              "      <th>description</th>\n",
              "      <th>comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>--0bCF-iK2E</td>\n",
              "      <td>Jadon Sancho  Magical Skills &amp; Goals</td>\n",
              "      <td>UC6UL29enLNe4mqwTfAyeNuw</td>\n",
              "      <td>Bundesliga</td>\n",
              "      <td>2021-07-01 10:00:00</td>\n",
              "      <td>1048888</td>\n",
              "      <td>19515</td>\n",
              "      <td>226</td>\n",
              "      <td>1319</td>\n",
              "      <td>football soccer ftbol alemn Bundesliga season ...</td>\n",
              "      <td>Enjoy the best skills and goals from Jadon San...</td>\n",
              "      <td>Respect to Dortmund fans,must be sad losing hi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>--14w5SOEUs</td>\n",
              "      <td>Migos - Avalanche (Official Video)</td>\n",
              "      <td>UCGIelM2Dj3zza3xyV3pL3WQ</td>\n",
              "      <td>MigosVEVO</td>\n",
              "      <td>2021-06-10 16:00:00</td>\n",
              "      <td>15352638</td>\n",
              "      <td>359277</td>\n",
              "      <td>7479</td>\n",
              "      <td>18729</td>\n",
              "      <td>Migos Avalanche Quality Control Music/Motown R...</td>\n",
              "      <td>Watch the the official video for Migos - \"Aval...</td>\n",
              "      <td>Migos just makes me want to live my live to th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>--40TEbZ9Is</td>\n",
              "      <td>Supporting Actress in a Comedy: 73rd Emmys</td>\n",
              "      <td>UClBKH8yZRcM4AsRjDVEdjMg</td>\n",
              "      <td>Television Academy</td>\n",
              "      <td>2021-09-20 01:03:32</td>\n",
              "      <td>925281</td>\n",
              "      <td>11212</td>\n",
              "      <td>401</td>\n",
              "      <td>831</td>\n",
              "      <td></td>\n",
              "      <td>Hannah Waddingham wins the Emmy for Supporting...</td>\n",
              "      <td>Hannah's energy bursts through any screen. Wel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>--4tfbSyYDE</td>\n",
              "      <td>JO1'YOUNG (JO1 ver.)' PERFORMANCE VIDEO</td>\n",
              "      <td>UCsmXiDP8S40uBeJYxvyulmA</td>\n",
              "      <td>JO1</td>\n",
              "      <td>2021-03-03 10:00:17</td>\n",
              "      <td>2641597</td>\n",
              "      <td>39131</td>\n",
              "      <td>441</td>\n",
              "      <td>3745</td>\n",
              "      <td>PRODUCE101JAPAN              JO1   TheSTAR STA...</td>\n",
              "      <td>JO1'YOUNG (JO1 ver.)' PERFORMANCE VIDEO\\n\\n---...</td>\n",
              "      <td>youngVer&gt;&lt;  REN is really PERFECT. It's not ju...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>--DKkzWVh-E</td>\n",
              "      <td>Why Retaining Walls Collapse</td>\n",
              "      <td>UCMOqf8ab-42UUQIdVoKwjlQ</td>\n",
              "      <td>Practical Engineering</td>\n",
              "      <td>2021-12-07 13:00:00</td>\n",
              "      <td>715724</td>\n",
              "      <td>32887</td>\n",
              "      <td>367</td>\n",
              "      <td>1067</td>\n",
              "      <td>retaining wall New Jersey highway Direct Conne...</td>\n",
              "      <td>One of the most important (and innocuous) part...</td>\n",
              "      <td>Keep up with all my projects here: https://pr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37417</th>\n",
              "      <td>zzd4ydafGR0</td>\n",
              "      <td>Lil Tjay - Calling My Phone (feat. 6LACK) [Off...</td>\n",
              "      <td>UCEB4a5o_6KfjxHwNMnmj54Q</td>\n",
              "      <td>Lil Tjay</td>\n",
              "      <td>2021-02-12 05:03:49</td>\n",
              "      <td>120408275</td>\n",
              "      <td>2180780</td>\n",
              "      <td>35871</td>\n",
              "      <td>81360</td>\n",
              "      <td>Lil Tjay Steady Calling My Phone Calling My Ph...</td>\n",
              "      <td>Official video for \"Calling My Phone\" by Lil T...</td>\n",
              "      <td>'DESTINED 2 WIN' OUT NOW !! https://liltjay.ln...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37418</th>\n",
              "      <td>zziBybeSAtw</td>\n",
              "      <td>PELICANS at LAKERS | FULL GAME HIGHLIGHTS | Ja...</td>\n",
              "      <td>UCWJ2lWNubArHWmf3FIHbfcQ</td>\n",
              "      <td>NBA</td>\n",
              "      <td>2021-01-16 05:39:05</td>\n",
              "      <td>2841917</td>\n",
              "      <td>20759</td>\n",
              "      <td>1049</td>\n",
              "      <td>2624</td>\n",
              "      <td>NBA G League Basketball game-0022000187 Lakers...</td>\n",
              "      <td>PELICANS at LAKERS | FULL GAME HIGHLIGHTS | Ja...</td>\n",
              "      <td>Montrezl Harrell is going crazy with the rebou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37419</th>\n",
              "      <td>zzk09ESX7e0</td>\n",
              "      <td>[MV]  (MAMAMOO) - Where Are We Now</td>\n",
              "      <td>UCuhAUMLzJxlP1W7mEk0_6lA</td>\n",
              "      <td>MAMAMOO</td>\n",
              "      <td>2021-06-02 09:00:10</td>\n",
              "      <td>13346678</td>\n",
              "      <td>720854</td>\n",
              "      <td>4426</td>\n",
              "      <td>90616</td>\n",
              "      <td>MAMAMOO  WAW  WAW MAMAMOO WAW Where Are We Now...</td>\n",
              "      <td>[MV]  (MAMAMOO) - Where Are We Now\\n\\nInstagra...</td>\n",
              "      <td>I honestly do not know why this song hit so ha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37420</th>\n",
              "      <td>zzmQEb0Em5I</td>\n",
              "      <td>FELLIPE ESCUDERO- Master Podcast  #12</td>\n",
              "      <td>UC8NjnNWMsRqq11NYvHAQb1g</td>\n",
              "      <td>Master Podcast</td>\n",
              "      <td>2020-10-20 20:59:30</td>\n",
              "      <td>252057</td>\n",
              "      <td>19198</td>\n",
              "      <td>1234</td>\n",
              "      <td>1471</td>\n",
              "      <td>master masterpodcast lord lord vinheteiro z z ...</td>\n",
              "      <td>DOCTOR HAIR\\nhttps://www.thedoctorhair.com/?fb...</td>\n",
              "      <td>Foi um prazer passar esta tarde com vocs debat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37421</th>\n",
              "      <td>zzxPZwaA-8w</td>\n",
              "      <td>Gareth Bale brace secures dramatic comeback on...</td>\n",
              "      <td>UCEg25rdRZXg32iwai6N6l0w</td>\n",
              "      <td>Tottenham Hotspur</td>\n",
              "      <td>2021-05-23 21:00:31</td>\n",
              "      <td>2252090</td>\n",
              "      <td>34063</td>\n",
              "      <td>868</td>\n",
              "      <td>2004</td>\n",
              "      <td>Spurs Tottenham Hotspur   Tottenham Leicester ...</td>\n",
              "      <td>Two minute highlights from Tottenham Hotspur's...</td>\n",
              "      <td>Thank you Kane for everything you have given t...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37422 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52bb4aca-2c27-4f59-96e4-ce3efd57bf78')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-52bb4aca-2c27-4f59-96e4-ce3efd57bf78 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-52bb4aca-2c27-4f59-96e4-ce3efd57bf78');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-YCumn7_Ffl"
      },
      "source": [
        "**Installing necessary packages:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s72vsWEVqM6l",
        "outputId": "c8927bcc-dc38-491e-ecc0-d93942dc9c53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting texthero\n",
            "  Using cached texthero-1.1.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: tqdm>=4.3 in /usr/local/lib/python3.7/dist-packages (from texthero) (4.63.0)\n",
            "Requirement already satisfied: spacy<3.0.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (2.2.4)\n",
            "Collecting nltk>=3.3\n",
            "  Using cached nltk-3.7-py3-none-any.whl (1.5 MB)\n",
            "Collecting unidecode>=1.1.1\n",
            "  Using cached Unidecode-1.3.4-py3-none-any.whl (235 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from texthero) (1.21.5)\n",
            "Requirement already satisfied: gensim<4.0,>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (3.6.0)\n",
            "Requirement already satisfied: wordcloud>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (1.5.0)\n",
            "Requirement already satisfied: pandas>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from texthero) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (3.2.2)\n",
            "Requirement already satisfied: plotly>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (5.5.0)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from texthero) (1.0.2)\n",
            "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim<4.0,>=3.6.0->texthero) (5.2.1)\n",
            "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim<4.0,>=3.6.0->texthero) (1.15.0)\n",
            "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim<4.0,>=3.6.0->texthero) (1.4.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.0->texthero) (1.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.0->texthero) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.0->texthero) (3.0.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.0->texthero) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.1.0->texthero) (3.10.0.2)\n",
            "Collecting regex>=2021.8.3\n",
            "  Using cached regex-2022.3.15-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk>=3.3->texthero) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk>=3.3->texthero) (1.1.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.2->texthero) (2018.9)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly>=4.2.0->texthero) (8.0.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->texthero) (3.1.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (3.0.6)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (1.0.0)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (7.4.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (2.23.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (0.4.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (0.9.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (1.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (57.4.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (2.0.6)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (1.0.6)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0->texthero) (1.1.3)\n",
            "Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy<3.0.0->texthero) (4.11.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy<3.0.0->texthero) (3.7.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0->texthero) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0->texthero) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0->texthero) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.0.0->texthero) (2.10)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud>=1.5.0->texthero) (7.1.2)\n",
            "Installing collected packages: regex, unidecode, nltk, texthero\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2019.12.20\n",
            "    Uninstalling regex-2019.12.20:\n",
            "      Successfully uninstalled regex-2019.12.20\n",
            "  Attempting uninstall: nltk\n",
            "    Found existing installation: nltk 3.2.5\n",
            "    Uninstalling nltk-3.2.5:\n",
            "      Successfully uninstalled nltk-3.2.5\n",
            "Successfully installed nltk-3.7 regex-2022.3.15 texthero-1.1.0 unidecode-1.3.4\n",
            "Collecting catboost\n",
            "  Downloading catboost-1.0.4-cp37-none-manylinux1_x86_64.whl (76.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 76.1 MB 54 kB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.3.5)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.21.5)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (3.0.7)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (3.10.0.2)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (8.0.1)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.0.4\n",
            "Collecting shap\n",
            "  Downloading shap-0.40.0-cp37-cp37m-manylinux2010_x86_64.whl (564 kB)\n",
            "\u001b[K     |████████████████████████████████| 564 kB 5.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.21.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from shap) (1.0.2)\n",
            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.5)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap) (21.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.4.1)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap) (4.63.0)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap) (0.51.2)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>20.9->shap) (3.0.7)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap) (57.4.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (3.1.0)\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.40.0 slicer-0.0.7\n"
          ]
        }
      ],
      "source": [
        "!pip install texthero\n",
        "!pip install catboost\n",
        "!pip install shap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqn6TBwTuNYe"
      },
      "source": [
        "**Cleaning texts:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "Brc9ZMW0c_Es",
        "outputId": "ad40de7e-66ee-4f9a-9782-c729466c2a90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   title  view_count    likes  \\\n",
              "0                      jadon sancho magical skills goals     1048888    19515   \n",
              "1                         migos avalanche official video    15352638   359277   \n",
              "2                   supporting actress comedy 73rd emmys      925281    11212   \n",
              "3                    jo1 young jo1 ver performance video     2641597    39131   \n",
              "4                               retaining walls collapse      715724    32887   \n",
              "...                                                  ...         ...      ...   \n",
              "37417   lil tjay calling phone feat 6lack official video   120408275  2180780   \n",
              "37418       pelicans lakers full game highlights january     2841917    20759   \n",
              "37419                                         mv mamamoo    13346678   720854   \n",
              "37420                    fellipe escudero master podcast      252057    19198   \n",
              "37421  gareth bale brace secures dramatic comeback fi...     2252090    34063   \n",
              "\n",
              "       dislikes  comment_count  \\\n",
              "0           226           1319   \n",
              "1          7479          18729   \n",
              "2           401            831   \n",
              "3           441           3745   \n",
              "4           367           1067   \n",
              "...         ...            ...   \n",
              "37417     35871          81360   \n",
              "37418      1049           2624   \n",
              "37419      4426          90616   \n",
              "37420      1234           1471   \n",
              "37421       868           2004   \n",
              "\n",
              "                                                    tags  \n",
              "0      football soccer ftbol alemn bundesliga season ...  \n",
              "1      migos avalanche quality control music motown r...  \n",
              "2                                                         \n",
              "3      produce101japan jo1 thestar stargazer protosta...  \n",
              "4      retaining wall new jersey highway direct conne...  \n",
              "...                                                  ...  \n",
              "37417  lil tjay steady calling phone calling phone ti...  \n",
              "37418  nba g league basketball game lakers pelicans p...  \n",
              "37419              mamamoo waw waw mamamoo waw wawn wawn  \n",
              "37420  master masterpodcast lord lord vinheteiro z z ...  \n",
              "37421  spurs tottenham hotspur tottenham leicester sp...  \n",
              "\n",
              "[37422 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c054c6f-b78a-49d0-9bda-4fda6a5a1168\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>title</th>\n",
              "      <th>view_count</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment_count</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>jadon sancho magical skills goals</td>\n",
              "      <td>1048888</td>\n",
              "      <td>19515</td>\n",
              "      <td>226</td>\n",
              "      <td>1319</td>\n",
              "      <td>football soccer ftbol alemn bundesliga season ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>migos avalanche official video</td>\n",
              "      <td>15352638</td>\n",
              "      <td>359277</td>\n",
              "      <td>7479</td>\n",
              "      <td>18729</td>\n",
              "      <td>migos avalanche quality control music motown r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>supporting actress comedy 73rd emmys</td>\n",
              "      <td>925281</td>\n",
              "      <td>11212</td>\n",
              "      <td>401</td>\n",
              "      <td>831</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>jo1 young jo1 ver performance video</td>\n",
              "      <td>2641597</td>\n",
              "      <td>39131</td>\n",
              "      <td>441</td>\n",
              "      <td>3745</td>\n",
              "      <td>produce101japan jo1 thestar stargazer protosta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>retaining walls collapse</td>\n",
              "      <td>715724</td>\n",
              "      <td>32887</td>\n",
              "      <td>367</td>\n",
              "      <td>1067</td>\n",
              "      <td>retaining wall new jersey highway direct conne...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37417</th>\n",
              "      <td>lil tjay calling phone feat 6lack official video</td>\n",
              "      <td>120408275</td>\n",
              "      <td>2180780</td>\n",
              "      <td>35871</td>\n",
              "      <td>81360</td>\n",
              "      <td>lil tjay steady calling phone calling phone ti...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37418</th>\n",
              "      <td>pelicans lakers full game highlights january</td>\n",
              "      <td>2841917</td>\n",
              "      <td>20759</td>\n",
              "      <td>1049</td>\n",
              "      <td>2624</td>\n",
              "      <td>nba g league basketball game lakers pelicans p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37419</th>\n",
              "      <td>mv mamamoo</td>\n",
              "      <td>13346678</td>\n",
              "      <td>720854</td>\n",
              "      <td>4426</td>\n",
              "      <td>90616</td>\n",
              "      <td>mamamoo waw waw mamamoo waw wawn wawn</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37420</th>\n",
              "      <td>fellipe escudero master podcast</td>\n",
              "      <td>252057</td>\n",
              "      <td>19198</td>\n",
              "      <td>1234</td>\n",
              "      <td>1471</td>\n",
              "      <td>master masterpodcast lord lord vinheteiro z z ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37421</th>\n",
              "      <td>gareth bale brace secures dramatic comeback fi...</td>\n",
              "      <td>2252090</td>\n",
              "      <td>34063</td>\n",
              "      <td>868</td>\n",
              "      <td>2004</td>\n",
              "      <td>spurs tottenham hotspur tottenham leicester sp...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37422 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c054c6f-b78a-49d0-9bda-4fda6a5a1168')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c054c6f-b78a-49d0-9bda-4fda6a5a1168 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c054c6f-b78a-49d0-9bda-4fda6a5a1168');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "import texthero as hero\n",
        "from texthero import preprocessing\n",
        "\n",
        "clean_df = df.drop(['video_id','published_at','channel_id','channel_title','description','comments'],axis=1)\n",
        "\n",
        "clean_df['title'] = clean_df['title'].pipe(hero.clean,[preprocessing.remove_urls , preprocessing.clean])\n",
        "clean_df['tags'] = clean_df['tags'].pipe(hero.clean,[preprocessing.remove_urls , preprocessing.clean])\n",
        "clean_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xDXcj1oagl1"
      },
      "source": [
        "**Creating bag-of-words:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEBmRlC4yYJw",
        "outputId": "de082759-af62-45eb-9f41-f040380c8bf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top repeated words for title: ['de' 'highlights' 'minecraft' 'music' 'new' 'official' 'shorts' 'trailer'\n",
            " 'video' 'vs']\n",
            "Top repeated words for tags: ['football' 'funny' 'highlights' 'league' 'minecraft' 'new' 'news'\n",
            " 'united' 'video' 'vs']\n"
          ]
        }
      ],
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "bag_of_words_df = pd.DataFrame()\n",
        "\n",
        "TOP_N_WORDS = 10\n",
        "\n",
        "for text_column in ['title','tags']:\n",
        "  vec = CountVectorizer(max_features = TOP_N_WORDS)\n",
        "  txt_to_fts = vec.fit_transform(clean_df[text_column]).toarray()\n",
        "  txt_to_fts.shape\n",
        "\n",
        "  names = vec.get_feature_names_out()\n",
        "  print(\"Top repeated words for {}:\".format(text_column),names)\n",
        "\n",
        "  txt_fts_names = [text_column + f'_word_{i}_count' for i in range(TOP_N_WORDS)]\n",
        "  bag_of_words_df[txt_fts_names] = txt_to_fts\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-xFYcWjwtJj"
      },
      "source": [
        "**Merging data:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YHCusrgUyH1t"
      },
      "outputs": [],
      "source": [
        "merged_df = pd.concat([clean_df,bag_of_words_df],axis=1).drop([\"tags\",\"title\"],axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "45cZpEbXxHW2"
      },
      "source": [
        "**Specify input variables (X) and an output variable (y):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "d5j9XyH_kczL"
      },
      "outputs": [],
      "source": [
        "X = merged_df.drop(['dislikes'],axis=1)\n",
        "y = merged_df['dislikes']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0CqFLkkxRPJ"
      },
      "source": [
        "**Traning and testing our model by K-fold cross-validation method:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "T2SNXBFBUSE1"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from catboost import CatBoostRegressor\n",
        "\n",
        "folds = 5\n",
        "\n",
        "regressor = CatBoostRegressor(silent=True)\n",
        "\n",
        "pipeline = Pipeline([ ('estimator', regressor)])\n",
        "\n",
        "cv = KFold(n_splits=folds)\n",
        "scores = cross_val_score(pipeline, X, y,cv = cv , scoring='neg_mean_absolute_error')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jb3Zdwnkxk4k"
      },
      "source": [
        "# Results:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fy1wc9rYRWhX",
        "outputId": "7ce9988a-5316-404e-d0dc-f7e00c39a38b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-2502.5  -2940.65 -2894.39 -2731.46 -2470.32]\n"
          ]
        }
      ],
      "source": [
        "print(scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDSgiOi0vg94",
        "outputId": "50a48efc-daa8-4aab-96af-10cde4d88dfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-2707.864441259917\n"
          ]
        }
      ],
      "source": [
        "print(scores.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "-uvXIfPiCZ8F"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEM_hdGVhm_D",
        "outputId": "a3cb29bf-b0d2-4e47-d5d7-460d7c3e5b25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostRegressor at 0x7f5da31c6990>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "cat_regressor = CatBoostRegressor(silent=True)\n",
        "cat_regressor.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5nyiSN8Tyv-m"
      },
      "source": [
        "**The impact of each feature on the model output:**\n",
        "\n",
        "*For having a cleaner plot, we remove outliers using Z-scores.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "oEHck-mqPfYH"
      },
      "outputs": [],
      "source": [
        "import scipy.stats as stats\n",
        "z_scores = stats.zscore(X_train)\n",
        "abs_z_scores = np.abs(z_scores)\n",
        "filtered_entries = (abs_z_scores < 1).all(axis=1)\n",
        "new_X = X_train[filtered_entries]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "LXukO5bZdMHw"
      },
      "outputs": [],
      "source": [
        "import shap\n",
        "explainer = shap.TreeExplainer(cat_regressor)\n",
        "shap_values = explainer.shap_values(new_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "kSMHagsGdO6-",
        "outputId": "7170b6fe-d206-4b09-c6e8-9a6b2e76807a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x684 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "shap.summary_plot(shap_values, new_X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z29AvOH3yVjH"
      },
      "source": [
        "**The relationship between MAE and video dislike quantity:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "DmMps7hhl2wA"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "errors = []\n",
        "ys = []\n",
        "for i in range (np.min(y_test)+1,np.max(y_test),1000):\n",
        "  ys.append(i)\n",
        "  slected_y_test = y_test[y_test<i]\n",
        "  slected_x_test = X_test[y_test<i]\n",
        "# Predicting the Test set results\n",
        "  y_pred = cat_regressor.predict(slected_x_test)\n",
        "  np.set_printoptions(precision=2)\n",
        "  errors.append(mean_absolute_error(slected_y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "deUXY--9mUQ1",
        "outputId": "d016f516-c325-413f-bb7e-e18d7c26c441"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4EAAAHgCAYAAAAbnWgaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZxlZWEn/N/T+94NdDdbNzRgI4uASotEdDCuqImYiRvGLUMkmdGJJmbe1yTzRhMzGTN5x0zMqIlG3CbqmMRERlFjcIsYEBBkFbpla5rem9679mf+qNukgV6qm6p7btX9fj+f+vS955x769d9oKp+9TznOaXWGgAAALrDpKYDAAAA0D5KIAAAQBdRAgEAALqIEggAANBFlEAAAIAuogQCAAB0kSlNBxgLCxcurMuWLWs6BgAAQCNuuummTbXWRfvbNyFL4LJly3LjjTc2HQMAAKARpZQHDrTPdFAAAIAuogQCAAB0ESUQAACgiyiBAAAAXUQJBAAA6CJKIAAAQBdRAgEAALqIEggAANBFlEAAAIAuogQCAAB0ESUQAACgiyiBAAAAXUQJBAAA6CJKIAAAQBdRAgEAALqIEggAANBFpjQdAACaVGvN9p6BpmMAMI7NmT4lkyeVpmOMmBIIQNeqteZXPn1jrvnJhqajADCO/dNvXpynLJ7TdIwRUwIB6Fh/+s178uFvrxqz969JBodqLrvgpHH1zRuAzrJwzrSmIxwWJRCAjvW129dm2cLZeenZx47Z5zhu3oxcdsFJmTLZZfIAdAclEICOMDRUc//mXRmqw897BwazcsPOvPOFy/OuF53ebDgAmECUQAA6wpXX3pc//OpdT9h+/slHNZAGACYuJRCAjvDTjbsyb8aU/OEvnPPotllTJ+ei0xY2mAoAJh4lEICOsHFHb05YMDOvPO+EpqMAwITmKngAOsKmnb1ZNHd60zEAYMJTAgHoCBt39GbRHCUQAMaaEghA42qt2WgkEADawjWBAIya79y9IV+5de1hv25wqKZvYEgJBIA2UAIBGDUf/c5Pc8vqrVl4BNM6lx0zKyuWHT0GqQCAfSmBAIyaB7fszivOPT4ffO3Tm44CAByAawIBGBU9/YNZt70nJx09q+koAMBBGAkE4ElZt60nvQODWb1lT2pNTj5GCQSATqYEAnDEvnvPxrzlyh8+ZtspC+c0lAYAGAklEIAjdtMDj2RSSf7bq8/LpJLMmT4l5y2Z33QsAOAglEAAjthda7fnlIWz8+rzlzQdBQAYIQvDAHBEvnvPxnzzzvU54/h5TUcBAA6DEgjAEfnE9+9Lkrx2xdKGkwAAh8N0UACSJD9YtSlfvW3tiI+/7t7N+XcXnZKLT180hqkAgNGmBAKQJPnod3+a6+7dnPkzp47o+IWzp+XSp58wxqkAgNGmBAKQJNm4ozfPf+rifPzNK5qOAgCMIdcEApAk2bSzLwvnTGs6BgAwxpRAADI4VLNlV28WzpnedBQAYIwpgQBk6+6+DNXkmNlGAgFgolMCAcimnX1JkoVzjQQCwERnYRiACWZgcCjv+t+3ZO22nhG/ZmfPQJKYDgoAXUAJBJhgblm9NV+5dW3OXTI/82aM7HYPM6dOzqmLZudpJ84f43QAQNOUQIAO9Hc3PZR/uXfzEb125YadmTyp5LOXP3vE9/wDALqHEgjQgf7smpXZuKM3Rx/hQi2vOX+JAggA7JcSCNCBdvYO5BfPPzF/+Kpzmo4CAEwwVgcF6EA7ewcye7rf0wEAo08JBOgwfQND6RsYylwlEAAYA0ogQIfZ1Tt8uwYjgQDAWFACATrMzlYJnKMEAgBjQAkE6DBKIAAwlpRAgA7zaAmcoQQCAKNPCQToMDt7XBMIAIwdJRCgw+wdCbQ6KAAwFvyEATDGBodq1m3vGfHxa7buSWIkEAAYG37CABhjv/v3t+ULN6w+rNdMKsm8mVPHKBEA0M2UQIBR8NONO/O6v7wuPf2DT9i3s3cgrzjn+Fx8+qIRv9/xC2ZYHRQAGBNj9hNGKWVpks8kOTZJTfKxWuuflVLel+RtSTa2Dv2dWuvVrdf8dpLLkwwm+fVa6zda2y9J8mdJJif5q1rrB8YqN8CRuPnBrdm0szevf9bSJ0zjnDZlUn7t4tMy38geANABxvLXzANJ3l1r/VEpZW6Sm0op32zt+9Na6/+/78GllLOSvD7J2UlOSPJPpZTTW7s/nOTFSR5KckMp5apa651jmB3gsKx5ZPg6vve98uzMmDq54TQAAAc2ZiWw1ro2ydrW4x2llLuSnHiQl1ya5Au11t4k95VSViW5oLVvVa313iQppXyhdawSCHSMNVt3Z9Hc6QogANDx2nKLiFLKsiTPSHJ9a9M7Sim3llKuLKUc1dp2YpJ9V054qLXtQNsf/zmuKKXcWEq5cePGjY/fDTCm1mzdkxMXzGw6BgDAIY35qgOllDlJ/i7Ju2qt20spH03y/gxfJ/j+JP89yb97sp+n1vqxJB9LkhUrVtQn+37A+HXNXevz9s/9KIND7ftS0D9Y83PnHt+2zwcAcKTGtASWUqZmuAD+da31S0lSa12/z/6PJ/lK6+maJEv3efmS1rYcZDvAE3zt9nWZNnlS3njRyW39vD9/3glt/XwAAEdiLFcHLUk+keSuWusH99l+fOt6wST5hSS3tx5fleRzpZQPZnhhmOVJfpikJFleSjklw+Xv9UneMFa5gc63p28wf3vT6vQODO13/z+v3JgLTz0m/88lZ7Q5GQBA5xvLkcCLkrwpyW2llFta234nyWWllKdneDro/Ul+NUlqrXeUUr6Y4QVfBpK8vdY6mCSllHck+UaGbxFxZa31jjHMDXS4v71pdf6/Lx/8y8CLzjy2TWkAAMaXUuvEu3xuxYoV9cYbb2w6BnAErvrxw4/ebuFgx/T0D+bL77hov/snleJG6wBAVyul3FRrXbG/fX5KAjrG/Zt25dc/f/OIjv2tl5yeeTPcfB0A4HApgUBj9vQN5pM/uC89fYNJkjvX7kiSXPPuiw95uwX34wMAODJKINCYf7prff7b1+9OkpQyvO25T1mY0xbNaTAVAMDEpgQCjbln/Y5MnlRy5x+8NNOnGNkDAGiHSU0HALrXPet35ORjZimAAABtZCQQGHM/vG9Lfv3zN2dg6LH39du6u9+tHAAA2kwJBMbExh29+dQP7svAYM1379mY/sGhXPK0455w3GtWLG0gHQBA91ICgTHxxRtX58Pf/mlmTJ2UkpLffvkZefPPLGs6FgBA11MCgTFx84Nbc+qi2fnWu5/fdBQAAPZhYRhgVG3v6c+vffamXLtqU56x9Kim4wAA8DhKIDCqbnlwa75+x7qcumh2XrNiSdNxAAB4HNNBgVG1bltPkuQv3nh+lh49q+E0AAA8npFAYFStbZXAY+fNaDgJAAD7owQCo2rd9j1ZOGdapk3x5QUAoBOZDgocloe37knvwNAB99+3aVeOm28UEACgUymBwIh9956NecuVPzzkcZec/cSbwgMA0BmUQGDErr93c6ZMKvmT15ybknLA4y445eg2pgIA4HAogcBjDA7VPLx1T2odfl5TH3184wOP5PRj5+YXnuHWDwAA45USCDzGH119Vz7x/fsOuP+yC05qYxoAAEabEgg8xg/v25Izj5+Xy597SpKkJCmtmZ+TSsnzli9sLhwAAE+aEgg8qn9wKHev25G3XrQsrz7flE8AgIlICQTSOzCYn/vQ97P6kd3pGxzK2SfMazoSAABjRAkEcvua7Vm5YWdecc7xOW3R7LzozGObjgQAwBhRAmGCeWDzrmzY0XtYr/n67euSJO/9+bOyeJ4bvQMATGRKIEwgO3r689L/8b309A8d9mtPWThbAQQA6AJKIIwTQ0M1O/sGDnrMd+7emJ7+ofz+K8/OaYvmHNb7n7Jo9pOJBwDAOKEEwjjxzv99S/7Pjx8+5HFzpk/JZReclGlTJrUhFQAA440SCB1i087e7Okb3O++3oGhfOOOdbn49EWHvE/fWSfMUwABADggJRA6wB0Pb8srPvT9Qx739p99Si445eg2JAIAYKJSAqEDfOuuDSkl+aNfOCdTJpX9HjNv5tQ8a9lRbU4GAMBEowRCA75994Z84OqfZKjWJMm6bT05+4R5ueyCkxpOBgDARKcEQps9sHlX3vWFWzJtyqRHR/aWHzsn//YZSxpOBgBAN1ACoc1+78t3ZNue/vzRL5yTNzzbyB8AAO1lCUFos3vW78jzli/MZRcsbToKAABdSAmENtrR05+123py4anHpJT9LwADAABjyXRQGAMPb92Tv/juT9PT/9j7/m3fM5AkecriOU3EAgAAJRBG09/e9FCuuWt97ly7PWu39uSYOdOecMzyxXPyzJPc6gEAgGYogTCK/ue3VmbLrr4sWzg7v/dzZ+WFZx7bdCQAAHgMJRBGSa01a7f15M0/c3J+9xVnNR0HAAD2y8IwMEq27OpL78BQjp8/s+koAABwQEogjJK123qSJCcsmNFwEgAAODDTQeEI7e4byE0PPPLo8x+v3pokRgIBAOhoSiAchg3be9LTP5Qkefff3JIb7n/kMfsnTyo56ehZTUQDAIARUQLhEH66cWe+fPOa3L95d6768cOP2fcbLzo9Fz3lmEefHz17Wo6a/cTbQgAAQKdQAuEgNu/szev+8rps2tmb2dMm57ILlmbFyUcnGS58z3/qopRSGk4JAAAjpwTS1QYGh1IPsO/hrXvy7i/+OFt39+Wrv/7cnH3C/LZmAwCAsaAE0rWu+vHDeecXbk49UAts+dV/c6oCCADAhKEE0rW+cfu6HDN7Wt76nGX73T918qS84IzFWX7s3PYGAwCAMaQE0lVWbdiZz/7L/RkYqvneyo158VnH5h0vWN50LAAAaBslkK6xp28wl3/6hqzd1pN5M6Zk1rTJeeV5JzQdCwAA2koJZEK6ZfXWfPLa+x5zvd+67T15YPPufO5tz85zTlvYXDgAAGiQEsiE9Dc3rs5Xbl37hBu3/+rFpyqAAAB0NSWQCWnrnv6cfMysfOvdz286CgAAdJRJTQeAsbB1d1+OmjWt6RgAANBxlEAmpK27+7Ng5tSmYwAAQMdRApmQtu7uz/xZSiAAADyeEsiEtHV3XxbMNB0UAAAeTwlkwukbGMquvsEcZSQQAACewOqgTCgbtvdk5YadSZIFSiAAADyBEsiE0TcwlBd+8LvZ0TOQJFk0d0bDiQAAoPMogUwYd67dnh09A/mPL3hKViw7OheddkzTkQAAoOMogUwIQ0M1f/KNnyRJfunZJ+e4+UYBAQBgfywMw4Rwx8Pbc+2qzZkxdZICCAAAB6EEMiFs2d2XJPnkWy9oOAkAAHS2MSuBpZSlpZRvl1LuLKXcUUp5Z2v70aWUb5ZSVrb+PKq1vZRSPlRKWVVKubWU8sx93ustreNXllLeMlaZGb+27+lPkiya696AAABwMGM5EjiQ5N211rOSXJjk7aWUs5K8J8k1tdblSa5pPU+SlyVZ3vq4IslHk+HSmOS9SZ6d5IIk791bHGGvba0SOG+G20IAAMDBjFkJrLWurbX+qPV4R5K7kpyY5NIkn24d9ukkr2o9vjTJZ+qw65IsKKUcn+SlSb5Za91Sa30kyTeTXDJWuRmftve0SuBMJRAAAA6mLdcEllKWJXlGkuuTHFtrXdvatS7Jsa3HJyZZvc/LHmptO9B2eNT2PQOZNnlSpk9xmSsAABzMmP/EXEqZk+Tvkryr1rp933211pqkjtLnuaKUcmMp5caNGzeOxlsyjmzv6c+8mVNSSmk6CgAAdLQxLYGllKkZLoB/XWv9Umvz+tY0z7T+3NDavibJ0n1evqS17UDbH6PW+rFa64pa64pFixaN7l+Ejrd9T7/rAQEAYATGcnXQkuQTSe6qtX5wn11XJdm7wudbknx5n+1vbq0SemGSba1po99I8pJSylGtBWFe0toGj9q2p9/1gAAAMAJTxvC9L0rypiS3lVJuaW37nSQfSPLFUsrlSR5I8trWvquTvDzJqiS7k/xyktRat5RS3p/khtZxf1Br3TKGuRmHHtndl6NmuT0EAAAcypiVwFrr95Mc6AKtF+7n+Jrk7Qd4ryuTXDl66RjvegcG87+uezD3b9qVgaGh3L5me37jRac3HQsAADreWI4Ewpj5r1f/JJ/6wf2ZP3NqJk8qOf3YOXnrc5Y1HQsAADqeEsi4s2VXX/7XdQ/kdSuW5o9ffW7TcQAAYFxRAhk3BodqBoaG8u2fbMjAUM1lzz6p6UgAADDuKIF0vO/cvSG3r9mWj//zfdm2pz9JcvTsaTn3xPkNJwMAgPFHCaSjbdzRm7d95sb0D9acefy8XPFvTk2SPOOkBZk0yY3hAQDgcCmBdJTf/OIt+dKP1jxh+9W//ryccdxcxQ8AAJ4kJZCO8p27N+a8pQty8emLHt12ysJZOeuEeQ2mAgCAiUMJpGNs2tmbLbv68vaffUouf+4pTccBAIAJaVLTAWCve9btSJI89di5DScBAICJSwmkY9y6ZluS5IzjlUAAABgrpoPSuKGhml19A7l21aactmh2Fs6Z3nQkAACYsJRAGtXTP5jLPn5dbn5wa5LkTRee3HAiAACY2JRAGvWJ79+Xmx/cmrf/7Gk5evb0/Ny5xzcdCQAAJjQlkLb6q3++N5+97oFHn6/b1pPnLV+Y//TSMxpMBQAA3UMJpG1qrfmrf74vM6dNznlL5idJJp1U8ivPO7XhZAAA0D2UQNrin+5cn/d/9c6s296TP3n1uXnNiqVNRwIAgK7kFhG0xf+6/oHs6BnIG559Ul52juv+AACgKUYCGXO7+wbyg59uzhuffXJ+7+fPajoOAAB0NSOBjLl/XrkpfQNDedGZi5uOAgAAXU8JZMz94x3rM3fGlDzrlKObjgIAAF1PCWRM3btxZ758y5pc+vQTMnWy/9wAAKBpfipnzOzpG8x/+OsfZc6MKfn1FyxvOg4AABALwzCG/vxbK3P3+h355FuflcXzZjQdBwAAiJFAxsi3frI+H/nOT/PiM4/N859qQRgAAOgUSiCjbuX6HfnVz96UyZNKfuV5pzYdBwAA2IfpoIy6b/1kQ/oHa374Oy80DRQAADqMkUBG3V1rt+f4+TMUQAAA6EBKIKPurrU7cubx85qOAQAA7IcSyKi67t7NuXv9jpx/8lFNRwEAAPZDCWRUffjbq3L8/Bn55YuWNR0FAADYDyWQUbG9pz9XfObG/PPKTXnN+Usya5o1hwAAoBP5SZ0nrXdgMK/7y+uycv2O/OIzl+TNz1nWdCQAAOAAlECelA9+85587ba1WblhZz72pvPzkrOPazoSAABwEKaDcsS+ffeGfOialZk1bXLe/eLTFUAAABgHjARyxP7xjnWZN2NK/vbfPydTJ/t9AgAAjAdKICPWOzCY29dsz+6+gdy/eXe+c/fGXHDK0QogAACMI0ogI1JrzeWfujHfX7XpMdvf+cLlDSUCAACOhBLIQW3d3ZfLPn591m7bk627+/Mfnn9aLj59UU48ambmz5yauTOmNh0RAAA4DEogB7RhR0/e8PHrs2rDzrzm/CU564R5edOFJ2eK6Z8AADBuKYEc0KeuvT+rNuzMGy88KX/4qnOajgMAAIwCQzrs17bd/fnijavzgjMWK4AAADCBKIHs159/a2Ue2d2f33jR6U1HAQAARpESyBP0Dw7lH25ZkxefeWzOWTK/6TgAAMAock0gj/HD+7bk6tvWZtPOvrzuWUubjgMAAIwyJZAkyd3rduTrt6/L/7jmntSaPG/5wjz/qYuajgUAAIwyJZDcs35HXvXha7OnfzBPX7ogf37ZM3LCgpkppTQdDQAAGGVKIPnwt1dlyqSSr73zeTnjuLnKHwAATGAWhuly67f35Ku3rs1rn7U0Zx4/TwEEAIAJTgnscn99/YMZrDVv/pmTm44CAAC0gRLYxXoHBvO56x/IC566OCcfM7vpOAAAQBu4JrBLrVy/Ix/97k+zaWdfLn/uKU3HAQAA2kQJ7EK3r9mWV/7P72eoJlf8m1PznKcsbDoSAADQJkpgl6m15vf/zx2ZPX1KPv+2C/O0E+c3HQkAAGgj1wR2mZtXb80N9z+S//eSMxRAAADoQkpgl3l4654kyQWnHN1wEgAAoAlKYJfZtKM3SXLM7GkNJwEAAJqgBHaZzbv6MqkkC2YpgQAA0I2UwC6zaWdfjp49PZMnlaajAAAADVACu8zmnb1ZOMcoIAAAdCslsMts2tmbY5RAAADoWkpgl1m/vTeL5kxvOgYAANAQJbCLbNvTnzVb9+T04+Y2HQUAAGiIEthFfrJ2e5LkrOPnNZwEAABoypSmA9AeH/veT/NHV/8kiRIIAADdTAnsAves35EPfO0nOWXh7PzK807J4nkzmo4EAAA05KDTQUspBxwyKqWcdIjXXllK2VBKuX2fbe8rpawppdzS+nj5Pvt+u5SyqpRydynlpftsv6S1bVUp5T0j+2uxr09ee3+mTZmUL/375+SXnn1y03EAAIAGHeqawO/sfVBKueZx+/7hEK/9VJJL9rP9T2utT299XN1677OSvD7J2a3XfKSUMrmUMjnJh5O8LMlZSS5rHcsIbdvTn3+4eU0uPe/EHDXbrSEAAKDbHWo6aNnn8dEH2fcEtdbvlVKWjTDHpUm+UGvtTXJfKWVVkgta+1bVWu9NklLKF1rH3jnC9+16n/2X+7OnfzBv+hkjgAAAwKFHAusBHu/v+Ui9o5Rya2u66FGtbScmWb3PMQ+1th1oOyPw4Obd+bNrVuaSs4/L006c33QcAACgAxxqJHBxKeU3Mzzqt/dxWs8XHcHn+2iS92e4QL4/yX9P8u+O4H2eoJRyRZIrkuSkkw56uWLX+Mh3VqWUkt+/9OymowAAAB3iUCOBH08yN8mcfR7vff5Xh/vJaq3ra62Dtdah1vvtnfK5JsnSfQ5d0tp2oO37e++P1VpX1FpXLFp0JP10YlmzdU/+7kcP5fXPWppjrQYKAAC0HHQksNb6+wfaV0p51uF+slLK8bXWta2nv5Bk78qhVyX5XCnlg0lOSLI8yQ8zPOK4vJRySobL3+uTvOFwP283uuau9ekfrPnli05pOgoAANBBDus+ga2VOS9rfWxNsuIgx34+yfOTLCylPJTkvUmeX0p5eoang96f5FeTpNZ6Rynlixle8GUgydtrrYOt93lHkm8kmZzkylrrHYeTuVvd8uDWLJwzPcuOmdV0FAAAoIMcsgS2VvjcW/z6k5ycZEWt9f6Dva7Wetl+Nn/iIMf/lyT/ZT/br05y9aFy8q/Wb+/J9fdtydOXLkgpB13EFQAA6DKHuln8vyT5aobL4i/WWs9PsuNQBZDmPLh5d57zgW9lzdY9efX5S5qOAwAAdJhDLQyzPsMLwRybf10N9EhvDUEbfOW2hzM4VPPhNzwzlzztuKbjAAAAHeagJbDW+qok5yS5Kcn7Sin3JTmqlHLBwV5Hc75++7qct3RBXnHu8U1HAQAAOtChRgJTa91Wa/1krfUlSS5M8ntJ/rSUsvoQL6XNHnpkd259aFteZgQQAAA4gEOWwH217vP357XWi5I8d4wycQRqrfndv789kyeVvOIco4AAAMD+HXR10FLKVYd4/StHMQtPwjfvXJ/v3rMx7/35s7L0aLeFAAAA9u9Qt4j4mSSrk3w+yfUZvnk7HWbzzt6850u3ZfniOXnThSc3HQcAAOhghyqBxyV5cYbvEfiGDN8u4vNu2N5ZvnDD6mzZ1ZfPve3ZmTL5sGb4AgAAXeZQq4MO1lq/Xmt9S4YXhVmV5DullHe0JR2HNDhU87nrH8xzTjsmZxw3r+k4AABAhzvUSGBKKdOTvCLDo4HLknwoyd+PbSxG6jt3b8iarXvyu684s+koAADAOHCohWE+k+RpSa5O8vu11tvbkooR++x1D2Tx3Ol58VnHNh0FAAAYBw51AdkbkyxP8s4kPyilbG997CilbB/7eBzMll19+e49G/OaFUsy1bWAAADACBx0JLDWqll0sGtXbUqtyYvONAoIAACMjJI3jv3TXeszb8aUnLtkQdNRAACAcUIJHKe27OrL125bl1c948RMnuT2jQAAwMgogePU39y4On2DQ3mjm8MDAACHQQkcp/7+5jV51rKjcvqxc5uOAgAAjCNK4Di0q3cg96zfkeectrDpKAAAwDijBI5Dd67dnqGanLtkftNRAACAcUYJHGdqrfnL7/40pSTnKIEAAMBhUgLHmbvX78g/3bUh73rh6Vk8d0bTcQAAgHFGCRxnvr9yU5Lk1SuWNJwEAAAYj5TAcebq29bmKYvn5MQFM5uOAgAAjENK4Dhy3b2b86MHt+aXnn1S01EAAIBxSgkcR/7r1XdlyVEz87pnLW06CgAAME4pgeNE/+BQ7nh4e37+vBMya9qUpuMAAADjlBI4TqzesjsDQzWnLpzddBQAAGAcUwLHiXs37kqSnLpoTsNJAACA8UwJHCfu2bAjSXLaIiOBAADAkVMCx4nv3bMxZxw3NwtmTWs6CgAAMI4pgePAtj39ueH+R/KCMxY3HQUAABjnlMBx4Hv3bMzgUM0Lz1QCAQCAJ0cJHAe+e8/GHDVrap6+9KimowAAAOOcEjgOPLh5d04/dm4mTypNRwEAAMY5JXAc2LCjJ4vnzWg6BgAAMAEogR2u1poNO3qzeO70pqMAAAATgBLY4Xb2DmR336ASCAAAjAolsMNt2NGbJFk8TwkEAACePCWww63b1pMkWTzXNYEAAMCTpwR2uM//8MHMmjY5Z58wr+koAADABKAEdrDNO3vztdvX5ZeefVIWzJrWdBwAAGACUAI72FduXZvBoZp/+8wlTUcBAAAmCCWwQw0O1Xzy2vty7pL5OeO4uU3HAQAAJgglsEN94451uX/z7vzaxaellNJ0HAAAYIJQAjvU53/4YJYePTMvPfu4pqMAAAATiBLYgQaHam5+cGsuPn1RJk8yCggAAIweJbADrdqwMzt7B/KMpUc1HQUAAJhglMAOdNfa7UmSc5bMbzgJAAAw0SiBHeihR3YnSU46elbDSQAAgIlGCexADz2yJwvnTM+MqZObjgIAAEwwSmAHeuiRPTnxqJlNxwAAACYgJbADrRsQdyUAABS9SURBVNm6J0uUQAAAYAwogR1mT99gHtyyO6ccM7vpKAAAwASkBHaYW1ZvzeBQzTNPXtB0FAAAYAJSAjvMTQ9sSZI88yT3CAQAAEafEthhvvWTDXnaifOyYNa0pqMAAAATkBLYQbbs6svNq7fmxWce13QUAABgglICO8iDW3an1uRpJ85rOgoAADBBKYEdZOOO3iTJornTG04CAABMVEpgB9m0c7gELpyjBAIAAGNDCewge0cCj5ljURgAAGBsKIEdZNPO3syfOTXTp0xuOgoAADBBKYEdZOOOXtcDAgAAY0oJ7CDrtvdksRIIAACMISWwQ9Ras2rDzpy2aE7TUQAAgAlszEpgKeXKUsqGUsrt+2w7upTyzVLKytafR7W2l1LKh0opq0opt5ZSnrnPa97SOn5lKeUtY5W3aRt29GZHz0CWH6sEAgAAY2csRwI/leSSx217T5Jraq3Lk1zTep4kL0uyvPVxRZKPJsOlMcl7kzw7yQVJ3ru3OE40K9fvTJI8ZbESCAAAjJ0xK4G11u8l2fK4zZcm+XTr8aeTvGqf7Z+pw65LsqCUcnySlyb5Zq11S631kSTfzBOL5YSwcsOOJMnyxXMbTgIAAExk7b4m8Nha69rW43VJjm09PjHJ6n2Oe6i17UDbJ5xVG3ZmwaypWegegQAAwBhqbGGYWmtNUkfr/UopV5RSbiyl3Lhx48bRetu2WblhZ5YvnpNSStNRAACACazdJXB9a5pnWn9uaG1fk2TpPsctaW070PYnqLV+rNa6ota6YtGiRaMefCzVWrNy/Q7XAwIAAGOu3SXwqiR7V/h8S5Iv77P9za1VQi9Msq01bfQbSV5SSjmqtSDMS1rbJpQ1W/fkkd39Oev4eU1HAQAAJrgpY/XGpZTPJ3l+koWllIcyvMrnB5J8sZRyeZIHkry2dfjVSV6eZFWS3Ul+OUlqrVtKKe9PckPruD+otT5+sZlx78ertyVJzlu6oOEkAADARDdmJbDWetkBdr1wP8fWJG8/wPtcmeTKUYzWcW5dszXTJk/KGccZCQQAAMZWYwvD8K9Wrd+ZUxfNzrQpTgcAADC2tI4OcO+mXTl10eymYwAAAF1ACWxY38BQHtyyO6cutDIoAAAw9pTAhj24ZXcGh2pOWWgkEAAAGHtKYMPu27QrSUwHBQAA2kIJbNi9G3cmiemgAABAWyiBDbt3464cM3ta5s+a2nQUAACgCyiBDbtv0y7XAwIAAG2jBDZsw46eHL9gZtMxAACALqEENmzzzr4cM3ta0zEAAIAuoQQ2qKd/MDt6B7JwjhIIAAC0hxLYoC27+pIkx8yZ3nASAACgWyiBDXq0BJoOCgAAtIkS2KBNO3uTGAkEAADaRwls0OadRgIBAID2UgIbtGbrnpSSHDd/RtNRAACALqEENuj+zbty/LwZmTF1ctNRAACALqEENuiBzbtz0jGzmo4BAAB0ESWwQQ9s3pVlx8xuOgYAANBFlMCG9A4MZtPOvpy4YGbTUQAAgC6iBDZk257+JMkCK4MCAABtpAQ2ZNvuVgmcObXhJAAAQDdRAhuydyRwvhIIAAC0kRLYkK17RwJnKYEAAED7KIENMRIIAAA0QQlsyNa9C8PMtDAMAADQPkpgQ7bt7kspydwZU5qOAgAAdBElsCHb9vRn3oypmTSpNB0FAADoIkpgQ7bu6bcoDAAA0HZKYEO27em3KAwAANB2SmBDtu5WAgEAgPZTAhuy3UggAADQACWwIa4JBAAAmqAENqDW6ppAAACgEUpgA3b2DmRwqLpRPAAA0HZKYAO27u5Pksw3HRQAAGgzJbAB2/a0SqDpoAAAQJspgQ3YrgQCAAANUQIbsL1nIEkyd8aUhpMAAADdRglswPae4ZHAeTOMBAIAAO2lBDZgR2skUAkEAADaTQlswN5rAueYDgoAALSZEtiAHT0DmTN9SiZPKk1HAQAAuowS2IDtPf2ZZxQQAABogBLYgB09/ZnrekAAAKABSmADtu8ZyLyZRgIBAID2UwIbsN1IIAAA0BAlsAE7egZcEwgAADRCCWyAkUAAAKApSmCb1VqHRwJdEwgAADRACWyz3X2DGRyqRgIBAIBGKIFttqNnIEkyTwkEAAAaoAS22fae/iTJXAvDAAAADVAC22xHqwTOm2kkEAAAaD8lsM227xmeDmokEAAAaIIS2GZ7p4O6JhAAAGiCEthm2x9dGMZIIAAA0H5KYJvt6h0ugbOnK4EAAED7KYFttqdvMEkyc+rkhpMAAADdSAlss57+wUyfMimTJpWmowAAAF1ICWyzPf2DmTnNKCAAANAMJbDNdvcNZpapoAAAQEOUwDbb0z+YGUYCAQCAhiiBbdbTN2hRGAAAoDFKYJvt7hvMLCOBAABAQxopgaWU+0spt5VSbiml3NjadnQp5ZullJWtP49qbS+llA+VUlaVUm4tpTyzicyjZU//YGYYCQQAABrS5Ejgz9Zan15rXdF6/p4k19Ralye5pvU8SV6WZHnr44okH2170lHU0286KAAA0JxOmg56aZJPtx5/Osmr9tn+mTrsuiQLSinHNxFwNJgOCgAANKmpEliT/GMp5aZSyhWtbcfWWte2Hq9Lcmzr8YlJVu/z2oda28Yl9wkEAACaNKWhz/vcWuuaUsriJN8spfxk35211lpKqYfzhq0yeUWSnHTSSaOXdJT19LkmEAAAaE4jI4G11jWtPzck+fskFyRZv3eaZ+vPDa3D1yRZus/Ll7S2Pf49P1ZrXVFrXbFo0aKxjH/Eaq3Z3W86KAAA0Jy2l8BSyuxSyty9j5O8JMntSa5K8pbWYW9J8uXW46uSvLm1SuiFSbbtM210XOkfrBkcqhaGAQAAGtPEdNBjk/x9KWXv5/9crfXrpZQbknyxlHJ5kgeSvLZ1/NVJXp5kVZLdSX65/ZFHR8/AYJJk+hQlEAAAaEbbS2Ct9d4k5+1n++YkL9zP9prk7W2INub6BoaSJDOmdtKirAAAQDfRRtqot1UCp03xzw4AADRDG2mj3n7TQQEAgGYpgW3UNzg8EjjdSCAAANAQbaSNevtNBwUAAJqljbTR3msCTQcFAACaogS20d7VQadbHRQAAGiINtJGva37BE6b7J8dAABohjbSRr1GAgEAgIZpI23U55pAAACgYUpgGz06HdTqoAAAQEO0kTb619VB/bMDAADN0EbaqE8JBAAAGqaNtNHekUDTQQEAgKZoI23U2+8WEQAAQLO0kTbqHRzK9CmTUkppOgoAANCllMA26u0fMhUUAABolEbSRr0DQ+4RCAAANEoJbKO+gSErgwIAAI3SSNqod2DQdFAAAKBRGkkbDQzWTJ1sURgAAKA5SmAbDQwNZcok/+QAAEBzNJI26jcSCAAANEwJbKOBoaFMcaN4AACgQRpJG/UP1kyZZCQQAABojhLYRgODQ5lqJBAAAGiQRtJGA0M1U1wTCAAANEgJbKPh6aD+yQEAgOZoJG00PB3USCAAANAcJbCNhqeD+icHAACao5G0Ud/AUKZaHRQAAGiQEthGw/cJVAIBAIDmKIFtNDBY3SICAABolEbSRv3uEwgAADRMI2mjgaGaKa4JBAAAGqQEttHAoNVBAQCAZmkkbdQ/5D6BAABAs5TANhkcqqk1mTLJPzkAANAcjaRN+geHksQtIgAAgEYpgW0yMFSTxHRQAACgUUpgmwzsHQk0HRQAAGiQRtIm/YNGAgEAgOYpgW0yMLT3mkD/5AAAQHM0kjYZaI0Eulk8AADQJCWwTfauDjrVSCAAANAgjaRN9q4O6hYRAABAk5TANum3OigAANABNJI2GbA6KAAA0AGUwDaxOigAANAJNJI26RtojQRaHRQAAGiQEtgms6ZNztOXLsi8mVObjgIAAHSxKU0H6BbnLV2Qf3j7RU3HAAAAupyRQAAAgC6iBAIAAHQRJRAAAKCLKIEAAABdRAkEAADoIkogAABAF1ECAQAAuogSCAAA0EWUQAAAgC6iBAIAAHQRJRAAAKCLKIEAAABdRAkEAADoIkogAABAFxk3JbCUckkp5e5SyqpSynuazgMAADAejYsSWEqZnOTDSV6W5Kwkl5VSzmo2FQAAwPgzLkpgkguSrKq13ltr7UvyhSSXNpwJAABg3BkvJfDEJKv3ef5QaxsAAACHYUrTAUZLKeWKJFe0nu4spdzdZJ4DWJhkU9MhGHPO88TnHHcH53nic467g/M88TnH+3fygXaMlxK4JsnSfZ4vaW17VK31Y0k+1s5Qh6uUcmOtdUXTORhbzvPE5xx3B+d54nOOu4PzPPE5x4dvvEwHvSHJ8lLKKaWUaUlen+SqhjMBAACMO+NiJLDWOlBKeUeSbySZnOTKWusdDccCAAAYd8ZFCUySWuvVSa5uOseT1NHTVRk1zvPE5xx3B+d54nOOu4PzPPE5x4ep1FqbzgAAAECbjJdrAgEAABgFSuAYKKVcUkq5u5SyqpTynv3sn15K+d+t/deXUpa1PyVP1gjO82+WUu4spdxaSrmmlHLAZXrpTIc6x/sc94ullFpKsTLZODSS81xKeW3r/+c7Simfa3dGnpwRfL0+qZTy7VLKza2v2S9vIidHrpRyZSllQynl9gPsL6WUD7X+G7i1lPLMdmfkyRnBOf6l1rm9rZTyg1LKee3OOJ4ogaOslDI5yYeTvCzJWUkuK6Wc9bjDLk/ySK31KUn+NMkftzclT9YIz/PNSVbUWs9N8rdJ/lt7U/JkjPAcp5QyN8k7k1zf3oSMhpGc51LK8iS/neSiWuvZSd7V9qAcsRH+v/yfk3yx1vqMDK9A/pH2pmQUfCrJJQfZ/7Iky1sfVyT5aBsyMbo+lYOf4/uSXFxrPSfJ++M6wYNSAkffBUlW1VrvrbX2JflCkksfd8ylST7devy3SV5YSiltzMiTd8jzXGv9dq11d+vpdRm+vyXjx0j+X06Gv9H8cZKedoZj1IzkPL8tyYdrrY8kSa11Q5sz8uSM5BzXJPNaj+cnebiN+RgFtdbvJdlykEMuTfKZOuy6JAtKKce3Jx2j4VDnuNb6g71fp+PnrkNSAkffiUlW7/P8oda2/R5Tax1Isi3JMW1Jx2gZyXne1+VJvjamiRhthzzHrelES2utX21nMEbVSP5fPj3J6aWUa0sp15VSDvabaDrPSM7x+5K8sZTyUIZXIv+P7YlGGx3u923GNz93HcK4uUUEjFellDcmWZHk4qazMHpKKZOSfDDJWxuOwtibkuEpZM/P8G+Wv1dKOafWurXRVIymy5J8qtb630spP5Pks6WUp9Vah5oOBhyeUsrPZrgEPrfpLJ3MSODoW5Nk6T7Pl7S27feYUsqUDE892dyWdIyWkZznlFJelOR3k7yy1trbpmyMjkOd47lJnpbkO6WU+5NcmOQqi8OMOyP5f/mhJFfVWvtrrfcluSfDpZDxYSTn+PIkX0ySWuu/JJmRZGFb0tEuI/q+zfhWSjk3yV8lubTW6mfrg1ACR98NSZaXUk4ppUzL8AXmVz3umKuSvKX1+NVJvlXdsHG8OeR5LqU8I8lfZrgAuoZo/DnoOa61bqu1Lqy1Lqu1Lsvw9QevrLXe2ExcjtBIvmb/Q4ZHAVNKWZjh6aH3tjMkT8pIzvGDSV6YJKWUMzNcAje2NSVj7aokb26tEnphkm211rVNh2L0lFJOSvKlJG+qtd7TdJ5OZzroKKu1DpRS3pHkG0kmJ7my1npHKeUPktxYa70qyScyPNVkVYYvcH19c4k5EiM8z3+SZE6Sv2mt+/NgrfWVjYXmsIzwHDPOjfA8fyPJS0opdyYZTPKf/IZ5/BjhOX53ko+XUn4jw4vEvNUvZ8eXUsrnM/zLmoWtazvfm2RqktRa/yLD13q+PMmqJLuT/HIzSTlSIzjHv5fhNTY+0vq5a6DWanbOARRf4wAAALqH6aAAAABdRAkEAADoIkogAABAF1ECAQAAuogSCAAA0EFKKVeWUjaUUm4f4fGvLaXcWUq5o5TyuUMdrwQCwD5KKYOllFta30h/XEp5dyllUmvfilLKhw7y2mV7v2Hve2wp5X2llN9qz98AgAngU0kuGcmBpZTlSX47yUW11rOTvOtQr3GfQAB4rD211qcnSSllcZLPJZmX5L211huT3DiSNzmcYwFgX7XW75VSlu27rZRyWpIPJ1mU4ftdvq3W+pMkb0vy4VrrI63XbjjU+xsJBIADaH0jvSLJO8qw55dSvpIkpZSLWyOGt5RSbi6lzN33tfse+7jtbyulfK2UMrOU8sZSyg9b7/GXpZTJrY9PlVJuL6Xc1rqBOQB8LMl/rLWen+S3knyktf30JKeXUq4tpVxXSjnkCKKRQAA4iFrrvaWUyUkWP27XbyV5e6312lLKnCQ9h3qvUso7krw4yauSnJrkdRmevtNfSvlIkl9KckeSE2utT2u9ZsHo/W0AGI9a32eek+RvSil7N09v/TklyfIkz0+yJMn3Sinn1Fq3Huj9lEAAODLXJvlgKeWvk3yp1vrQPt+Y9+fNSVYneVWr9L0wyflJbmi9bmaSDUn+T5JTSyl/nuSrSf5xDP8OAIwPk5Js3Xu5wuM8lOT6Wmt/kvtKKfdkuBTecLA3AwAOoJRyapLBDBe0R9VaP5DkVzJc3q4tpZxxiLe6LcmyDP+WNklKkk/XWp/e+nhqrfV9rWs6zkvynSS/luSvRuvvAsD4VGvdnuGC95okaV2icF5r9z9keBQwpZSFGZ4eeu/B3k8JBIADKKUsSvIXSf5nrbU+bt9ptdbbaq1/nOHfth6qBN6c5FeTXFVKOSHJNUle3Vp8JqWUo0spJ7e+gU+qtf5dkv+c5Jmj+7cCoNOVUj6f5F+SPLWU8lAp5fIMXzJweSnlxxm+dODS1uHfSLK5lHJnkm8n+U+11s0He3/TQQHgsWaWUm5JMjXJQJLPJvngfo57VynlZ5MMZfib8deSHH+wN661fr91q4ivZvjawP+c5B9bt6DoT/L2JHuSfHLvbSkyvOw3AF2k1nrZAXY9YdGX1i8pf7P1MSLlcb/YBAAAYAIzHRQAAKCLKIEAAABdRAkEAADoIkogAABAF1ECAQAAuogSCAAA0EWUQAAAgC6iBAIAAHSR/wv/KdHIaYTxawAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 15, 8\n",
        "plt.plot(ys ,errors)\n",
        "plt.xlabel(\"Dislikes\")\n",
        "plt.ylabel(\"MAE\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZ06VZU3ypJn"
      },
      "source": [
        "**The relationship between MAE and video views quantity:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "DMxQEcQ5JUfR"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "errors = []\n",
        "ys = []\n",
        "for i in range (np.min(X_test['view_count'])+1,np.max(X_test['view_count']),100000):\n",
        "  ys.append(i)\n",
        "  selected_y_test = y_test[X_test['view_count']<i]\n",
        "  selected_x_test = X_test[X_test['view_count']<i]\n",
        "# Predicting the Test set results\n",
        "  y_pred = cat_regressor.predict(selected_x_test)\n",
        "  np.set_printoptions(precision=2)\n",
        "  errors.append(mean_absolute_error(selected_y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "Lt7zlgPhvSUE",
        "outputId": "bff5c10b-64fa-4376-e56d-c976753cd15e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 15, 8\n",
        "plt.plot(ys ,errors)\n",
        "plt.xlabel(\"Views\")\n",
        "plt.ylabel(\"MAE\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Predicting YouTube Dislikes (bag-of-words model)",
      "provenance": [],
      "authorship_tag": "ABX9TyNWw/0IvBbpfAjP8IxIftbq",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}