Predicting_YouTube_Dislikes_via_Catboost_and_Sentiment_Analysis.ipynb

{
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    {
      "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_via_Catboost_and_Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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": {
        "id": "7jiZyXR6AlHO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0817a5fe-08f6-4ecd-a1f9-16cffc0a2d24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading youtube-dislikes-dataset.zip to /content\n",
            " 83% 49.0M/58.8M [00:01<00:00, 44.6MB/s]\n",
            "100% 58.8M/58.8M [00:01<00:00, 36.9MB/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": "5c35b537-4118-414d-aa5c-4a6611446eee"
      },
      "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\")"
      ]
    },
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              "    [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-c2d4fe70-04cd-4a9c-a0af-64a1368d6f0c 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-c2d4fe70-04cd-4a9c-a0af-64a1368d6f0c');\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": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s72vsWEVqM6l",
        "outputId": "e5d1374e-820d-4e6a-dc21-d03f38fa4d5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting vaderSentiment\n",
            "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
            "\u001b[?25l\r\u001b[K     |██▋                             | 10 kB 25.4 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 20 kB 29.1 MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 30 kB 17.4 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 40 kB 11.0 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 51 kB 10.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 61 kB 11.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████▏             | 71 kB 11.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 81 kB 11.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▍        | 92 kB 12.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 102 kB 11.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 112 kB 11.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 122 kB 11.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 125 kB 11.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vaderSentiment) (2.23.0)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (3.0.4)\n",
            "Installing collected packages: vaderSentiment\n",
            "Successfully installed vaderSentiment-3.3.2\n",
            "Collecting texthero\n",
            "  Downloading texthero-1.1.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: spacy<3.0.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (2.2.4)\n",
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            "Collecting nltk>=3.3\n",
            "  Downloading nltk-3.7-py3-none-any.whl (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 17.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wordcloud>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from texthero) (1.5.0)\n",
            "Collecting unidecode>=1.1.1\n",
            "  Downloading Unidecode-1.3.4-py3-none-any.whl (235 kB)\n",
            "\u001b[K     |████████████████████████████████| 235 kB 12.8 MB/s \n",
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            "Collecting regex>=2021.8.3\n",
            "  Downloading regex-2022.3.15-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB)\n",
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            "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",
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            "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: 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: 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 55 kB/s \n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
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            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.21.5)\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: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\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",
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            "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: 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 11.7 MB/s \n",
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            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.4.1)\n",
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            "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",
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            "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: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2018.9)\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 --upgrade vaderSentiment\n",
        "!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": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "Brc9ZMW0c_Es",
        "outputId": "7d45d949-775c-4ce6-f7b1-c3865b3b942c"
      },
      "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 in a Comedy: 73rd Emmys      925281    11212   \n",
              "3                JO1'YOUNG (JO1 ver.)' PERFORMANCE VIDEO     2641597    39131   \n",
              "4                           Why Retaining Walls Collapse      715724    32887   \n",
              "...                                                  ...         ...      ...   \n",
              "37417  Lil Tjay - Calling My Phone (feat. 6LACK) [Off...   120408275  2180780   \n",
              "37418  PELICANS at LAKERS | FULL GAME HIGHLIGHTS | Ja...     2841917    20759   \n",
              "37419                 [MV]  (MAMAMOO) - Where Are We Now    13346678   720854   \n",
              "37420              FELLIPE ESCUDERO- Master Podcast  #12      252057    19198   \n",
              "37421  Gareth Bale brace secures dramatic comeback on...     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 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 W...   \n",
              "37420  master masterpodcast lord lord vinheteiro z z ...   \n",
              "37421  Spurs Tottenham Hotspur Tottenham Leicester Sp...   \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",
              "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 Instagram:  ...   \n",
              "37420  DOCTOR HAIR     FOCO MUSICAL     --------- COR...   \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 jus...  \n",
              "4      Keep up with all my projects here:   Get some ...  \n",
              "...                                                  ...  \n",
              "37417  'DESTINED 2 WIN' OUT NOW !!   What a song will...  \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 8 columns]"
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              "      <th>0</th>\n",
              "      <td>Jadon Sancho  Magical Skills &amp; 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",
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              "      <td>Respect to Dortmund fans,must be sad losing hi...</td>\n",
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              "      <th>1</th>\n",
              "      <td>Migos - Avalanche (Official Video)</td>\n",
              "      <td>15352638</td>\n",
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              "      <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",
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              "      <th>2</th>\n",
              "      <td>Supporting Actress in a Comedy: 73rd Emmys</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>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",
              "      <td>JO1'YOUNG (JO1 ver.)' PERFORMANCE VIDEO ------...</td>\n",
              "      <td>youngVer&gt;&lt; REN is really PERFECT. It's not jus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Why 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",
              "      <td>One of the most important (and innocuous) part...</td>\n",
              "      <td>Keep up with all my projects here:   Get some ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37417</th>\n",
              "      <td>Lil Tjay - Calling My Phone (feat. 6LACK) [Off...</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 !!   What a song will...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37418</th>\n",
              "      <td>PELICANS at LAKERS | FULL GAME HIGHLIGHTS | Ja...</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>[MV]  (MAMAMOO) - Where Are We Now</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 W...</td>\n",
              "      <td>[MV] (MAMAMOO) - Where Are We Now Instagram:  ...</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>FELLIPE ESCUDERO- Master Podcast  #12</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     FOCO MUSICAL     --------- COR...</td>\n",
              "      <td>Foi um prazer passar esta tarde com vocs debat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37421</th>\n",
              "      <td>Gareth Bale brace secures dramatic comeback on...</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",
              "      <td>Two minute highlights from Tottenham Hotspur's...</td>\n",
              "      <td>Thank you Kane for everything you have given t...</td>\n",
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              "  </tbody>\n",
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              "<p>37422 rows × 8 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "import texthero as hero\n",
        "from texthero import preprocessing\n",
        "\n",
        "df_new = df.drop(['video_id','published_at','channel_id','channel_title'],axis=1)\n",
        "\n",
        "custom_pipeline = [preprocessing.fillna,\n",
        "                   preprocessing.remove_whitespace,\n",
        "                   preprocessing.remove_urls,\n",
        "                   ]\n",
        "\n",
        "df_new['description'] = df_new['description'].pipe(hero.clean, custom_pipeline)\n",
        "df_new['comments'] = df_new['comments'].pipe(hero.clean, custom_pipeline)\n",
        "df_new['tags'] = df_new['tags'].pipe(hero.clean, custom_pipeline)\n",
        "df_new"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8ONWib2umMn"
      },
      "source": [
        "**Sentiment Analysis using VADER:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "eK57DXF0fDB1"
      },
      "outputs": [],
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4OQLkwi4HGMN"
      },
      "outputs": [],
      "source": [
        "description_analysis = df_new['description'].apply(analyzer.polarity_scores).apply(pd.Series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "kej7W-ZakI6M"
      },
      "outputs": [],
      "source": [
        "comments_analysis = df_new['comments'].apply(analyzer.polarity_scores).apply(pd.Series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "nDUlhtcVkJs7"
      },
      "outputs": [],
      "source": [
        "tags_analysis = df_new['tags'].apply(analyzer.polarity_scores).apply(pd.Series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "SjkGrwwyxang"
      },
      "outputs": [],
      "source": [
        "description_analysis.rename(columns={\"neg\": \"des_neg\", \"neu\": \"des_neu\", \"pos\": \"des_pos\", \"compound\": \"des_compound\"},inplace=True)\n",
        "comments_analysis.rename(columns={\"neg\": \"com_neg\", \"neu\": \"com_neu\", \"pos\": \"com_pos\", \"compound\": \"com_compound\"},inplace=True)\n",
        "tags_analysis.rename(columns={\"neg\": \"tag_neg\", \"neu\": \"tag_neu\", \"pos\": \"tag_pos\", \"compound\": \"tag_compound\"},inplace=True)\n",
        "\n",
        "sentiment_df = pd.concat([description_analysis,comments_analysis,tags_analysis],axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-xFYcWjwtJj"
      },
      "source": [
        "**Merging data:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "YHCusrgUyH1t"
      },
      "outputs": [],
      "source": [
        "cleaned_df = pd.concat([df_new,sentiment_df],axis=1).drop([\"comments\",\"description\",\"tags\",\"title\"],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ABfqU5cDfRZc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "434b85da-6945-40cf-a503-97fa97613dfc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['view_count', 'likes', 'dislikes', 'comment_count', 'des_neg',\n",
              "       'des_neu', 'des_pos', 'des_compound', 'com_neg', 'com_neu', 'com_pos',\n",
              "       'com_compound', 'tag_neg', 'tag_neu', 'tag_pos', 'tag_compound'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "cleaned_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "45cZpEbXxHW2"
      },
      "source": [
        "**Specify input variables (X) and an output variable (y):**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "d5j9XyH_kczL"
      },
      "outputs": [],
      "source": [
        "X = cleaned_df.drop(['dislikes'],axis=1)\n",
        "y = cleaned_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": 16,
      "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",
        "from xgboost import XGBRegressor\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": 17,
      "metadata": {
        "id": "fy1wc9rYRWhX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96ef65d3-b627-4001-d933-9297152d1bc1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2564.08636614, -2909.00865281, -2931.26289486, -2944.04111978,\n",
              "       -2701.01887863])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "gDSgiOi0vg94",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92c5801c-98db-4cd4-f749-1ccaedd10cc4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-2809.8835824427333"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "scores.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "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": 20,
      "metadata": {
        "id": "QEM_hdGVhm_D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd921939-3bed-462e-9a08-cf5cc32fa787"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostRegressor at 0x7f0148c7f4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "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": 21,
      "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": 22,
      "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": 23,
      "metadata": {
        "id": "kSMHagsGdO6-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "08719b02-645f-48a5-beb4-bd68edebbbc8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x540 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": 24,
      "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": 25,
      "metadata": {
        "id": "deUXY--9mUQ1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "5c6cb170-f29f-4773-c8d6-0ae136a54f94"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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jC2wTCQAA84FAyJP2yP6ZnsF/+ZPPy8suWt9xNQAAwFNlDiFP2iP7DiVJ1q5cepwzAQCAPhMIedI+91cPJUnWrVzWcSUAAMDTIRDypDyw80D+042bkyTrVukhBACA+cwcQo5r/8RUrr9nR1qSO7bsSpK87y0vyPIli7stDAAAeFoEQp7Qb/zZ7fnIX96bicnpw21LFlV+4Ps2dFgVAAAwFwRCjmlquh0Og7/+xmfnsjNPTpKsX7Usp64yfxAAAOY7gZBj+uhXv5OJyen8Pz/x3Lzpxed2XQ4AADDHBEK+x+TUdL6xeWeuu/3BJMlrnn1GxxUBAADDIBCSJGmtZXK6JUk+dv19+SefujVJ8ppnnZ5TTrKaKAAALEQCIUmSv/lv/zI33Pvw4ftnrz0p//Inn5vLzjq5w6oAAIBhEgjJpof25IZ7H86Vl27M5c9YlyS54oL1ueKCUzuuDAAAGCaBcMy11vLmD/xlkuTnXn5BXnHJxo4rAgAARmVR1wXQnanpll/9o1uzbc9EfvZlz8gPXmxvQQAAGCd6CMfU9HTLP/rkzfnEjZtz9tqT8q7XPTNV1XVZAADACAmEY2b/xFQOTk7l1//0jnzya5vz1y5anz/4uy8RBgEAYAwJhGNk00O789r/9y8Oby9x9tqT8u9+5kXCIAAAjCmBcIz81QO7Mznd8r9ceVEuO+vkvP7ZZ2bRImEQAADGlUA4Rh7YeSBJctUPXpi1K5d1XA0AANA1q4yOiZs3P5Krv/jtrFy2OKectLTrcgAAgB4QCMfEJ27cnO/uPJB3/9hl5gwCAABJBMKx8e1te/Pss0/OT734vK5LAQAAekIgHAPb9hzMlzZtyyWnr+m6FAAAoEcEwjFw7/Z9mW7Ja591RtelAAAAPSIQjoEdeyeSJKefvKLjSgAAgD4RCMfAjr0HkyTrV9tqAgAAeJRAOAa2D3oI169a3nElAABAnwiEY2D7nomctHRxTlq2uOtSAACAHhEIx8COvROGiwIAAN9DIBwDn7ntgaxfJRACAACPJRAucLd/d1f2Tkxl7UqBEAAAeCyBcAG79f6def37/iJJ8utveHbH1QAAAH0jEC5gf3Lzd5Mk733T83Le+pUdVwMAAPSNQLiA3bJ5Z553zin5Gy88p+tSAACAHhIIF7C7t+7N9522pusyAACAnhIIF6iDk1N5YNeBnG+oKAAAcAwC4QK1bc9EkmTjmuUdVwIAAPSVQLhAbdt9MEmyYbVACAAAHJ1AuEB96EvfTpJs0EMIAAAcg0C4AO0+cCh/fsdDuXDDqjzrrJO7LgcAAOipJV0XwNz70qbt2XNwMh986+VZuljmBwAAjk5aWGB2HTiU91xzW5LkOeec0nE1AABAnwmEC8z7P/+tPLDrQH74+0/PymU6gAEAgGMTCBeYG+95OC84b21+962Xd10KAADQcwLhAjI13XLrd3fmeees7boUAABgHhAIF5C7HtydfRNTea65gwAAwAkQCBeQ2cVkrrjg1I4rAQAA5gOBcAG568HdOfOUFTln3cquSwEAAOYBgXCBeHjvRB7edyh/5+UXdF0KAAAwTwiEC8Td2/YkSS7cuKrjSgAAgPlCIFwgvrV1b5Lkwo2rO64EAACYLwTCBeLaW7Zk9fIlOXfdSV2XAgAAzBMC4QLw2TsezOfv3JqXXrg+Sxb7JwUAAE6M9LAAfOhL9yRJ/q8ff3a3hQAAAPOKQLgAPLDrQP6HizfktJNXdF0KAAAwjwiEC8CDOw/kIovJAAAAT9KSrgvgqZucms5Vv39jdh+czHmn2oweAAB4cvQQzmP/+nOb8l//6qH8tYvW52+95LyuywEAAOYZPYTz1NR0ywe+cHeeecaa/P7bXpLFi6rrkgAAgHlGD+E89aVN27JvYiovPv9UYRAAAHhKBMJ56s4HdidJfvFVl3RcCQAAMF8JhPPU7Vt2ZcPq5Tl11bKuSwEAAOYpgXAeaq3lK3dvzxUXrOu6FAAAYB4TCOehzQ/vz3d3HshLL1zfdSkAAMA8JhDOQ1++e3uS5CUXCIQAAMBTJxDOQ7fcvzNrli/Jxaet7roUAABgHhMI56F7t+/LMzaszCLbTQAAAE/D0AJhVV1dVQ9V1a1HtL2nqu6vqpsGH68/4tg/rqpNVXVnVb3miPbXDto2VdW7hlXvfPHfv7Ut/+2urbloo95BAADg6RlmD+GHk7z2KO2/1Vp7/uDj2iSpqsuSvDnJswaP+TdVtbiqFif5nSSvS3JZkrcMzh1bv/6ndyRJfsn+gwAAwNO0ZFhP3Fr7QlWdf4KnvyHJx1prB5N8u6o2JblicGxTa+3uJKmqjw3OvX2Oy50XvrV1T+7Ysisvu3B9nrF+VdflAAAA81wXcwjfUVU3D4aUzm6kd3aS+444Z/Og7VjtY+lfffrOJMk//bGx7iQFAADmyKgD4fuTXJTk+Um2JPnNuXriqrqqqm6oqhu2bt06V0/bG621XH/PjqxftSyXnr6m63IAAIAFYKSBsLX2YGttqrU2neTf59FhofcnOfeIU88ZtB2r/WjP/YHW2uWttcs3btw498V37J7t+7Jtz0Te+epLrS4KAADMiZEGwqo684i7P55kdgXSa5K8uaqWV9UFSS5O8tUk1ye5uKouqKplmVl45ppR1twXX//Ow0mSFz1j3XHOBAAAODFDW1Smqj6a5MokG6pqc5J3J7myqp6fpCW5J8nfT5LW2m1V9fHMLBYzmeTtrbWpwfO8I8mnkyxOcnVr7bZh1dxn9+3YnyQ5f8PKjisBAAAWimGuMvqWozR/8AnO/40kv3GU9muTXDuHpc1LW3buz4bVy7J8yeKuSwEAABaILlYZ5Sm4Z/venL1O7yAAADB3BMJ5YHJqOjdv3pnnn3NK16UAAAALiEA4D/zxTd/NvompvNCCMgAAwBwSCOeBD37x20mSl124vuNKAACAhUQg7Lnf//K9uX3Lrrznxy7LaSev6LocAABgAXnCQFhVJz/BsfPmvhwe70+/8d0kyd940TkdVwIAACw0x+sh/Pzsjar67OOOfWrOq+Exrrv9wXz1nh35B1delJNXLO26HAAAYIE5XiCsI26f+gTHGIJP3XR/Tl+zIj//you7LgUAAFiAjhcI2zFuH+0+c+yOLbvy3HNOyYqlNqMHAADm3pLjHD+tqn4pM72Bs7czuL9xqJWNuf0TU7ln29782HPP6roUAABggTpeIPz3SdYc5XaS/O5QKiJJ8vX7Hs50S5511jHX9QEAAHhanjAQttZ+7VjHqurFc18OSdJay3s/c1dOWro4P3Dxhq7LAQAAFqjj9RA+RlVdluQtg49Hklw+jKLG3e1bduWGex/OD126MSuXPal/IgAAgBN23LRRVefn0RB4KMkzklzeWrtnmIWNs9u/uytJ8r//yGUdVwIAACxkx9uY/i+T/FlmguNPtNZelGS3MDhcd2zZnRVLF+WCDau6LgUAAFjAjrftxIOZWUjm9Dy6qqjtJobsji27cunpa7J4ka0eAQCA4XnCQNhae2OS5yS5Mcl7qurbSdZV1RWjKG5c3fXg7lx6xprjnwgAAPA0HHcOYWttZ5IPJflQVZ2e5E1JfquqzmutnTvsAsfN9j0Hs33vRC45XSAEAACG63hDRh+jtfZga+23W2svT/IDQ6pprN314J4kycUCIQAAMGRP2ENYVdcc5/F/fQ5rITPDRZPkUoEQAAAYsuMNGX1ZkvuSfDTJV5JY5WTI7npwd9asWJLTT17edSkAAMACd7xAeEaSV2VmD8K/lZktKD7aWrtt2IWNq28+uCeXnL4mVbI3AAAwXMdbZXSqtfZfWmtvTfLSJJuSfL6q3jGS6sZMay13PrjbgjIAAMBIHHeV0apanuRHMtNLeH6S9yX5o+GWNZ7+y60PZOf+Q3nBeWu7LgUAABgDx1tU5veSPDvJtUl+rbV260iqGlOf/Nr9OXvtSfmJF57TdSkAAMAYOF4P4U8n2Zvk55P8wyPmtVWS1lo7eYi1jZ07H9yVF5y3NosXmT8IAAAM3xMGwtbak9qnkKduYnI6mx/en7/xAr2DAADAaAh8PfHgrgNpLTlr7YquSwEAAMaEQNgTD+46kCQ5/WSBEAAAGA2BsCfueGB3kuScdSs7rgQAABgXAmFPfOGurXnG+pW5aOOqrksBAADGhEDYE3dv3ZNnnrEmR6zkCgAAMFQCYQ/sPTiZe7bvy8Wnrem6FAAAYIwIhD3wjc2PZGq65fLz13VdCgAAMEYEwh64e+veJMmlZ+ghBAAARkcg7IF7tu3N8iWLcvoaW04AAACjIxD2wI3feTjPOuvkLFpkQRkAAGB0BMIeuOuB3XnuOWu7LgMAABgzAmHH9k9MZe/EVDauWd51KQAAwJgRCDu2bc/BJMnG1QIhAAAwWgJhx7YOAuGGNcs6rgQAABg3AmHHNj+8P0ly1tqTOq4EAAAYNwJhx+7bsS9Jcu66lR1XAgAAjBuBsGMP7TqQNSuWZNXyJV2XAgAAjBmBsGMP7zuUdSvNHwQAAEZPIOzYI/sPZd3KpV2XAQAAjCGBsGOP7JvIWj2EAABABwTCju3YO6GHEAAA6IRA2KGp6ZYHdh7ImbacAAAAOiAQdmjr7oOZnG45WyAEAAA6IBB26IFdB5IkZ5y8ouNKAACAcSQQdmj7noNJkg1rlndcCQAAMI4Ewg5t3zORJNmw2iqjAADA6AmEHdq2d6aHcP0qPYQAAMDoCYQd2r5nIquWLc5JyxZ3XQoAADCGBMIObd9zMOtX6x0EAAC6IRB2aPveiaw3fxAAAOiIQNihbXsmzB8EAAA6IxB2aPueg1YYBQAAOiMQdmR6uhkyCgAAdEog7MjO/YcyNd0MGQUAADojEHZk++wehHoIAQCAjgiEHdm2ZyJJssG2EwAAQEcEwo5sHwRCPYQAAEBXBMKOHB4yag4hAADQEYGwI9v2TKQqWbdyadelAAAAY0og7Mj2PQdz6splWbLYPwEAANANaaQj2/fYgxAAAOiWQNiR7XsPmj8IAAB0amiBsKqurqqHqurWI9pOrarrquqbg8/rBu1VVe+rqk1VdXNVvfCIx7x1cP43q+qtw6p31PQQAgAAXRtmD+GHk7z2cW3vSvLZ1trFST47uJ8kr0ty8eDjqiTvT2YCZJJ3J3lJkiuSvHs2RM532/YctAchAADQqaEFwtbaF5LseFzzG5J8ZHD7I0neeET777UZX06ytqrOTPKaJNe11na01h5Ocl2+N2TOOxOT09l1YDLrV+khBAAAujPqOYSnt9a2DG4/kOT0we2zk9x3xHmbB23Hap/Xduyd3ZReDyEAANCdzhaVaa21JG2unq+qrqqqG6rqhq1bt87V0w7Ftj2DTenNIQQAADo06kD44GAoaAafHxq035/k3CPOO2fQdqz279Fa+0Br7fLW2uUbN26c88Ln0mwg3CAQAgAAHRp1ILwmyexKoW9N8sdHtP/sYLXRlybZORha+ukkr66qdYPFZF49aJvXtu+ZGTJ6qm0nAACADi0Z1hNX1UeTXJlkQ1Vtzsxqof88ycer6m1J7k3ypsHp1yZ5fZJNSfYl+bkkaa3tqKpfT3L94Lx/1lp7/EI1887D+2YDoR5CAACgO0MLhK21txzj0CuPcm5L8vZjPM/VSa6ew9I6t3P/oVQla5YP7ccPAABwXJ0tKjPOdu4/lFNOWppFi6rrUgAAgDEmEHbgkX0zgRAAAKBLAmEHdu4/lLUCIQAA0DGBsAOP7D+UU1ZaUAYAAOiWQNiBXfsNGQUAALonEHbgkX0ThowCAACdEwhHbHq6HV5lFAAAoEsC4YjtPzSV6ZasWWEPQgAAoFsC4YgdODSVJFmxdHHHlQAAAONOIByxA5PTSZIVS/3oAQCAbkklI6aHEAAA6AuBcMRmA+HyJQIhAADQLYFwxA4cMmQUAADoB6lkxA4aMgoAAPSEQDhiByYFQgAAoB8EwhE7aMgoAADQE1LJiB3uIbSoDAAA0DGBcMRmF5VZrocQAADomFQyYof3IdRDCAAAdEwgHLFHt50QCAEAgG4JhCP26Mb0fvQAAEC3pJIROzA5lWVLFmXRouq6FAAAYMwJhCN28NB0VugdBAAAekAyGbEDh6bMHwQAAHpBIBwxgVsDsyEAAA8DSURBVBAAAOgLgXDEDhyazgp7EAIAAD0gmYzYgUk9hAAAQD8IhCN24NCUTekBAIBeEAhH7MCh6Sw3ZBQAAOgByWTEDhyaynI9hAAAQA8IhCN2cNKiMgAAQD9IJiNm2wkAAKAvBMIR00MIAAD0hWQyYlYZBQAA+kIgHLEDh6asMgoAAPSCZDJCk1PTmW6xyigAANALAuEIHZycTpIsX+LHDgAAdE8yGaGJQSBcJhACAAA9IJmM0MSUQAgAAPSHZDJCBw/NDhk1hxAAAOieQDhCE1NTSfQQAgAA/SCZjNDsojLLFvuxAwAA3ZNMRmjCKqMAAECPSCYjZNsJAACgTySTEbLtBAAA0CeSyQgJhAAAQJ9IJiP06JBR204AAADdEwhHyLYTAABAn0gmI2TIKAAA0CeSyQhZZRQAAOgTyWSE9BACAAB9IpmM0GwP4bLFfuwAAED3JJMRmhAIAQCAHpFMRujg5HSWLV6URYuq61IAAAAEwlGamJw2fxAAAOgN6WSEJqamBEIAAKA3pJMROnho2vxBAACgN6STEZqablm6xPxBAACgHwTCEZqcbllcAiEAANAPAuEITbWWxVYYBQAAekIgHKGpqZYli/zIAQCAfpBORmhyutmDEAAA6A2BcISmW8sSgRAAAOgJgXCEJqfNIQQAAPpDIByhqelpgRAAAOgNgXCEpvQQAgAAPSIQjtDUtDmEAABAfwiEI2QOIQAA0CcC4QhNC4QAAECPCIQjNGnIKAAA0COdBMKquqeqbqmqm6rqhkHbqVV1XVV9c/B53aC9qup9VbWpqm6uqhd2UfNcmJpuWVQCIQAA0A9d9hD+UGvt+a21ywf335Xks621i5N8dnA/SV6X5OLBx1VJ3j/ySufI1HTLksUCIQAA0A99GjL6hiQfGdz+SJI3HtH+e23Gl5Osraozuyjw6ZrZdqJPP3IAAGCcdZVOWpLPVNWNVXXVoO301tqWwe0Hkpw+uH12kvuOeOzmQdu8MzndooMQAADoiyUdfd0faK3dX1WnJbmuqv7qyIOttVZV7ck84SBYXpUk55133txVOof0EAIAAH3SSTpprd0/+PxQkj9KckWSB2eHgg4+PzQ4/f4k5x7x8HMGbY9/zg+01i5vrV2+cePGYZb/lNmYHgAA6JORB8KqWlVVa2ZvJ3l1kluTXJPkrYPT3prkjwe3r0nys4PVRl+aZOcRQ0vnlcnplkUCIQAA0BNdDBk9Pckf1cz2C0uS/IfW2n+pquuTfLyq3pbk3iRvGpx/bZLXJ9mUZF+Snxt9yXNjuukhBAAA+mPkgbC1dneS5x2lfXuSVx6lvSV5+whKG7rJqeksFggBAICesMLJCE232JgeAADoDYFwhFprkQcBAIC+EAhHaKaHsOsqAAAAZgiEI9TSUroIAQCAnhAIR2i6xZBRAACgNwTCUWpJRSIEAAD6QSAcoenWzCEEAAB6QyAcoRZDRgEAgP4QCEdopodQIgQAAPpBIByh1mIGIQAA0BsC4Yi01pLEthMAAEBvCIQjMsiD5hACAAC9IRCOyCAPmkMIAAD0hkA4ItOzQ0Y7rgMAAGCWQDgis0NGF9mIEAAA6AmBcERmewgBAAD6QiAcMXMIAQCAvhAIR+TwHEJ5EAAA6AmBcEQOzyEUCAEAgJ4QCEfk0VVGJUIAAKAfBMIRmV1SxpBRAACgLwTCEWnTM59LIgQAAHpCIByRNugjNIcQAADoC4FwRKYHY0blQQAAoC8EwhFpg0VlFukiBAAAekIgHBE9hAAAQN8IhCMyO4fQojIAAEBfCIQjMrsxvTwIAAD0hUA4IrOBcJFECAAA9IRAOCLTg0QoDgIAAH0hEI7IoINQDyEAANAbAuGITFtmFAAA6BmBcMT0EAIAAH0hEI6IOYQAAEDfCIQjcniVUT9xAACgJ8STEXm0h1AfIQAA0A8C4YjMrjJqCiEAANAXAuGItNkeQokQAADoCYFwRA7PIZQHAQCAnhAIR+TRbQglQgAAoB8EwhFpg1mEeggBAIC+EAhHZHp65rMphAAAQF8IhCMy20NoURkAAKAvBMIRaYfnEAIAAPSDQDgij64yKhICAAD9IBCOyPThfQg7LgQAAGBAIByRQQehHkIAAKA3BMIRmTaJEAAA6BmBcETMIQQAAPpGIByRNjuHsOM6AAAAZgmEI2IOIQAA0DcC4YhMT1tlFAAA6BeBcERmewgFQgAAoC8EwhE5vA+hWYQAAEBPCIQjsmb50jz/3LVZs2JJ16UAAAAkSaSTEXnOOafkU29/eddlAAAAHKaHEAAAYEwJhAAAAGNKIAQAABhTAiEAAMCYEggBAADGlEAIAAAwpgRCAACAMSUQAgAAjCmBEAAAYEwJhAAAAGNKIAQAABhTAiEAAMCYEggBAADG1LwJhFX12qq6s6o2VdW7uq4HAABgvpsXgbCqFif5nSSvS3JZkrdU1WXdVgUAADC/zYtAmOSKJJtaa3e31iaSfCzJGzquCQAAYF6bL4Hw7CT3HXF/86ANAACAp2i+BMLjqqqrquqGqrph69atXZcDAADQe0u6LuAE3Z/k3CPunzNoO6y19oEkH0iSqtpaVfeOrrwTtiHJtq6LYCy59uiKa4+uuPboguuOrhzt2nvGiTywWmtzX84cq6olSe5K8srMBMHrk/yt1tptnRb2JFXVDa21y7uug/Hj2qMrrj264tqjC647uvJ0rr150UPYWpusqnck+XSSxUmunm9hEAAAoG/mRSBMktbatUmu7boOAACAhWLBLCozT3yg6wIYW649uuLaoyuuPbrguqMrT/namxdzCAEAAJh7eggBAADGlEA4BFX12qq6s6o2VdW7jnJ8eVX9x8Hxr1TV+aOvkoXoBK69vz3YluWmwcff7aJOFpaqurqqHqqqW49xvKrqfYPr8uaqeuGoa2RhOoFr78qq2nnEa94/HXWNLDxVdW5Vfa6qbq+q26rq549yjtc95twJXntP+nVv3iwqM19U1eIkv5PkVUk2J7m+qq5prd1+xGlvS/Jwa+37qurNSf5Fkp8afbUsJCd47SXJf2ytvWPkBbKQfTjJv07ye8c4/rokFw8+XpLk/YPP8HR9OE987SXJX7TWfnQ05TAmJpO8s7X2tapak+TGqrruce+3XvcYhhO59pIn+bqnh3DuXZFkU2vt7tbaRJKPJXnD4855Q5KPDG5/Iskrq6pGWCML04lcezDnWmtfSLLjCU55Q5LfazO+nGRtVZ05mupYyE7g2oM511rb0lr72uD27iR3JDn7cad53WPOneC196QJhHPv7CT3HXF/c773H+rwOa21ySQ7k6wfSXUsZCdy7SXJTwyGr3yiqs4dTWmMuRO9NmEYXlZV36iq/1xVz+q6GBaWwbSfFyT5yuMOed1jqJ7g2kue5OueQAjj5U+SnN9ae26S6/JoTzXAQvS1JM9orT0vyW8n+VTH9bCAVNXqJJ9M8guttV1d18P4OM6196Rf9wTCuXd/kiN7Xc4ZtB31nKpakuSUJNtHUh0L2XGvvdba9tbawcHd303yohHVxng7kddFmHOttV2ttT2D29cmWVpVGzouiwWgqpZm5hfyP2it/eFRTvG6x1Ac79p7Kq97AuHcuz7JxVV1QVUtS/LmJNc87pxrkrx1cPsnk/zXZkNInr7jXnuPm7/w1zMz9hyG7ZokPztYde+lSXa21rZ0XRQLX1WdMTtHv6quyMzvPf4Ay9MyuKY+mOSO1tp7j3Ga1z3m3Ilce0/ldc8qo3OstTZZVe9I8ukki5Nc3Vq7rar+WZIbWmvXZOYf8veralNmJsO/ubuKWShO8Nr7h1X11zOzStWOJH+7s4JZMKrqo0muTLKhqjYneXeSpUnSWvu3Sa5N8vokm5LsS/Jz3VTKQnMC195PJvkHVTWZZH+SN/sDLHPg5Ul+JsktVXXToO1Xk5yXeN1jqE7k2nvSr3vldREAAGA8GTIKAAAwpgRCAACAMSUQAgAAjCmBEAAAYEwJhAAAAD1SVVdX1UNVdesJnv+mqrq9qm6rqv/wZL6WQAgAR6iqqaq6afCm+o2qemdVLRocu7yq3vcEjz1/9s37yHOr6j1V9cuj+Q4AWAA+nOS1J3JiVV2c5B8neXlr7VlJfuHJfCH7EALAY+1vrT0/SarqtCT/IcnJSd7dWrshyQ0n8iRP5lwAOFJr7QtVdf6RbVV1UZLfSbIxM/tb/r3W2l8l+XtJfqe19vDgsQ89ma+lhxAAjmHwpnpVknfUjCur6k+TpKpeMehJvKmqvl5Va4587JHnPq7971XVf66qk6rqp6vqq4Pn+HdVtXjw8eGqurWqbqmqXxzNdwtAz30gyf/aWntRkl9O8m8G7ZckuaSqvlRVX66qE+pZnKWHEACeQGvt7qpanOS0xx365SRvb619qapWJzlwvOeqqnckeVWSNya5MMlPZWaIz6Gq+jdJ/qcktyU5u7X27MFj1s7ddwPAfDR4n/lrSf5TVc02Lx98XpLk4iRXJjknyReq6jmttUdO5LkFQgB4ar6U5L1V9QdJ/rC1tvmIN+mj+dkk9yV54yAAvjLJi5JcP3jcSUkeSvInSS6sqt9O8mdJPjPE7wGA+WFRkkdmpzQ8zuYkX2mtHUry7aq6KzMB8foTfWIA4Biq6sIkU5kJa4e11v55kr+bmSD3pap65nGe6pYk52fmr7dJUkk+0lp7/uDj0tbaewZzQJ6X5PNJ/uckvztX3wsA81NrbVdmwt7fTJLBNIbnDQ5/KjO9g6mqDZkZQnr3iT63QAgAx1BVG5P82yT/urXWHnfsotbaLa21f5GZv8IeLxB+PcnfT3JNVZ2V5LNJfnKwcE2q6tSqesbgzXxRa+2TSf6PJC+c2+8KgL6rqo8m+cskl1bV5qp6W2amFbytqr6RmekFbxic/ukk26vq9iSfS/IrrbXtJ/q1DBkFgMc6qapuSrI0yWSS30/y3qOc9wtV9UNJpjPzxvyfk5z5RE/cWvviYPuJP8vMXML/I8lnBttaHEry9iT7k3xodquLzCwlDsAYaa295RiHvmfBmMEfLH9p8PGk1eP+4AkAAMCYMGQUAABgTAmEAAAAY0ogBAAAGFMCIQAAwJgSCAEAAMaUQAgAADCmBEIAAIAxJRACAACMqf8fCSrtqS7HFMsAAAAASUVORK5CYII=\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": 26,
      "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": 27,
      "metadata": {
        "id": "Lt7zlgPhvSUE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "58138f14-3265-4429-88e0-30093c150395"
      },
      "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": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Predicting YouTube Dislikes via Catboost and Sentiment Analysis",
      "provenance": [],
      "authorship_tag": "ABX9TyNMsKCdI2ggYwTtchD2XEVg",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}