YouTube_comments_Classification.ipynb

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  "metadata": {
    "colab": {
      "name": "YouTube comments Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPWcCzmyC3fYNVX8WLHDwLh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JosueAfouda/TUTORIALS/blob/main/YouTube_comments_Classification.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": "zxfkkilFM-BI"
      },
      "outputs": [],
      "source": [
        "# Librairies\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pip install wget"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HEFNUl8iO40y",
        "outputId": "10dae911-bcf6-4015-f006-95aba3e50c93"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9675 sha256=bce882a03c5ce2bd14b544b2f9de2936dba75bf0ce832cf63d48515ba84b848a\n",
            "  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Données (Source : https://archive.ics.uci.edu/ml/datasets/YouTube+Spam+Collection)\n",
        "# Les commentaires au niveau de 5 vidéos YouTube\n",
        "import wget\n",
        "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00380/YouTube-Spam-Collection-v1.zip'\n",
        "wget.download(url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "F3jKPukCOGm5",
        "outputId": "e9db42b6-2d06-4de7-c263-0d508cf6a5fc"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'YouTube-Spam-Collection-v1.zip'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extraction des fichiers dans le dossier zip\n",
        "\n",
        "from zipfile import ZipFile \n",
        "\n",
        "file = \"YouTube-Spam-Collection-v1.zip\"\n",
        "  \n",
        "# open the zip file in read mode\n",
        "with ZipFile(file, 'r') as zip: \n",
        "    # list all the contents of the zip file\n",
        "    zip.printdir() \n",
        "  \n",
        "    # extract all files\n",
        "    print('extraction...') \n",
        "    zip.extractall() \n",
        "    print('Done!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94anInMaO3Mc",
        "outputId": "11504a25-1e9b-43ac-c0ac-245171aecb55"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File Name                                             Modified             Size\n",
            "Youtube01-Psy.csv                              2017-03-26 18:29:06        57438\n",
            "__MACOSX/                                      2017-03-26 18:33:38            0\n",
            "__MACOSX/._Youtube01-Psy.csv                   2017-03-26 18:29:06          177\n",
            "Youtube02-KatyPerry.csv                        2017-03-26 18:29:22        64279\n",
            "__MACOSX/._Youtube02-KatyPerry.csv             2017-03-26 18:29:22          177\n",
            "Youtube03-LMFAO.csv                            2017-03-26 18:29:26        64419\n",
            "__MACOSX/._Youtube03-LMFAO.csv                 2017-03-26 18:29:26          177\n",
            "Youtube04-Eminem.csv                           2017-03-26 18:29:30        82896\n",
            "__MACOSX/._Youtube04-Eminem.csv                2017-03-26 18:29:30          177\n",
            "Youtube05-Shakira.csv                          2017-03-26 18:29:34        72706\n",
            "__MACOSX/._Youtube05-Shakira.csv               2017-03-26 18:29:34          177\n",
            "extraction...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On s'est assuré que les 05 datasets ont la même configuration (même nombre et noms de colonnes). Nous allons donc pouvoir les concactener afin de former un seul jeu de données."
      ],
      "metadata": {
        "id": "27y8fBQUdDAp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([\n",
        "                pd.read_csv('Youtube01-Psy.csv'),\n",
        "                pd.read_csv('Youtube02-KatyPerry.csv'),\n",
        "                pd.read_csv('Youtube03-LMFAO.csv'),\n",
        "                pd.read_csv('Youtube04-Eminem.csv'),\n",
        "                pd.read_csv('Youtube05-Shakira.csv')\n",
        "])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oFzLpxC5PzGA",
        "outputId": "70f4319e-9a7b-4db0-957b-ec53e8eb536e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      COMMENT_ID                AUTHOR  \\\n",
              "0    LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU             Julius NM   \n",
              "1    LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A           adam riyati   \n",
              "2    LZQPQhLyRh9MSZYnf8djyk0gEF9BHDPYrrK-qCczIY8      Evgeny Murashkin   \n",
              "3            z13jhp0bxqncu512g22wvzkasxmvvzjaz04       ElNino Melendez   \n",
              "4            z13fwbwp1oujthgqj04chlngpvzmtt3r3dw                GsMega   \n",
              "..                                           ...                   ...   \n",
              "365  _2viQ_Qnc6-bMSjqyL1NKj57ROicCSJV5SwTrw-RFFA          Katie Mettam   \n",
              "366  _2viQ_Qnc6-pY-1yR6K2FhmC5i48-WuNx5CumlHLDAI  Sabina Pearson-Smith   \n",
              "367  _2viQ_Qnc6_k_n_Bse9zVhJP8tJReZpo8uM2uZfnzDs         jeffrey jules   \n",
              "368  _2viQ_Qnc6_yBt8UGMWyg3vh0PulTqcqyQtdE7d4Fl0        Aishlin Maciel   \n",
              "369  _2viQ_Qnc685RPw1aSa1tfrIuHXRvAQ2rPT9R06KTqA           Latin Bosch   \n",
              "\n",
              "                           DATE  \\\n",
              "0           2013-11-07T06:20:48   \n",
              "1           2013-11-07T12:37:15   \n",
              "2           2013-11-08T17:34:21   \n",
              "3           2013-11-09T08:28:43   \n",
              "4           2013-11-10T16:05:38   \n",
              "..                          ...   \n",
              "365  2013-07-13T13:27:39.441000   \n",
              "366  2013-07-13T13:14:30.021000   \n",
              "367  2013-07-13T12:09:31.188000   \n",
              "368  2013-07-13T11:17:52.308000   \n",
              "369  2013-07-12T22:33:27.916000   \n",
              "\n",
              "                                               CONTENT  CLASS  \n",
              "0    Huh, anyway check out this you[tube] channel: ...      1  \n",
              "1    Hey guys check out my new channel and our firs...      1  \n",
              "2               just for test I have to say murdev.com      1  \n",
              "3     me shaking my sexy ass on my channel enjoy ^_^ ﻿      1  \n",
              "4              watch?v=vtaRGgvGtWQ   Check this out .﻿      1  \n",
              "..                                                 ...    ...  \n",
              "365  I love this song because we sing it at Camp al...      0  \n",
              "366  I love this song for two reasons: 1.it is abou...      0  \n",
              "367                                                wow      0  \n",
              "368                            Shakira u are so wiredo      0  \n",
              "369                         Shakira is the best dancer      0  \n",
              "\n",
              "[1956 rows x 5 columns]"
            ],
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              "      <th>COMMENT_ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU</td>\n",
              "      <td>Julius NM</td>\n",
              "      <td>2013-11-07T06:20:48</td>\n",
              "      <td>Huh, anyway check out this you[tube] channel: ...</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A</td>\n",
              "      <td>adam riyati</td>\n",
              "      <td>2013-11-07T12:37:15</td>\n",
              "      <td>Hey guys check out my new channel and our firs...</td>\n",
              "      <td>1</td>\n",
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              "      <td>LZQPQhLyRh9MSZYnf8djyk0gEF9BHDPYrrK-qCczIY8</td>\n",
              "      <td>Evgeny Murashkin</td>\n",
              "      <td>2013-11-08T17:34:21</td>\n",
              "      <td>just for test I have to say murdev.com</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>z13jhp0bxqncu512g22wvzkasxmvvzjaz04</td>\n",
              "      <td>ElNino Melendez</td>\n",
              "      <td>2013-11-09T08:28:43</td>\n",
              "      <td>me shaking my sexy ass on my channel enjoy ^_^ ﻿</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>z13fwbwp1oujthgqj04chlngpvzmtt3r3dw</td>\n",
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              "      <td>2013-11-10T16:05:38</td>\n",
              "      <td>watch?v=vtaRGgvGtWQ   Check this out .﻿</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>365</th>\n",
              "      <td>_2viQ_Qnc6-bMSjqyL1NKj57ROicCSJV5SwTrw-RFFA</td>\n",
              "      <td>Katie Mettam</td>\n",
              "      <td>2013-07-13T13:27:39.441000</td>\n",
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              "      <td>0</td>\n",
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              "      <th>366</th>\n",
              "      <td>_2viQ_Qnc6-pY-1yR6K2FhmC5i48-WuNx5CumlHLDAI</td>\n",
              "      <td>Sabina Pearson-Smith</td>\n",
              "      <td>2013-07-13T13:14:30.021000</td>\n",
              "      <td>I love this song for two reasons: 1.it is abou...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>367</th>\n",
              "      <td>_2viQ_Qnc6_k_n_Bse9zVhJP8tJReZpo8uM2uZfnzDs</td>\n",
              "      <td>jeffrey jules</td>\n",
              "      <td>2013-07-13T12:09:31.188000</td>\n",
              "      <td>wow</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>_2viQ_Qnc6_yBt8UGMWyg3vh0PulTqcqyQtdE7d4Fl0</td>\n",
              "      <td>Aishlin Maciel</td>\n",
              "      <td>2013-07-13T11:17:52.308000</td>\n",
              "      <td>Shakira u are so wiredo</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>_2viQ_Qnc685RPw1aSa1tfrIuHXRvAQ2rPT9R06KTqA</td>\n",
              "      <td>Latin Bosch</td>\n",
              "      <td>2013-07-12T22:33:27.916000</td>\n",
              "      <td>Shakira is the best dancer</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "<p>1956 rows × 5 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 : spam, 0: not spam"
      ],
      "metadata": {
        "id": "E3jM3YIORQBJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUUbFGuLXnLI",
        "outputId": "d8dbfc77-41b0-4c88-8eb9-4a68ae88074c"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1956 entries, 0 to 369\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   COMMENT_ID  1956 non-null   object\n",
            " 1   AUTHOR      1956 non-null   object\n",
            " 2   DATE        1711 non-null   object\n",
            " 3   CONTENT     1956 non-null   object\n",
            " 4   CLASS       1956 non-null   int64 \n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 91.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemple de contenu spam\n",
        "df.CONTENT.iloc[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Pu-bAAi4QheP",
        "outputId": "c9b522f8-3d07-48d8-9b3d-8ad1cadefde8"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me shaking my sexy ass on my channel enjoy ^_^ \\ufeff'"
            ],
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            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exemple de contenu non spam\n",
        "df.CONTENT.iloc[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "P040Vh-oRp7w",
        "outputId": "0b754005-e8e8-41a6-fb45-7f0de5d8bd55"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Shakira is the best dancer'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.CLASS.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYkjrYAnRw3p",
        "outputId": "f3439573-ff1f-43ac-a02b-2c69568db4c6"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1005\n",
              "0     951\n",
              "Name: CLASS, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len('hello world')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkJjpGUlopKZ",
        "outputId": "f228e113-7c59-46d8-ad8b-4e644ce2e85a"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering\n",
        "df['CONTENT_LENGHT'] = df['CONTENT'].apply(lambda x: len(x))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "wSapZ5TIoQAh",
        "outputId": "e9c78324-6c07-4649-c3c6-95d9af05885c"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      COMMENT_ID                AUTHOR  \\\n",
              "0    LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU             Julius NM   \n",
              "1    LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A           adam riyati   \n",
              "2    LZQPQhLyRh9MSZYnf8djyk0gEF9BHDPYrrK-qCczIY8      Evgeny Murashkin   \n",
              "3            z13jhp0bxqncu512g22wvzkasxmvvzjaz04       ElNino Melendez   \n",
              "4            z13fwbwp1oujthgqj04chlngpvzmtt3r3dw                GsMega   \n",
              "..                                           ...                   ...   \n",
              "365  _2viQ_Qnc6-bMSjqyL1NKj57ROicCSJV5SwTrw-RFFA          Katie Mettam   \n",
              "366  _2viQ_Qnc6-pY-1yR6K2FhmC5i48-WuNx5CumlHLDAI  Sabina Pearson-Smith   \n",
              "367  _2viQ_Qnc6_k_n_Bse9zVhJP8tJReZpo8uM2uZfnzDs         jeffrey jules   \n",
              "368  _2viQ_Qnc6_yBt8UGMWyg3vh0PulTqcqyQtdE7d4Fl0        Aishlin Maciel   \n",
              "369  _2viQ_Qnc685RPw1aSa1tfrIuHXRvAQ2rPT9R06KTqA           Latin Bosch   \n",
              "\n",
              "                           DATE  \\\n",
              "0           2013-11-07T06:20:48   \n",
              "1           2013-11-07T12:37:15   \n",
              "2           2013-11-08T17:34:21   \n",
              "3           2013-11-09T08:28:43   \n",
              "4           2013-11-10T16:05:38   \n",
              "..                          ...   \n",
              "365  2013-07-13T13:27:39.441000   \n",
              "366  2013-07-13T13:14:30.021000   \n",
              "367  2013-07-13T12:09:31.188000   \n",
              "368  2013-07-13T11:17:52.308000   \n",
              "369  2013-07-12T22:33:27.916000   \n",
              "\n",
              "                                               CONTENT  CLASS  CONTENT_LENGHT  \n",
              "0    Huh, anyway check out this you[tube] channel: ...      1              56  \n",
              "1    Hey guys check out my new channel and our firs...      1             166  \n",
              "2               just for test I have to say murdev.com      1              38  \n",
              "3     me shaking my sexy ass on my channel enjoy ^_^ ﻿      1              48  \n",
              "4              watch?v=vtaRGgvGtWQ   Check this out .﻿      1              39  \n",
              "..                                                 ...    ...             ...  \n",
              "365  I love this song because we sing it at Camp al...      0              58  \n",
              "366  I love this song for two reasons: 1.it is abou...      0              93  \n",
              "367                                                wow      0               3  \n",
              "368                            Shakira u are so wiredo      0              23  \n",
              "369                         Shakira is the best dancer      0              26  \n",
              "\n",
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              "      <th>0</th>\n",
              "      <td>LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU</td>\n",
              "      <td>Julius NM</td>\n",
              "      <td>2013-11-07T06:20:48</td>\n",
              "      <td>Huh, anyway check out this you[tube] channel: ...</td>\n",
              "      <td>1</td>\n",
              "      <td>56</td>\n",
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              "      <th>1</th>\n",
              "      <td>LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Evgeny Murashkin</td>\n",
              "      <td>2013-11-08T17:34:21</td>\n",
              "      <td>just for test I have to say murdev.com</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
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              "      <td>me shaking my sexy ass on my channel enjoy ^_^ ﻿</td>\n",
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              "      <td>48</td>\n",
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              "      <td>z13fwbwp1oujthgqj04chlngpvzmtt3r3dw</td>\n",
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              "      <td>2013-11-10T16:05:38</td>\n",
              "      <td>watch?v=vtaRGgvGtWQ   Check this out .﻿</td>\n",
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              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>_2viQ_Qnc6-bMSjqyL1NKj57ROicCSJV5SwTrw-RFFA</td>\n",
              "      <td>Katie Mettam</td>\n",
              "      <td>2013-07-13T13:27:39.441000</td>\n",
              "      <td>I love this song because we sing it at Camp al...</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>_2viQ_Qnc6-pY-1yR6K2FhmC5i48-WuNx5CumlHLDAI</td>\n",
              "      <td>Sabina Pearson-Smith</td>\n",
              "      <td>2013-07-13T13:14:30.021000</td>\n",
              "      <td>I love this song for two reasons: 1.it is abou...</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
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              "    <tr>\n",
              "      <th>367</th>\n",
              "      <td>_2viQ_Qnc6_k_n_Bse9zVhJP8tJReZpo8uM2uZfnzDs</td>\n",
              "      <td>jeffrey jules</td>\n",
              "      <td>2013-07-13T12:09:31.188000</td>\n",
              "      <td>wow</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>_2viQ_Qnc6_yBt8UGMWyg3vh0PulTqcqyQtdE7d4Fl0</td>\n",
              "      <td>Aishlin Maciel</td>\n",
              "      <td>2013-07-13T11:17:52.308000</td>\n",
              "      <td>Shakira u are so wiredo</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
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              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>_2viQ_Qnc685RPw1aSa1tfrIuHXRvAQ2rPT9R06KTqA</td>\n",
              "      <td>Latin Bosch</td>\n",
              "      <td>2013-07-12T22:33:27.916000</td>\n",
              "      <td>Shakira is the best dancer</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1956 rows × 6 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8bd53ffd-84fd-4acb-a174-d7e8d1df0070')\"\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fonction qui donne le taux de ponctuation  dans un texte\n",
        "import string\n",
        "def count_punctuation(text):\n",
        "    binary_array = [1 for ch in text if ch in string.punctuation] \n",
        "    nb_ponctuation = sum(binary_array)\n",
        "    total = len(text) - text.count(\" \")\n",
        "    return nb_ponctuation / total"
      ],
      "metadata": {
        "id": "H7Gzhi0iq2_V"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Colonne donnant le taux de ponctuation dans un commentaire\n",
        "df['PUNCTUATION_RATE'] = df['CONTENT'].apply(lambda x: count_punctuation(x))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "ZdJp8qtiqWUI",
        "outputId": "ebf4c4b6-4877-4d4a-ff1e-793f7a642124"
      },
      "execution_count": 97,
      "outputs": [
        {
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              "                                      COMMENT_ID                AUTHOR  \\\n",
              "0    LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU             Julius NM   \n",
              "1    LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A           adam riyati   \n",
              "2    LZQPQhLyRh9MSZYnf8djyk0gEF9BHDPYrrK-qCczIY8      Evgeny Murashkin   \n",
              "3            z13jhp0bxqncu512g22wvzkasxmvvzjaz04       ElNino Melendez   \n",
              "4            z13fwbwp1oujthgqj04chlngpvzmtt3r3dw                GsMega   \n",
              "..                                           ...                   ...   \n",
              "365  _2viQ_Qnc6-bMSjqyL1NKj57ROicCSJV5SwTrw-RFFA          Katie Mettam   \n",
              "366  _2viQ_Qnc6-pY-1yR6K2FhmC5i48-WuNx5CumlHLDAI  Sabina Pearson-Smith   \n",
              "367  _2viQ_Qnc6_k_n_Bse9zVhJP8tJReZpo8uM2uZfnzDs         jeffrey jules   \n",
              "368  _2viQ_Qnc6_yBt8UGMWyg3vh0PulTqcqyQtdE7d4Fl0        Aishlin Maciel   \n",
              "369  _2viQ_Qnc685RPw1aSa1tfrIuHXRvAQ2rPT9R06KTqA           Latin Bosch   \n",
              "\n",
              "                           DATE  \\\n",
              "0           2013-11-07T06:20:48   \n",
              "1           2013-11-07T12:37:15   \n",
              "2           2013-11-08T17:34:21   \n",
              "3           2013-11-09T08:28:43   \n",
              "4           2013-11-10T16:05:38   \n",
              "..                          ...   \n",
              "365  2013-07-13T13:27:39.441000   \n",
              "366  2013-07-13T13:14:30.021000   \n",
              "367  2013-07-13T12:09:31.188000   \n",
              "368  2013-07-13T11:17:52.308000   \n",
              "369  2013-07-12T22:33:27.916000   \n",
              "\n",
              "                                               CONTENT  CLASS  CONTENT_LENGHT  \\\n",
              "0    Huh, anyway check out this you[tube] channel: ...      1              56   \n",
              "1    Hey guys check out my new channel and our firs...      1             166   \n",
              "2               just for test I have to say murdev.com      1              38   \n",
              "3     me shaking my sexy ass on my channel enjoy ^_^ ﻿      1              48   \n",
              "4              watch?v=vtaRGgvGtWQ   Check this out .﻿      1              39   \n",
              "..                                                 ...    ...             ...   \n",
              "365  I love this song because we sing it at Camp al...      0              58   \n",
              "366  I love this song for two reasons: 1.it is abou...      0              93   \n",
              "367                                                wow      0               3   \n",
              "368                            Shakira u are so wiredo      0              23   \n",
              "369                         Shakira is the best dancer      0              26   \n",
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              "     PUNCTUATION_RATE  \n",
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              "1            0.066667  \n",
              "2            0.032258  \n",
              "3            0.078947  \n",
              "4            0.090909  \n",
              "..                ...  \n",
              "365          0.043478  \n",
              "366          0.039474  \n",
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              "368          0.000000  \n",
              "369          0.000000  \n",
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              "      <td>LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU</td>\n",
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              "      <td>LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A</td>\n",
              "      <td>adam riyati</td>\n",
              "      <td>2013-11-07T12:37:15</td>\n",
              "      <td>Hey guys check out my new channel and our firs...</td>\n",
              "      <td>1</td>\n",
              "      <td>166</td>\n",
              "      <td>0.066667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LZQPQhLyRh9MSZYnf8djyk0gEF9BHDPYrrK-qCczIY8</td>\n",
              "      <td>Evgeny Murashkin</td>\n",
              "      <td>2013-11-08T17:34:21</td>\n",
              "      <td>just for test I have to say murdev.com</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>0.032258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>z13jhp0bxqncu512g22wvzkasxmvvzjaz04</td>\n",
              "      <td>ElNino Melendez</td>\n",
              "      <td>2013-11-09T08:28:43</td>\n",
              "      <td>me shaking my sexy ass on my channel enjoy ^_^ ﻿</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "      <td>0.078947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>z13fwbwp1oujthgqj04chlngpvzmtt3r3dw</td>\n",
              "      <td>GsMega</td>\n",
              "      <td>2013-11-10T16:05:38</td>\n",
              "      <td>watch?v=vtaRGgvGtWQ   Check this out .﻿</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>0.090909</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365</th>\n",
              "      <td>_2viQ_Qnc6-bMSjqyL1NKj57ROicCSJV5SwTrw-RFFA</td>\n",
              "      <td>Katie Mettam</td>\n",
              "      <td>2013-07-13T13:27:39.441000</td>\n",
              "      <td>I love this song because we sing it at Camp al...</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>0.043478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>_2viQ_Qnc6-pY-1yR6K2FhmC5i48-WuNx5CumlHLDAI</td>\n",
              "      <td>Sabina Pearson-Smith</td>\n",
              "      <td>2013-07-13T13:14:30.021000</td>\n",
              "      <td>I love this song for two reasons: 1.it is abou...</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "      <td>0.039474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>367</th>\n",
              "      <td>_2viQ_Qnc6_k_n_Bse9zVhJP8tJReZpo8uM2uZfnzDs</td>\n",
              "      <td>jeffrey jules</td>\n",
              "      <td>2013-07-13T12:09:31.188000</td>\n",
              "      <td>wow</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>_2viQ_Qnc6_yBt8UGMWyg3vh0PulTqcqyQtdE7d4Fl0</td>\n",
              "      <td>Aishlin Maciel</td>\n",
              "      <td>2013-07-13T11:17:52.308000</td>\n",
              "      <td>Shakira u are so wiredo</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>_2viQ_Qnc685RPw1aSa1tfrIuHXRvAQ2rPT9R06KTqA</td>\n",
              "      <td>Latin Bosch</td>\n",
              "      <td>2013-07-12T22:33:27.916000</td>\n",
              "      <td>Shakira is the best dancer</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1956 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90e708f8-00ba-4095-8723-e72e8702b0c1')\"\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-90e708f8-00ba-4095-8723-e72e8702b0c1 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-90e708f8-00ba-4095-8723-e72e8702b0c1');\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": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisation\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.hist(df[df['CLASS']== 1]['CONTENT_LENGHT'], alpha=0.5, label='spam')\n",
        "plt.hist(df[df['CLASS']== 0]['CONTENT_LENGHT'], alpha=0.5, label='ham')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Longueur du commentaire')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "ZiIJwii9rSks",
        "outputId": "dbe47e0e-c083-4d2d-90be-89a643fd0cf6"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df[df['CLASS']== 1]['PUNCTUATION_RATE'], alpha=0.5, label='spam')\n",
        "plt.hist(df[df['CLASS']== 0]['PUNCTUATION_RATE'], alpha=0.5, label='ham')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Taux de ponctuation')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Ulm-FBI1sac5",
        "outputId": "0ba63377-5217-46ff-f289-a28d8692f12e"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A partir de ces graphiques, nous pouvons dire que la longueur du commentaire (variable ***CONTENT_LENGTH***) a probablement un pouvoir prédictif sur le type de commentaire (spam ou ham) ce qui n'est pas tellement le cas pour le taux de ponctuation. \n",
        "\n",
        "Nous ajouterons ces 2 variables comme *features*."
      ],
      "metadata": {
        "id": "TJZh46qnsz-u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Données d'entraînement et de test\n",
        "from sklearn.model_selection import train_test_split\n",
        "seed = 123\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df[['CONTENT', 'CONTENT_LENGHT', 'PUNCTUATION_RATE']],\n",
        "    df['CLASS'],\n",
        "    test_size = 0.2,\n",
        "    random_state = seed\n",
        ")\n",
        "\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_test shape:\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oqam0nfrX98O",
        "outputId": "de5e2c6e-db7c-4d61-bf4d-3b2a53434403"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (1564, 3)\n",
            "y_train shape: (1564,)\n",
            "X_test shape: (392, 3)\n",
            "y_test shape: (392,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "W-8hK5s5Y1bK",
        "outputId": "928261b6-210f-4bdd-a1a8-5cbdeb748cfd"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               CONTENT  CONTENT_LENGHT  \\\n",
              "38   Love this song! My soccer team made a cd for o...              77   \n",
              "72                                    Sub my channel!﻿              16   \n",
              "356  I WILL NEVER FORGET THIS SONG IN MY LIFE LIKE ...             105   \n",
              "126                 Love the way you lie - Driveshaft﻿              34   \n",
              "340  *****PLEASE READ*****  Hey everyone! I&#39;m a...             351   \n",
              "..                                                 ...             ...   \n",
              "422  check it out free stuff for watching videos an...             181   \n",
              "208  HEY GUYS!!! ❤❤❤❤❤❤❤  BEFORE YOU IGNORE ME, PLE...             504   \n",
              "268                 Hello. İ am from Azerbaijan<br />﻿              34   \n",
              "251                                 dude check out psy              18   \n",
              "396  I KNOW YOU MAY NOT WANT TO READ THIS BUT pleas...             261   \n",
              "\n",
              "     PUNCTUATION_RATE  \n",
              "38           0.032787  \n",
              "72           0.071429  \n",
              "356          0.058824  \n",
              "126          0.035714  \n",
              "340          0.109929  \n",
              "..                ...  \n",
              "422          0.190476  \n",
              "208          0.052009  \n",
              "268          0.137931  \n",
              "251          0.000000  \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BSTshRp8Y7Fq",
        "outputId": "af924aed-b825-481c-a8d3-3577170e9b28"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38     0\n",
              "72     1\n",
              "356    1\n",
              "126    0\n",
              "340    1\n",
              "      ..\n",
              "422    1\n",
              "208    1\n",
              "268    0\n",
              "251    1\n",
              "396    1\n",
              "Name: CLASS, Length: 1564, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
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    {
      "cell_type": "code",
      "source": [
        "# Sac de mots avec CountVectorizer \n",
        "#source : https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vectorizer = CountVectorizer() # Essayez après avec l'argument stop_words = 'english'\n",
        "X_train_content_vec = vectorizer.fit_transform(X_train['CONTENT'])\n",
        "X_test_content_vec = vectorizer.transform(X_test['CONTENT'])"
      ],
      "metadata": {
        "id": "u2X9bysKSKw8"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_content_vec"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kd9bHMrZfdm",
        "outputId": "dfa9af05-88aa-48e8-83e8-4b0ab92103c1"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<1564x3846 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 20361 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Après transformation avec *CountVectorizer*, l'ensemble d'entraînement est une matrice de 1564 lignes (il y a 1564 commentaires) et 3846 mots, c'est-à-dire qu'il y a au total 3846 mots distincts qui apparaissent dans les commentaires de l'ensemble d'entraînement. Ces mots représentent les *features* qui seront utilisées pour entraîner les algorithmes de Classification automatique."
      ],
      "metadata": {
        "id": "32iMB7PRUkFn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Affichage des features\n",
        "vectorizer.get_feature_names()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ7hKuYxUSnK",
        "outputId": "f7cac92a-a9ae-4911-e6b6-2b9fc0716cc7"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
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              " '_o3h',\n",
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              " 'a0qouc7q48v3_qiaaabpugaaacsqar0_vgoqwqxjmpuyvkosf3k',\n",
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              " 'acting',\n",
              " 'activates',\n",
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              " 'actorid',\n",
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              " 'actresses',\n",
              " 'actual',\n",
              " 'actually',\n",
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              " 'adam',\n",
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              " 'admitting',\n",
              " 'adore',\n",
              " 'adoult',\n",
              " 'adroid',\n",
              " 'adsense',\n",
              " 'adult',\n",
              " 'adurl',\n",
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              " 'advertisements',\n",
              " 'advertisiments',\n",
              " 'advertising',\n",
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              " 'affiliated',\n",
              " 'affiliateid',\n",
              " 'afflicted',\n",
              " 'afford',\n",
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              " 'afraid',\n",
              " 'africa',\n",
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              " 'again',\n",
              " 'against',\n",
              " 'age',\n",
              " 'ago',\n",
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              " 'agreeable',\n",
              " 'ah',\n",
              " 'ahead',\n",
              " 'ahhh',\n",
              " 'ai',\n",
              " 'aid',\n",
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              " 'aimbwbfqbzg',\n",
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              " 'airlines',\n",
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              " 'amazing',\n",
              " 'amazon',\n",
              " 'ambition',\n",
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              " 'amendment',\n",
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              " 'american',\n",
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              " 'angry',\n",
              " 'animal',\n",
              " 'animals',\n",
              " 'animation',\n",
              " 'animator',\n",
              " 'anime',\n",
              " 'animes',\n",
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              " 'aod64_1ofc7seh_1pop',\n",
              " 'aplica',\n",
              " 'apocalypse',\n",
              " 'apologies',\n",
              " 'apostles',\n",
              " 'app',\n",
              " 'apparel',\n",
              " 'apparently',\n",
              " 'applause',\n",
              " 'apple',\n",
              " 'applied',\n",
              " 'applocker',\n",
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              " 'appreciated',\n",
              " 'approve',\n",
              " 'apps',\n",
              " 'arbitrate',\n",
              " 'are',\n",
              " 'aren',\n",
              " 'arguements',\n",
              " 'arive',\n",
              " 'arkglzjqup0',\n",
              " 'around',\n",
              " 'arrogant',\n",
              " 'arrowgance',\n",
              " 'art',\n",
              " 'artady',\n",
              " 'artist',\n",
              " 'artists',\n",
              " 'as',\n",
              " 'aseris',\n",
              " 'asia',\n",
              " 'asian',\n",
              " 'asinine',\n",
              " 'ask',\n",
              " 'asked',\n",
              " 'asking',\n",
              " 'aslamu',\n",
              " 'aspx',\n",
              " 'ass',\n",
              " 'assume',\n",
              " 'astauand',\n",
              " 'aswell',\n",
              " 'at',\n",
              " 'atlastatlas',\n",
              " 'attacks',\n",
              " 'attention',\n",
              " 'auburn',\n",
              " 'audio',\n",
              " 'audiojungle',\n",
              " 'audit',\n",
              " 'audition',\n",
              " 'auditiondetail_',\n",
              " 'auditioning',\n",
              " 'auditions',\n",
              " 'australia',\n",
              " 'authenticviews',\n",
              " 'authority',\n",
              " 'auto',\n",
              " 'autotune',\n",
              " 'autotuned',\n",
              " 'avaaz',\n",
              " 'available',\n",
              " 'avicii',\n",
              " 'avoid',\n",
              " 'aw',\n",
              " 'awards',\n",
              " 'away',\n",
              " 'aways',\n",
              " 'awesom',\n",
              " 'awesome',\n",
              " 'awesomeness',\n",
              " 'awesoooome',\n",
              " 'awesum',\n",
              " 'awsome',\n",
              " 'axeljonssons',\n",
              " 'axiomatic',\n",
              " 'axy665',\n",
              " 'aye',\n",
              " 'azerbaijan',\n",
              " 'b00ecvf93g',\n",
              " 'b00mppqhri',\n",
              " 'b3',\n",
              " 'b5',\n",
              " 'b5t',\n",
              " 'b7b',\n",
              " 'b8l',\n",
              " 'ba',\n",
              " 'babe',\n",
              " 'baby',\n",
              " 'back',\n",
              " 'bad',\n",
              " 'bady',\n",
              " 'ball',\n",
              " 'ballad',\n",
              " 'balls',\n",
              " 'band',\n",
              " 'bands',\n",
              " 'bang',\n",
              " 'bangers',\n",
              " 'bangladesh',\n",
              " 'barnesandnoble',\n",
              " 'bars',\n",
              " 'base',\n",
              " 'based',\n",
              " 'basketball',\n",
              " 'bass',\n",
              " 'bastard',\n",
              " 'bdp',\n",
              " 'be',\n",
              " 'beast',\n",
              " 'beat',\n",
              " 'beaties',\n",
              " 'beating',\n",
              " 'beats',\n",
              " 'beautiful',\n",
              " 'beauty',\n",
              " 'because',\n",
              " 'become',\n",
              " 'becomes',\n",
              " 'been',\n",
              " 'before',\n",
              " 'begin',\n",
              " 'beginning',\n",
              " 'behavior',\n",
              " 'behind',\n",
              " 'behold',\n",
              " 'beibs',\n",
              " 'being',\n",
              " 'belarus',\n",
              " 'belgique',\n",
              " 'belgium',\n",
              " 'believe',\n",
              " 'believemefilm',\n",
              " 'believer',\n",
              " 'bella',\n",
              " 'belle',\n",
              " 'belly',\n",
              " 'below',\n",
              " 'belrus',\n",
              " 'bengal',\n",
              " 'bennett',\n",
              " 'berzerk',\n",
              " 'besloor',\n",
              " 'best',\n",
              " 'bet',\n",
              " 'betfair',\n",
              " 'better',\n",
              " 'between',\n",
              " 'beutiful',\n",
              " 'beware',\n",
              " 'bf4',\n",
              " 'bg',\n",
              " 'bieber',\n",
              " 'big',\n",
              " 'bigboss286',\n",
              " 'bigelow',\n",
              " 'bigger',\n",
              " 'bighit',\n",
              " 'bikini',\n",
              " 'bil',\n",
              " 'bilion',\n",
              " 'billion',\n",
              " 'billions',\n",
              " 'billon',\n",
              " 'bills',\n",
              " 'binbox',\n",
              " 'bing',\n",
              " 'birtgday',\n",
              " 'birthday',\n",
              " 'bishopsgravemarker',\n",
              " 'bit',\n",
              " 'bitch',\n",
              " 'bitcoins',\n",
              " 'bite',\n",
              " 'black',\n",
              " 'blanc',\n",
              " 'blank',\n",
              " 'blast',\n",
              " 'bleach',\n",
              " 'bless',\n",
              " 'blessing',\n",
              " 'blog',\n",
              " 'blogfa',\n",
              " 'blogspot',\n",
              " 'blond',\n",
              " 'blow',\n",
              " 'blows',\n",
              " 'blue',\n",
              " 'blushing',\n",
              " 'boa',\n",
              " 'boaconic',\n",
              " 'bocilile',\n",
              " 'body',\n",
              " 'bogdan',\n",
              " 'bonus',\n",
              " 'boobs',\n",
              " 'book',\n",
              " 'bookies',\n",
              " 'bookmakers',\n",
              " 'boomerul',\n",
              " 'border',\n",
              " 'borderlands',\n",
              " 'bored',\n",
              " 'boring',\n",
              " 'born',\n",
              " 'bother',\n",
              " 'bots',\n",
              " 'bottom',\n",
              " 'bout',\n",
              " 'box',\n",
              " 'boxium',\n",
              " 'boy',\n",
              " 'boyfriend',\n",
              " 'boys',\n",
              " 'bps',\n",
              " 'br',\n",
              " 'brah',\n",
              " 'brain',\n",
              " 'brake',\n",
              " 'brand',\n",
              " 'brave',\n",
              " 'brazilian',\n",
              " 'break',\n",
              " 'breaken',\n",
              " 'breaks',\n",
              " 'brew',\n",
              " 'briefs',\n",
              " 'bring',\n",
              " 'bringing',\n",
              " 'brings',\n",
              " 'british',\n",
              " 'britishs',\n",
              " 'broken',\n",
              " 'brooooo',\n",
              " 'brother',\n",
              " 'brotherhood',\n",
              " 'brothers',\n",
              " 'brought',\n",
              " 'browser',\n",
              " 'brt0u5',\n",
              " 'brutally',\n",
              " 'bs',\n",
              " 'btw',\n",
              " 'bubblews',\n",
              " 'buchmair',\n",
              " 'bucket',\n",
              " 'bucks',\n",
              " 'buggti',\n",
              " 'build',\n",
              " 'bulgaria',\n",
              " 'bumps',\n",
              " 'bunch',\n",
              " 'burda',\n",
              " 'burder',\n",
              " 'burned',\n",
              " 'burst',\n",
              " 'business',\n",
              " 'busyglide',\n",
              " 'but',\n",
              " 'butalabs',\n",
              " 'butt',\n",
              " 'button',\n",
              " 'butts',\n",
              " 'buy',\n",
              " 'buys',\n",
              " 'bxrosr',\n",
              " 'by',\n",
              " 'c3',\n",
              " 'c349',\n",
              " 'ca',\n",
              " 'cabelo',\n",
              " 'cachebuster',\n",
              " 'call',\n",
              " 'called',\n",
              " 'calls',\n",
              " 'came',\n",
              " 'camera',\n",
              " 'cameraman',\n",
              " 'camp',\n",
              " 'campid',\n",
              " 'can',\n",
              " 'canal',\n",
              " 'cancer',\n",
              " 'canibus',\n",
              " 'cant',\n",
              " 'canvas',\n",
              " 'cap',\n",
              " 'capitalized',\n",
              " 'car',\n",
              " 'card',\n",
              " 'cards',\n",
              " 'care',\n",
              " 'career',\n",
              " 'cares',\n",
              " 'caroline',\n",
              " 'case',\n",
              " 'catch',\n",
              " 'catchy',\n",
              " 'categories',\n",
              " 'cats',\n",
              " 'cause',\n",
              " 'cazzy',\n",
              " 'cd',\n",
              " 'cd92db3f4',\n",
              " 'ce',\n",
              " 'cece',\n",
              " 'celeb',\n",
              " 'celebrate',\n",
              " 'censor',\n",
              " 'cent',\n",
              " 'central',\n",
              " 'cents',\n",
              " 'certain',\n",
              " 'certification',\n",
              " 'cevxzvsjlk8',\n",
              " 'cge',\n",
              " 'chainise',\n",
              " 'challenge',\n",
              " 'challenges',\n",
              " 'champion',\n",
              " 'chance',\n",
              " 'chanel',\n",
              " 'chanell',\n",
              " 'change',\n",
              " 'changeable',\n",
              " 'chanicka',\n",
              " 'channel',\n",
              " 'channels',\n",
              " 'channnnnnelll',\n",
              " 'chanson',\n",
              " 'chap',\n",
              " 'characterized',\n",
              " 'charity',\n",
              " 'charlie',\n",
              " 'chaste',\n",
              " 'chaîne',\n",
              " 'chcfcvzfzfbvzdr',\n",
              " 'cheat',\n",
              " 'cheats',\n",
              " 'check',\n",
              " 'checked',\n",
              " 'checking',\n",
              " 'cheer',\n",
              " 'cheers',\n",
              " 'cheetos',\n",
              " 'cheilith',\n",
              " 'chick',\n",
              " 'child',\n",
              " 'children',\n",
              " 'chillpal',\n",
              " 'chillstep',\n",
              " 'china',\n",
              " 'ching',\n",
              " 'chiptunes',\n",
              " 'choice',\n",
              " 'chooses',\n",
              " 'chorenn',\n",
              " 'chorus',\n",
              " 'christ',\n",
              " 'christianity',\n",
              " 'christians',\n",
              " 'christmas',\n",
              " 'chubby',\n",
              " 'cid',\n",
              " 'cirus',\n",
              " 'citizen',\n",
              " 'cking',\n",
              " 'claiming',\n",
              " 'claire',\n",
              " 'class',\n",
              " 'classsic',\n",
              " 'claster',\n",
              " 'clean',\n",
              " 'cleaning',\n",
              " 'cleanse',\n",
              " 'clear',\n",
              " 'click',\n",
              " 'clicked',\n",
              " 'client',\n",
              " 'clip',\n",
              " 'close',\n",
              " 'closer',\n",
              " 'clothes',\n",
              " 'clothing',\n",
              " 'club',\n",
              " 'co',\n",
              " 'coby',\n",
              " 'code',\n",
              " 'codes',\n",
              " 'codytolleson',\n",
              " 'coffee',\n",
              " 'cold',\n",
              " 'collaboration',\n",
              " 'collaborator',\n",
              " 'collection',\n",
              " 'collections',\n",
              " 'colorful',\n",
              " 'colors',\n",
              " 'columbus',\n",
              " 'column',\n",
              " 'com',\n",
              " 'come',\n",
              " 'comeback',\n",
              " 'comedy',\n",
              " 'comeing',\n",
              " 'comentars',\n",
              " 'comes',\n",
              " 'comfort',\n",
              " 'comforter',\n",
              " 'comic',\n",
              " 'coming',\n",
              " 'comment',\n",
              " 'comment_id',\n",
              " 'commenter',\n",
              " 'commenting',\n",
              " 'comments',\n",
              " 'commercial',\n",
              " 'commit',\n",
              " 'community',\n",
              " 'companions',\n",
              " 'company',\n",
              " 'compared',\n",
              " 'comparison',\n",
              " 'comparisons',\n",
              " 'competition',\n",
              " 'complaints',\n",
              " 'complete',\n",
              " 'completely',\n",
              " 'composer',\n",
              " 'computer',\n",
              " 'conceived',\n",
              " 'concert',\n",
              " 'condition',\n",
              " 'confessors',\n",
              " 'confidence',\n",
              " 'confirmed',\n",
              " 'confusing',\n",
              " 'congrasulation',\n",
              " 'congrats',\n",
              " 'congratulations',\n",
              " 'congress',\n",
              " 'conqueror',\n",
              " 'console',\n",
              " 'consolidating',\n",
              " 'constitution',\n",
              " 'constrictor',\n",
              " 'constructive',\n",
              " 'consumers',\n",
              " 'contact',\n",
              " 'content',\n",
              " 'contest',\n",
              " 'continue',\n",
              " 'conveying',\n",
              " 'convinced',\n",
              " 'cook',\n",
              " 'cool',\n",
              " 'coolest',\n",
              " 'cooooooooooooolllllllllll',\n",
              " 'cope',\n",
              " 'copied',\n",
              " 'copy',\n",
              " 'core',\n",
              " 'cost',\n",
              " 'couch',\n",
              " 'could',\n",
              " 'couldn',\n",
              " 'counsel',\n",
              " 'count',\n",
              " 'countless',\n",
              " 'countries',\n",
              " 'country',\n",
              " 'counts',\n",
              " 'couple',\n",
              " 'course',\n",
              " 'court',\n",
              " 'cover',\n",
              " 'covers',\n",
              " 'crabby',\n",
              " 'craft',\n",
              " 'crank',\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# La méthode get_feature_names() est obsolète et sera bientôt enlevée dans la future version de sklearn\n",
        "len(vectorizer.get_feature_names_out())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dboaO-eVmo2",
        "outputId": "c041bd86-d587-47ba-ae56-82ab81ae1a7f"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3846"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_content_vec"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oN5NqjEYbpar",
        "outputId": "a4daf8f0-904f-4056-9458-fc9a0c68bc44"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<392x3846 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 4771 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformation de X_train_content_vec en une dataframe\n",
        "X_train_content = pd.DataFrame(\n",
        "    X_train_content_vec.toarray(),\n",
        "    columns = vectorizer.get_feature_names_out()\n",
        ")\n",
        "X_train_content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "xNH-t1UnusBu",
        "outputId": "0a2ccb75-b1f6-4170-8687-38e790b53bc0"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      00  000  002  018  034  04  047000  05  053012  0687119038  ...  \\\n",
              "0      0    0    0    0    0   0       0   0       0           0  ...   \n",
              "1      0    0    0    0    0   0       0   0       0           0  ...   \n",
              "2      0    0    0    0    0   0       0   0       0           0  ...   \n",
              "3      0    0    0    0    0   0       0   0       0           0  ...   \n",
              "4      0    0    0    0    0   0       0   0       0           0  ...   \n",
              "...   ..  ...  ...  ...  ...  ..     ...  ..     ...         ...  ...   \n",
              "1559   0    0    0    0    0   0       0   0       0           0  ...   \n",
              "1560   0    0    0    0    0   0       0   0       0           0  ...   \n",
              "1561   0    0    0    0    0   0       0   0       0           0  ...   \n",
              "1562   0    0    0    0    0   0       0   0       0           0  ...   \n",
              "1563   0    0    0    0    0   0       0   0       0           0  ...   \n",
              "\n",
              "      ｃｏｍｍｅｎｔ  ｄａｍｎ  ｅｂａｙ  ｆａｎｃy  ｈｔｔｐ  ｉｓ  ｓｈｏｅｃｏｌｌｅｃｔｏｒ314  ｔｈｉｓ  ｕｓｒ  ｗｗｗ  \n",
              "0           0     0     0      0     0   0                 0     0    0    0  \n",
              "1           0     0     0      0     0   0                 0     0    0    0  \n",
              "2           0     0     0      0     0   0                 0     0    0    0  \n",
              "3           0     0     0      0     0   0                 0     0    0    0  \n",
              "4           0     0     0      0     0   0                 0     0    0    0  \n",
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          "metadata": {},
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    {
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        "# Transformation de X_test_content_vec en une dataframe\n",
        "X_test_content = pd.DataFrame(\n",
        "    X_test_content_vec.toarray(),\n",
        "    columns = vectorizer.get_feature_names_out()\n",
        ")\n",
        "X_test_content"
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              "<p>392 rows × 3846 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[['CONTENT_LENGHT', 'PUNCTUATION_RATE']].reset_index(drop=True)"
      ],
      "metadata": {
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          "height": 423
        },
        "id": "ygKsB0Yrwkgk",
        "outputId": "fbf20fcf-536f-4974-8b98-a754e3e237c1"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CONTENT_LENGHT  PUNCTUATION_RATE\n",
              "0                 77          0.032787\n",
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              "2                105          0.058824\n",
              "3                 34          0.035714\n",
              "4                351          0.109929\n",
              "...              ...               ...\n",
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              "1562              18          0.000000\n",
              "1563             261          0.033816\n",
              "\n",
              "[1564 rows x 2 columns]"
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-644a2a96-e88a-4146-9464-0ba571e337dd');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          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",
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              "  "
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          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensemble d'entraînement prêt pour la modélisation\n",
        "\n",
        "X_train_final = pd.concat(\n",
        "    [\n",
        "     X_train[['CONTENT_LENGHT', 'PUNCTUATION_RATE']].reset_index(drop = True),\n",
        "     X_train_content\n",
        "    ],\n",
        "    axis = 1\n",
        ")\n",
        "\n",
        "X_train_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "A2kW18YXvzay",
        "outputId": "f6929872-ae79-4aef-f290-f3d31958fbf0"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      CONTENT_LENGHT  PUNCTUATION_RATE  00  000  002  018  034  04  047000  \\\n",
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              "<p>1564 rows × 3848 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32f7f127-7d23-45d1-92de-84607f686d51')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensemble d'évaluation pour la modélisation\n",
        "\n",
        "X_test_final = pd.concat(\n",
        "    [\n",
        "     X_test[['CONTENT_LENGHT', 'PUNCTUATION_RATE']].reset_index(drop = True),\n",
        "     X_test_content\n",
        "    ],\n",
        "    axis = 1\n",
        ")\n",
        "\n",
        "X_test_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "8bBAV18Cw6Rw",
        "outputId": "234f69fd-4993-467b-b008-0a3f5e397654"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "\n",
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              "      \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",
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              "      border-radius: 50%;\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-9056ad6d-568f-4f77-9255-5333af5e88b3 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-9056ad6d-568f-4f77-9255-5333af5e88b3');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.core.common import random_state\n",
        "# Modélisation\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(random_state = seed)\n",
        "rf_fitted = rf.fit(X_train_final, y_train)"
      ],
      "metadata": {
        "id": "GmtqpFhVWSYZ"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Score\n",
        "rf_fitted.score(X_test_final, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4TgIXiobiwy",
        "outputId": "3a89a51b-dd18-4a53-8028-dc7a2c04bea5"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9591836734693877"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, rf_fitted.predict(X_test_final)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrbVYDo2ijGf",
        "outputId": "04bc1072-4ad2-4211-d97c-3d4dd7823704"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96       187\n",
            "           1       0.97      0.95      0.96       205\n",
            "\n",
            "    accuracy                           0.96       392\n",
            "   macro avg       0.96      0.96      0.96       392\n",
            "weighted avg       0.96      0.96      0.96       392\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Validation croisée\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(rf, X_train_final, y_train, cv = 3)\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fa_CeW6xb-kX",
        "outputId": "8dc3a165-d6bf-4c86-f8a0-7a4512a8c532"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.95019157, 0.95201536, 0.94817658])"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Score moyen = \", round(scores.mean(), 2), \"+/-\", scores.std())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRcn4AAsjo3e",
        "outputId": "2db4670a-ce19-4ee7-86b0-86a59b07106d"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score moyen =  0.95 +/- 0.0015678198006017592\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Faisons des inférences :"
      ],
      "metadata": {
        "id": "w6uG52jq0QyW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_comment1 = \"join me to have sex on XXX\""
      ],
      "metadata": {
        "id": "G10m8qWYz3VV"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data1 = pd.concat(\n",
        "    [\n",
        "     pd.DataFrame({'CONTENT_LENGHT':[len(new_comment1)], 'PUNCTUATION_RATE':[count_punctuation(new_comment1)]}),\n",
        "     pd.DataFrame(vectorizer.transform([new_comment1]).toarray(), columns = vectorizer.get_feature_names())\n",
        "    ],\n",
        "    axis = 1\n",
        ")\n",
        "\n",
        "new_data1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "YvV3JnwoyAA_",
        "outputId": "75c009bf-8f34-4fc7-e3f3-95840402648e"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "0  ...        0     0     0      0     0   0                 0     0    0    0  \n",
              "\n",
              "[1 rows x 3848 columns]"
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              "<p>1 rows × 3848 columns</p>\n",
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              "\n",
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              "          document.querySelector('#df-300729ca-1285-4388-a2fc-e97d6a587172 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-300729ca-1285-4388-a2fc-e97d6a587172');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_fitted.predict(new_data1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNh_B31jlSU0",
        "outputId": "96162b09-2548-497b-d907-44ba3dabba51"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_comment2 = \"What a great video! Thank you for sharing :)\"\n",
        "\n",
        "new_data2 = pd.concat(\n",
        "    [\n",
        "     pd.DataFrame({'CONTENT_LENGHT':[len(new_comment2)], 'PUNCTUATION_RATE':[count_punctuation(new_comment2)]}),\n",
        "     pd.DataFrame(vectorizer.transform([new_comment2]).toarray(), columns = vectorizer.get_feature_names_out())\n",
        "    ],\n",
        "    axis = 1\n",
        ")\n",
        "\n",
        "rf_fitted.predict(new_data2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OHBoUWGUl-NR",
        "outputId": "27cc7652-58df-4249-fd25-2962b5820216"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    }
  ]
}