deep_tweets_classification.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Frandiiile/Deep_tweets/blob/main/deep_tweets_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cuw05ojYdx1j"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJooBen7vcRH"
      },
      "source": [
        "## read data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Ni1aXtwIdx1l"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "51ILz2hBdx1l"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv('data/train.csv')\n",
        "test = pd.read_csv('data/test.csv')\n",
        "training=pd.read_csv('data/training.csv')"
      ]
    },
    {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              TweetId     Label  \\\n",
              "0  304271250237304833  Politics   \n",
              "1  304834304222064640  Politics   \n",
              "2  303568995880144898    Sports   \n",
              "3  304366580664528896    Sports   \n",
              "4  296770931098009601    Sports   \n",
              "\n",
              "                                           TweetText  \n",
              "0  '#SecKerry: The value of the @StateDept and @U...  \n",
              "1                            '@rraina1481 I fear so'  \n",
              "2  'Watch video highlights of the #wwc13 final be...  \n",
              "3  'RT @chelscanlan: At Nitro Circus at #AlbertPa...  \n",
              "4  '@cricketfox Always a good thing. Thanks for t...  "
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              "      <th>0</th>\n",
              "      <td>304271250237304833</td>\n",
              "      <td>Politics</td>\n",
              "      <td>'#SecKerry: The value of the @StateDept and @U...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>304834304222064640</td>\n",
              "      <td>Politics</td>\n",
              "      <td>'@rraina1481 I fear so'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>303568995880144898</td>\n",
              "      <td>Sports</td>\n",
              "      <td>'Watch video highlights of the #wwc13 final be...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>304366580664528896</td>\n",
              "      <td>Sports</td>\n",
              "      <td>'RT @chelscanlan: At Nitro Circus at #AlbertPa...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>Sports</td>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
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      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ehI2UD2vcRK"
      },
      "source": [
        "## Check null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rx3soe_-dx1m",
        "outputId": "de1dcf4c-e3b3-40fd-e7d4-49997624ef28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TweetId      0\n",
              "Label        0\n",
              "TweetText    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "train.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "FNoyssQUdx1m",
        "outputId": "0d235292-cc61-479f-b978-b7bdb6a52ffb"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TweetId      0\n",
              "TweetText    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "test.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmvQniDvdx1n",
        "outputId": "8485c59e-1cfe-4a27-b457-724668f0a2d7"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6525, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UfOve4iGvcRM"
      },
      "source": [
        "## Data cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        "colab": {
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        },
        "id": "fbCKp_Rtdx1n",
        "outputId": "5d89c398-73ba-4218-a91f-3c2db9114a26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-c6469047054e>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  train['TweetText'] = train['TweetText'].str.replace('.', '')\n",
            "<ipython-input-7-c6469047054e>:4: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  train['TweetText'] = train['TweetText'].str.replace('?', '')\n",
            "<ipython-input-7-c6469047054e>:5: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  train['TweetText'] = train['TweetText'].str.replace('(', '')\n",
            "<ipython-input-7-c6469047054e>:6: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  train['TweetText'] = train['TweetText'].str.replace(')', '')\n"
          ]
        }
      ],
      "source": [
        "def clean_column(train):\n",
        "    train['TweetText'] = train['TweetText'].str.replace('.', '')\n",
        "    train['TweetText'] = train['TweetText'].str.replace('!', '')\n",
        "    train['TweetText'] = train['TweetText'].str.replace('?', '')\n",
        "    train['TweetText'] = train['TweetText'].str.replace('(', '')\n",
        "    train['TweetText'] = train['TweetText'].str.replace(')', '')\n",
        "    train['TweetText'] = train['TweetText'].str.replace('-', '')\n",
        "clean_column(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "hXulVhr4dx1n"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "def clean_Text(text):\n",
        "    text=re.sub('@[A-Za-z0–9]+','',text) #Removing @mentions\n",
        "    text=re.sub('#', '', text) # Removing '#' hash tag\n",
        "    text=re.sub('RT[\\s]+', '', text) # Removing RT\n",
        "    text=re.sub('https?:\\/\\/\\S+', '', text) # Removing hyperlink\n",
        "    text=re.sub(':', '', text)\n",
        "    text=re.sub(\"'\", '', text)\n",
        "    text=re.sub(\",\", '', text)\n",
        "    text=re.sub(\";\", '', text)\n",
        "    text=re.sub(\"&\", '', text)\n",
        "    text=re.sub(\"``\", '', text)\n",
        "    text=re.sub('\"', '', text)\n",
        "    return text\n",
        "train['TweetText']=train['TweetText'].apply(clean_Text)"
      ]
    },
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      "execution_count": 9,
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            "text/plain": [
              "              TweetId     Label  \\\n",
              "0  304271250237304833  Politics   \n",
              "1  304834304222064640  Politics   \n",
              "2  303568995880144898    Sports   \n",
              "3  304366580664528896    Sports   \n",
              "4  296770931098009601    Sports   \n",
              "\n",
              "                                           TweetText  \n",
              "0  SecKerry The value of the  and  is measured no...  \n",
              "1                                     1481 I fear so  \n",
              "2  Watch video highlights of the wwc13 final betw...  \n",
              "3   At Nitro Circus at AlbertPark  theymakeitlook...  \n",
              "4       Always a good thing Thanks for the feedback   "
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              "      <td>Always a good thing Thanks for the feedback</td>\n",
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              "      margin-bottom: 4px;\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-6a8811fd-b813-4bdd-89f3-fadce9ffdae2 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-6a8811fd-b813-4bdd-89f3-fadce9ffdae2');\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",
              "\n",
              "\n",
              "<div id=\"df-6c12c22e-e257-4314-9ad3-3b69860825ee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c12c22e-e257-4314-9ad3-3b69860825ee')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6c12c22e-e257-4314-9ad3-3b69860825ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8ud0sBfdx1o",
        "outputId": "a01ab694-6c20-4374-8a27-bc700401ed47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sV9gwEsZvcRN"
      },
      "source": [
        "## Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5uoF9yqTdx1p",
        "outputId": "63229159-d971-4003-9a80-f3516da080c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       ['SecKerry', 'The', 'value', 'of', 'the', '', ...\n",
              "1                             ['1481', 'I', 'fear', 'so']\n",
              "2       ['Watch', 'video', 'highlights', 'of', 'the', ...\n",
              "3       ['', 'At', 'Nitro', 'Circus', 'at', 'AlbertPar...\n",
              "4       ['', 'Always', 'a', 'good', 'thing', 'Thanks',...\n",
              "                              ...                        \n",
              "6520    ['Photo', 'PM', 'has', 'laid', 'a', 'wreath', ...\n",
              "6521    ['The', 'secret', 'of', 'the', 'Chennai', 'pit...\n",
              "6522        ['', 'he', 'isnt', 'on', 'Twitter', 'either']\n",
              "6523    ['Which', 'England', 'player', 'would', 'you',...\n",
              "6524    ['Dmitry', 'Medvedev', 'expressed', 'condolenc...\n",
              "Name: TweetText, Length: 6525, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "def tolist(x):\n",
        "    return x.split(' ')\n",
        "train['TweetText']=train['TweetText'].apply(tolist)\n",
        "train['TweetText'].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "riGsi2jfdx1p"
      },
      "outputs": [],
      "source": [
        "#remove null strings\n",
        "def remove_empty(x):\n",
        "    return [item for item in x if item != '']\n",
        "train['TweetText']=train['TweetText'].apply(remove_empty)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VxkNn-PPdx1p",
        "outputId": "50397978-9cc0-4c3b-93fc-8f0b0bab0689"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              TweetId     Label  \\\n",
              "0  304271250237304833  Politics   \n",
              "1  304834304222064640  Politics   \n",
              "2  303568995880144898    Sports   \n",
              "3  304366580664528896    Sports   \n",
              "4  296770931098009601    Sports   \n",
              "\n",
              "                                           TweetText  \n",
              "0  [SecKerry, The, value, of, the, and, is, measu...  \n",
              "1                                [1481, I, fear, so]  \n",
              "2  [Watch, video, highlights, of, the, wwc13, fin...  \n",
              "3  [At, Nitro, Circus, at, AlbertPark, theymakeit...  \n",
              "4  [Always, a, good, thing, Thanks, for, the, fee...  "
            ],
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TweetId</th>\n",
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              "      <th>0</th>\n",
              "      <td>304271250237304833</td>\n",
              "      <td>Politics</td>\n",
              "      <td>[SecKerry, The, value, of, the, and, is, measu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>304834304222064640</td>\n",
              "      <td>Politics</td>\n",
              "      <td>[1481, I, fear, so]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>303568995880144898</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[Watch, video, highlights, of, the, wwc13, fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>304366580664528896</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[At, Nitro, Circus, at, AlbertPark, theymakeit...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>296770931098009601</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[Always, a, good, thing, Thanks, for, the, fee...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90482cda-3e87-4402-b7d0-e09e043adf2e')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-90482cda-3e87-4402-b7d0-e09e043adf2e 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-90482cda-3e87-4402-b7d0-e09e043adf2e');\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",
              "\n",
              "\n",
              "<div id=\"df-56b727cd-5cfe-4ea5-a704-65fdb92a5b03\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56b727cd-5cfe-4ea5-a704-65fdb92a5b03')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-56b727cd-5cfe-4ea5-a704-65fdb92a5b03 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2MzJxsPvcRO"
      },
      "source": [
        "## lowercase all words in train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "D-MjuVEodx1p",
        "outputId": "4be414ad-babc-4ac1-d709-21618774c93e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-defe20fe5eca>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['TweetText'][i] = [x.lower() for x in data['TweetText'][i]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 TweetId     Label  \\\n",
              "0     304271250237304833  Politics   \n",
              "1     304834304222064640  Politics   \n",
              "2     303568995880144898    Sports   \n",
              "3     304366580664528896    Sports   \n",
              "4     296770931098009601    Sports   \n",
              "...                  ...       ...   \n",
              "6520  296675082267410433  Politics   \n",
              "6521  306677536195231746    Sports   \n",
              "6522  306451295307431937    Sports   \n",
              "6523  306088574221176832    Sports   \n",
              "6524  277090953242759169  Politics   \n",
              "\n",
              "                                              TweetText  \n",
              "0     [seckerry, the, value, of, the, and, is, measu...  \n",
              "1                                   [1481, i, fear, so]  \n",
              "2     [watch, video, highlights, of, the, wwc13, fin...  \n",
              "3     [at, nitro, circus, at, albertpark, theymakeit...  \n",
              "4     [always, a, good, thing, thanks, for, the, fee...  \n",
              "...                                                 ...  \n",
              "6520  [photo, pm, has, laid, a, wreath, at, martyrs,...  \n",
              "6521  [the, secret, of, the, chennai, pitch, crumbli...  \n",
              "6522                    [he, isnt, on, twitter, either]  \n",
              "6523  [which, england, player, would, you, take, out...  \n",
              "6524  [dmitry, medvedev, expressed, condolences, to,...  \n",
              "\n",
              "[6525 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-670f9146-ad35-4cd2-b10f-d28d4540f211\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TweetId</th>\n",
              "      <th>Label</th>\n",
              "      <th>TweetText</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>304271250237304833</td>\n",
              "      <td>Politics</td>\n",
              "      <td>[seckerry, the, value, of, the, and, is, measu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>304834304222064640</td>\n",
              "      <td>Politics</td>\n",
              "      <td>[1481, i, fear, so]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>303568995880144898</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[watch, video, highlights, of, the, wwc13, fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>304366580664528896</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[at, nitro, circus, at, albertpark, theymakeit...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>296770931098009601</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[always, a, good, thing, thanks, for, the, fee...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6520</th>\n",
              "      <td>296675082267410433</td>\n",
              "      <td>Politics</td>\n",
              "      <td>[photo, pm, has, laid, a, wreath, at, martyrs,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6521</th>\n",
              "      <td>306677536195231746</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[the, secret, of, the, chennai, pitch, crumbli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6522</th>\n",
              "      <td>306451295307431937</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[he, isnt, on, twitter, either]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6523</th>\n",
              "      <td>306088574221176832</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[which, england, player, would, you, take, out...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6524</th>\n",
              "      <td>277090953242759169</td>\n",
              "      <td>Politics</td>\n",
              "      <td>[dmitry, medvedev, expressed, condolences, to,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6525 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-670f9146-ad35-4cd2-b10f-d28d4540f211')\"\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 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-670f9146-ad35-4cd2-b10f-d28d4540f211 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-670f9146-ad35-4cd2-b10f-d28d4540f211');\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",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fc243c8c-67d1-4890-8b49-07f1889bcfa5 button');\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "def lower_case(data):\n",
        "    for i in range(data.shape[0]) :\n",
        "        data['TweetText'][i] = [x.lower() for x in data['TweetText'][i]]\n",
        "    return data\n",
        "lower_case(train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeEOVEIFvcRO"
      },
      "source": [
        "## removing stop words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ROxLWQEdx1p",
        "outputId": "f7057563-2c0f-4260-f4c9-646f4a229075"
      },
      "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": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "x8nxVxF5dx1p"
      },
      "outputs": [],
      "source": [
        "stop_words = ['amp','the','i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n",
        "def remove_stopwords(data):\n",
        "    for i in range(data.shape[0]) :\n",
        "        for word in  stop_words:\n",
        "            if word in data['TweetText'][i]:\n",
        "                data['TweetText'][i].remove(word)\n",
        "    return data\n",
        "train = remove_stopwords(train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1Boxa7vvcRP"
      },
      "source": [
        "## Lemmatization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAD6_6m_dx1p",
        "outputId": "f7af51cd-fd3f-4617-afbd-91519a61d2b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "NLTK Downloader\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> d\n",
            "\n",
            "Download which package (l=list; x=cancel)?\n",
            "  Identifier> \n",
            "Downloader> \n",
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> \n",
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> \n",
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> d\n",
            "\n",
            "Download which package (l=list; x=cancel)?\n",
            "  Identifier> \n",
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> d\n",
            "\n",
            "Download which package (l=list; x=cancel)?\n",
            "  Identifier> q\n",
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> d\n",
            "\n",
            "Download which package (l=list; x=cancel)?\n",
            "  Identifier> l\n",
            "Packages:\n",
            "  [ ] abc................. Australian Broadcasting Commission 2006\n",
            "  [ ] alpino.............. Alpino Dutch Treebank\n",
            "  [ ] averaged_perceptron_tagger Averaged Perceptron Tagger\n",
            "  [ ] averaged_perceptron_tagger_ru Averaged Perceptron Tagger (Russian)\n",
            "  [ ] basque_grammars..... Grammars for Basque\n",
            "  [ ] bcp47............... BCP-47 Language Tags\n",
            "  [ ] biocreative_ppi..... BioCreAtIvE (Critical Assessment of Information\n",
            "                           Extraction Systems in Biology)\n",
            "  [ ] bllip_wsj_no_aux.... BLLIP Parser: WSJ Model\n",
            "  [ ] book_grammars....... Grammars from NLTK Book\n",
            "  [ ] brown............... Brown Corpus\n",
            "  [ ] brown_tei........... Brown Corpus (TEI XML Version)\n",
            "  [ ] cess_cat............ CESS-CAT Treebank\n",
            "  [ ] cess_esp............ CESS-ESP Treebank\n",
            "  [ ] chat80.............. Chat-80 Data Files\n",
            "  [ ] city_database....... City Database\n",
            "  [ ] cmudict............. The Carnegie Mellon Pronouncing Dictionary (0.6)\n",
            "  [ ] comparative_sentences Comparative Sentence Dataset\n",
            "  [ ] comtrans............ ComTrans Corpus Sample\n",
            "  [ ] conll2000........... CONLL 2000 Chunking Corpus\n",
            "Hit Enter to continue: \n",
            "  [ ] conll2002........... CONLL 2002 Named Entity Recognition Corpus\n",
            "  [ ] conll2007........... Dependency Treebanks from CoNLL 2007 (Catalan\n",
            "                           and Basque Subset)\n",
            "  [ ] crubadan............ Crubadan Corpus\n",
            "  [ ] dependency_treebank. Dependency Parsed Treebank\n",
            "  [ ] dolch............... Dolch Word List\n",
            "  [ ] europarl_raw........ Sample European Parliament Proceedings Parallel\n",
            "                           Corpus\n",
            "  [ ] extended_omw........ Extended Open Multilingual WordNet\n",
            "  [ ] floresta............ Portuguese Treebank\n",
            "  [ ] framenet_v15........ FrameNet 1.5\n",
            "  [ ] framenet_v17........ FrameNet 1.7\n",
            "  [ ] gazetteers.......... Gazeteer Lists\n",
            "  [ ] genesis............. Genesis Corpus\n",
            "  [ ] gutenberg........... Project Gutenberg Selections\n",
            "  [ ] ieer................ NIST IE-ER DATA SAMPLE\n",
            "  [ ] inaugural........... C-Span Inaugural Address Corpus\n",
            "  [ ] indian.............. Indian Language POS-Tagged Corpus\n",
            "  [ ] jeita............... JEITA Public Morphologically Tagged Corpus (in\n",
            "                           ChaSen format)\n",
            "  [ ] kimmo............... PC-KIMMO Data Files\n",
            "Hit Enter to continue: \n",
            "  [ ] knbc................ KNB Corpus (Annotated blog corpus)\n",
            "  [ ] large_grammars...... Large context-free and feature-based grammars\n",
            "                           for parser comparison\n",
            "  [ ] lin_thesaurus....... Lin's Dependency Thesaurus\n",
            "  [ ] mac_morpho.......... MAC-MORPHO: Brazilian Portuguese news text with\n",
            "                           part-of-speech tags\n",
            "  [ ] machado............. Machado de Assis -- Obra Completa\n",
            "  [ ] masc_tagged......... MASC Tagged Corpus\n",
            "  [ ] maxent_ne_chunker... ACE Named Entity Chunker (Maximum entropy)\n",
            "  [ ] maxent_treebank_pos_tagger Treebank Part of Speech Tagger (Maximum entropy)\n",
            "  [ ] moses_sample........ Moses Sample Models\n",
            "  [ ] movie_reviews....... Sentiment Polarity Dataset Version 2.0\n",
            "  [ ] mte_teip5........... MULTEXT-East 1984 annotated corpus 4.0\n",
            "  [ ] mwa_ppdb............ The monolingual word aligner (Sultan et al.\n",
            "                           2015) subset of the Paraphrase Database.\n",
            "  [ ] names............... Names Corpus, Version 1.3 (1994-03-29)\n",
            "  [ ] nombank.1.0......... NomBank Corpus 1.0\n",
            "  [ ] nonbreaking_prefixes Non-Breaking Prefixes (Moses Decoder)\n",
            "  [ ] nps_chat............ NPS Chat\n",
            "  [ ] omw-1.4............. Open Multilingual Wordnet\n",
            "  [ ] omw................. Open Multilingual Wordnet\n",
            "Hit Enter to continue: \n",
            "  [ ] opinion_lexicon..... Opinion Lexicon\n",
            "  [ ] panlex_swadesh...... PanLex Swadesh Corpora\n",
            "  [ ] paradigms........... Paradigm Corpus\n",
            "  [ ] pe08................ Cross-Framework and Cross-Domain Parser\n",
            "                           Evaluation Shared Task\n",
            "  [ ] perluniprops........ perluniprops: Index of Unicode Version 7.0.0\n",
            "                           character properties in Perl\n",
            "  [ ] pil................. The Patient Information Leaflet (PIL) Corpus\n",
            "  [ ] pl196x.............. Polish language of the XX century sixties\n",
            "  [ ] porter_test......... Porter Stemmer Test Files\n",
            "  [ ] ppattach............ Prepositional Phrase Attachment Corpus\n",
            "  [ ] problem_reports..... Problem Report Corpus\n",
            "  [ ] product_reviews_1... Product Reviews (5 Products)\n",
            "  [ ] product_reviews_2... Product Reviews (9 Products)\n",
            "  [ ] propbank............ Proposition Bank Corpus 1.0\n",
            "  [ ] pros_cons........... Pros and Cons\n",
            "  [ ] ptb................. Penn Treebank\n",
            "  [ ] qc.................. Experimental Data for Question Classification\n",
            "  [ ] reuters............. The Reuters-21578 benchmark corpus, ApteMod\n",
            "                           version\n",
            "  [ ] rslp................ RSLP Stemmer (Removedor de Sufixos da Lingua\n",
            "                           Portuguesa)\n",
            "Hit Enter to continue: \n",
            "  [ ] rte................. PASCAL RTE Challenges 1, 2, and 3\n",
            "  [ ] sample_grammars..... Sample Grammars\n",
            "  [ ] semcor.............. SemCor 3.0\n",
            "  [ ] senseval............ SENSEVAL 2 Corpus: Sense Tagged Text\n",
            "  [ ] sentence_polarity... Sentence Polarity Dataset v1.0\n",
            "  [ ] sentiwordnet........ SentiWordNet\n",
            "  [ ] shakespeare......... Shakespeare XML Corpus Sample\n",
            "  [ ] sinica_treebank..... Sinica Treebank Corpus Sample\n",
            "  [ ] smultron............ SMULTRON Corpus Sample\n",
            "  [ ] snowball_data....... Snowball Data\n",
            "  [ ] spanish_grammars.... Grammars for Spanish\n",
            "  [ ] state_union......... C-Span State of the Union Address Corpus\n",
            "  [ ] subjectivity........ Subjectivity Dataset v1.0\n",
            "  [ ] swadesh............. Swadesh Wordlists\n",
            "  [ ] switchboard......... Switchboard Corpus Sample\n",
            "  [ ] tagsets............. Help on Tagsets\n",
            "  [ ] timit............... TIMIT Corpus Sample\n",
            "  [ ] toolbox............. Toolbox Sample Files\n",
            "  [ ] treebank............ Penn Treebank Sample\n",
            "  [ ] twitter_samples..... Twitter Samples\n",
            "  [ ] udhr2............... Universal Declaration of Human Rights Corpus\n",
            "                           (Unicode Version)\n",
            "Hit Enter to continue: \n",
            "  [ ] udhr................ Universal Declaration of Human Rights Corpus\n",
            "  [ ] unicode_samples..... Unicode Samples\n",
            "  [ ] universal_tagset.... Mappings to the Universal Part-of-Speech Tagset\n",
            "  [ ] universal_treebanks_v20 Universal Treebanks Version 2.0\n",
            "  [ ] vader_lexicon....... VADER Sentiment Lexicon\n",
            "  [ ] verbnet3............ VerbNet Lexicon, Version 3.3\n",
            "  [ ] verbnet............. VerbNet Lexicon, Version 2.1\n",
            "  [ ] webtext............. Web Text Corpus\n",
            "  [ ] wmt15_eval.......... Evaluation data from WMT15\n",
            "  [ ] word2vec_sample..... Word2Vec Sample\n",
            "  [ ] wordnet2021......... Open English Wordnet 2021\n",
            "  [ ] wordnet2022......... Open English Wordnet 2022\n",
            "  [ ] wordnet31........... Wordnet 3.1\n",
            "  [ ] wordnet_ic.......... WordNet-InfoContent\n",
            "  [ ] words............... Word Lists\n",
            "  [ ] ycoe................ York-Toronto-Helsinki Parsed Corpus of Old\n",
            "                           English Prose\n",
            "\n",
            "Collections:\n",
            "  [P] all-corpora......... All the corpora\n",
            "  [P] all-nltk............ All packages available on nltk_data gh-pages\n",
            "                           branch\n",
            "Hit Enter to continue: \n",
            "  [P] all................. All packages\n",
            "  [P] book................ Everything used in the NLTK Book\n",
            "  [P] popular............. Popular packages\n",
            "  [ ] tests............... Packages for running tests\n",
            "  [ ] third-party......... Third-party data packages\n",
            "\n",
            "([*] marks installed packages; [P] marks partially installed collections)\n",
            "\n",
            "Download which package (l=list; x=cancel)?\n",
            "  Identifier> \n",
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> q\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#nltk dependencies\n",
        "nltk.download('wordnet')\n",
        "nltk.download()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "-LAe3ceOdx1q"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "nltk.data.path.append(\"/Users/aithassouneyouness/nltk_data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TUwrwQ5edx1q",
        "outputId": "e08258f4-61de-48ae-8dd6-fb19d1819488"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-da73a308088f>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['TweetText'][i] = lemmatized_tweet\n"
          ]
        }
      ],
      "source": [
        "wnl = nltk.WordNetLemmatizer()\n",
        "\n",
        "def lemmatization(data):\n",
        "    for i in range(data.shape[0]):\n",
        "        lemmatized_tweet = []\n",
        "        for word in data['TweetText'][i]:\n",
        "            lemmatized_word = wnl.lemmatize(word)\n",
        "            lemmatized_tweet.append(lemmatized_word)\n",
        "        data['TweetText'][i] = lemmatized_tweet\n",
        "    return data\n",
        "\n",
        "train = lemmatization(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "B5F6RlrzvcRP",
        "outputId": "c0f468e4-2322-49c6-eda3-566eca4ed569"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              TweetId     Label  \\\n",
              "0  304271250237304833  Politics   \n",
              "1  304834304222064640  Politics   \n",
              "2  303568995880144898    Sports   \n",
              "3  304366580664528896    Sports   \n",
              "4  296770931098009601    Sports   \n",
              "\n",
              "                                           TweetText  \n",
              "0  [seckerry, value, measured, dollar, in, term, ...  \n",
              "1                                       [1481, fear]  \n",
              "2  [watch, video, highlight, wwc13, final, austra...  \n",
              "3  [nitro, circus, at, albertpark, theymakeitlook...  \n",
              "4            [always, good, thing, thanks, feedback]  "
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              "      <td>Politics</td>\n",
              "      <td>[1481, fear]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>303568995880144898</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[watch, video, highlight, wwc13, final, austra...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>304366580664528896</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[nitro, circus, at, albertpark, theymakeitlook...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>296770931098009601</td>\n",
              "      <td>Sports</td>\n",
              "      <td>[always, good, thing, thanks, feedback]</td>\n",
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              "\n",
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              "    .colab-df-buttons div {\n",
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              "\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-86cab012-9c13-471b-a516-e9e5940e35fd 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-86cab012-9c13-471b-a516-e9e5940e35fd');\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed2b2831-8a58-42be-9400-3f997b4f0ea3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EphM1bp2vcRP"
      },
      "source": [
        "## Exploratory data analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jg1NVEDIvcRQ"
      },
      "source": [
        "### check if our data is balanced or not"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amdCMbJ-vcRQ",
        "outputId": "c26f8d3f-afaa-4ddf-b0f6-4cc18229eb6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sports      3325\n",
              "Politics    3200\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train['Label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Gua3I4hXvcRQ",
        "outputId": "57904eb5-453b-4910-c7de-5c9ed6e4f951"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Label', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x=\"Label\",data =train, palette=[\"#FF5733\", \"#3399FF\", \"#33FF7A\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioVFqZdBvcRQ"
      },
      "source": [
        "### have idea about the frequency of words in each label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPKEH8EOvcRQ",
        "outputId": "b2c33f24-0129-43cb-a1ff-4ac479132fc6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{('seckerry', 'Politics'): 51,\n",
              " ('value', 'Politics'): 8,\n",
              " ('measured', 'Politics'): 2,\n",
              " ('dollar', 'Politics'): 9,\n",
              " ('in', 'Politics'): 122,\n",
              " ('term', 'Politics'): 19,\n",
              " ('of', 'Politics'): 216,\n",
              " ('deepest', 'Politics'): 2,\n",
              " ('american', 'Politics'): 65,\n",
              " ('1481', 'Politics'): 4,\n",
              " ('fear', 'Politics'): 6,\n",
              " ('watch', 'Sports'): 79,\n",
              " ('video', 'Sports'): 27,\n",
              " ('highlight', 'Sports'): 24,\n",
              " ('wwc13', 'Sports'): 30,\n",
              " ('final', 'Sports'): 155,\n",
              " ('australia', 'Sports'): 104,\n",
              " ('west', 'Sports'): 25,\n",
              " ('indie', 'Sports'): 16,\n",
              " ('nitro', 'Sports'): 2,\n",
              " ('circus', 'Sports'): 2,\n",
              " ('at', 'Sports'): 25,\n",
              " ('albertpark', 'Sports'): 8,\n",
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              " ('quarterfinal', 'Sports'): 8,\n",
              " ('date', 'Sports'): 12,\n",
              " ('set', 'Sports'): 54,\n",
              " ('bupass', 'Sports'): 6,\n",
              " ('langer', 'Sports'): 1,\n",
              " ('rapt', 'Sports'): 1,\n",
              " ('warrior', 'Sports'): 5,\n",
              " ('fightback', 'Sports'): 4,\n",
              " ('waca', 'Sports'): 6,\n",
              " ('read', 'Sports'): 28,\n",
              " ('follow', 'Politics'): 35,\n",
              " ('update', 'Politics'): 21,\n",
              " ('blog', 'Politics'): 13,\n",
              " ('straight', 'Politics'): 3,\n",
              " ('bcim', 'Politics'): 52,\n",
              " ('car', 'Politics'): 57,\n",
              " ('rally', 'Politics'): 67,\n",
              " ('tumblr', 'Politics'): 1,\n",
              " ('wake', 'Sports'): 3,\n",
              " ('forest', 'Sports'): 2,\n",
              " ('dropped', 'Sports'): 10,\n",
              " ('miami', 'Sports'): 4,\n",
              " ('year', 'Sports'): 91,\n",
              " ('ncaa', 'Sports'): 1,\n",
              " ('bracket', 'Sports'): 3,\n",
              " ('going', 'Sports'): 44,\n",
              " ('hardest', 'Sports'): 1,\n",
              " ('multiple', 'Sports'): 1,\n",
              " ('choice', 'Sports'): 5,\n",
              " ('ever', 'Sports'): 17,\n",
              " ('photoset', 'Politics'): 1,\n",
              " ('social', 'Politics'): 20,\n",
              " ('state', 'Politics'): 103,\n",
              " ('union', 'Politics'): 38,\n",
              " ('stats', 'Politics'): 4,\n",
              " ('according', 'Politics'): 5,\n",
              " ('google', 'Politics'): 2,\n",
              " ('four', 'Politics'): 34,\n",
              " ('rising', 'Politics'): 7,\n",
              " ('search', 'Politics'): 4,\n",
              " ('january', 'Politics'): 50,\n",
              " ('24', 'Politics'): 9,\n",
              " ('2013am', 'Politics'): 15,\n",
              " ('two', 'Sports'): 60,\n",
              " ('_champ', 'Sports'): 4,\n",
              " ('manager', 'Sports'): 30,\n",
              " ('\\\\u2019s', 'Sports'): 3,\n",
              " ('darren', 'Sports'): 4,\n",
              " ('ferguson', 'Sports'): 3,\n",
              " ('_official\\\\u2019s', 'Sports'): 1,\n",
              " ('david', 'Sports'): 17,\n",
              " ('flitcroft', 'Sports'): 1,\n",
              " ('podcast', 'Sports'): 8,\n",
              " ('know', 'Politics'): 33,\n",
              " ('i', 'Politics'): 13,\n",
              " ('seen', 'Politics'): 9,\n",
              " ('it', 'Politics'): 11,\n",
              " ('good', 'Politics'): 26,\n",
              " ('democrat', 'Politics'): 3,\n",
              " ('come', 'Politics'): 26,\n",
              " ('together', 'Politics'): 17,\n",
              " ('republican', 'Politics'): 7,\n",
              " ('solve', 'Politics'): 6,\n",
              " ('big', 'Politics'): 11,\n",
              " ('problem', 'Politics'): 20,\n",
              " ('need', 'Politics'): 75,\n",
              " ('leadership', 'Politics'): 16,\n",
              " ('israel', 'Politics'): 7,\n",
              " ('former', 'Politics'): 25,\n",
              " ('negotiator', 'Politics'): 2,\n",
              " ('damascus', 'Politics'): 1,\n",
              " ('israeli', 'Politics'): 6,\n",
              " ('air', 'Politics'): 6,\n",
              " ('force', 'Politics'): 15,\n",
              " ('struck', 'Politics'): 1,\n",
              " ('syria', 'Politics'): 19,\n",
              " ('whats', 'Politics'): 9,\n",
              " ('next', 'Politics'): 29,\n",
              " ('21', 'Politics'): 11,\n",
              " ('daily', 'Sports'): 4,\n",
              " ('aegon', 'Sports'): 20,\n",
              " ('international', 'Sports'): 8,\n",
              " ('get', 'Sports'): 120,\n",
              " ('calling', 'Sports'): 7,\n",
              " ('0844', 'Sports'): 2,\n",
              " ('581', 'Sports'): 2,\n",
              " ('3015', 'Sports'): 2,\n",
              " ('ayaz', 'Sports'): 2,\n",
              " ('active', 'Sports'): 2,\n",
              " ('twitter', 'Sports'): 20,\n",
              " ('exciting', 'Sports'): 4,\n",
              " ('quiz', 'Sports'): 5,\n",
              " ('activity', 'Sports'): 2,\n",
              " ('lined', 'Sports'): 1,\n",
              " ('pls', 'Sports'): 2,\n",
              " ('let', 'Sports'): 22,\n",
              " ('ur', 'Sports'): 3,\n",
              " ('halftime', 'Sports'): 2,\n",
              " ('01', 'Sports'): 12,\n",
              " ('message', 'Politics'): 15,\n",
              " ('nelsonmandela', 'Politics'): 135,\n",
              " ('announcing', 'Politics'): 5,\n",
              " ('1999', 'Politics'): 3,\n",
              " ('international', 'Politics'): 24,\n",
              " ('older', 'Politics'): 2,\n",
              " ('person', 'Politics'): 10,\n",
              " ('lifeofpi', 'Politics'): 2,\n",
              " ('undoubtedly', 'Politics'): 2,\n",
              " ('best', 'Politics'): 27,\n",
              " ('movie', 'Politics'): 4,\n",
              " ('cant', 'Politics'): 13,\n",
              " ('wait', 'Politics'): 2,\n",
              " ('watch', 'Politics'): 70,\n",
              " ('djangounchained', 'Politics'): 1,\n",
              " ('though', 'Politics'): 4,\n",
              " ('inaugurated', 'Politics'): 3,\n",
              " ('selfdefense', 'Politics'): 1,\n",
              " ('workshop', 'Politics'): 6,\n",
              " ('female', 'Politics'): 1,\n",
              " ('student', 'Politics'): 15,\n",
              " ('organized', 'Politics'): 6,\n",
              " ('colaba', 'Politics'): 2,\n",
              " ('30', 'Sports'): 30,\n",
              " ('fulham', 'Sports'): 11,\n",
              " ('wellworked', 'Sports'): 1,\n",
              " ('host', 'Sports'): 11,\n",
              " ('enterprising', 'Sports'): 1,\n",
              " ('from', 'Sports'): 10,\n",
              " ('robert', 'Sports'): 4,\n",
              " ('williams', 'Sports'): 11,\n",
              " ('cfcu18', 'Sports'): 7,\n",
              " ('22', 'Politics'): 13,\n",
              " ('2013pm', 'Politics'): 20,\n",
              " ('learnt', 'Politics'): 2,\n",
              " ('plenary', 'Politics'): 6,\n",
              " ('highlight', 'Politics'): 7,\n",
              " ('missed', 'Sports'): 13,\n",
              " ('talking', 'Sports'): 5,\n",
              " ('aegonchampionships', 'Sports'): 7,\n",
              " ('rallyagainstcancer', 'Sports'): 5,\n",
              " ('0838', 'Sports'): 1,\n",
              " ('\\\\u2019s', 'Politics'): 19,\n",
              " ('toward', 'Politics'): 4,\n",
              " ('china', 'Politics'): 33,\n",
              " ('hurting', 'Politics'): 3,\n",
              " ('small', 'Politics'): 13,\n",
              " ('can\\\\u2019t', 'Politics'): 30,\n",
              " ('afford', 'Politics'): 28,\n",
              " ('another', 'Politics'): 20,\n",
              " ('4', 'Politics'): 44,\n",
              " ('like', 'Politics'): 50,\n",
              " ('last', 'Politics'): 46,\n",
              " ('thought', 'Sports'): 12,\n",
              " ('mumbai', 'Sports'): 14,\n",
              " ('peak', 'Sports'): 2,\n",
              " ('hour', 'Sports'): 17,\n",
              " ('traffic', 'Sports'): 4,\n",
              " ('bad', 'Sports'): 6,\n",
              " ('delhi', 'Sports'): 3,\n",
              " ('pit', 'Sports'): 2,\n",
              " ('road', 'Sports'): 7,\n",
              " ('2/12', 'Sports'): 1,\n",
              " ('hr', 'Sports'): 2,\n",
              " ('okhla', 'Sports'): 1,\n",
              " ('janakpuri', 'Sports'): 1,\n",
              " ('congrats', 'Politics'): 4,\n",
              " ('4africa', 'Politics'): 1,\n",
              " ('winner', 'Politics'): 13,\n",
              " ('challenge', 'Politics'): 20,\n",
              " ('developing', 'Politics'): 4,\n",
              " ('solution', 'Politics'): 14,\n",
              " ('youth', 'Politics'): 16,\n",
              " ('unemployment', 'Politics'): 7,\n",
              " ('thats', 'Sports'): 28,\n",
              " ('asad', 'Sports'): 2,\n",
              " ('shafiq', 'Sports'): 1,\n",
              " ('malik', 'Sports'): 2,\n",
              " ('nfl', 'Sports'): 8,\n",
              " ('combine', 'Sports'): 4,\n",
              " ('review', 'Sports'): 6,\n",
              " ('youll', 'Sports'): 8,\n",
              " ('find', 'Sports'): 23,\n",
              " ('youtube', 'Sports'): 3,\n",
              " ('link', 'Sports'): 9,\n",
              " ('giving', 'Sports'): 14,\n",
              " ('discus', 'Sports'): 6,\n",
              " ('_environment', 'Politics'): 9,\n",
              " ('diagnostic', 'Politics'): 1,\n",
              " ('medical', 'Politics'): 5,\n",
              " ('device', 'Politics'): 3,\n",
              " ('ethic', 'Politics'): 1,\n",
              " ('dna', 'Politics'): 2,\n",
              " ('testing', 'Politics'): 2,\n",
              " ('start', 'Politics'): 34,\n",
              " ('1230pm', 'Politics'): 2,\n",
              " ('cet', 'Politics'): 45,\n",
              " ('join', 'Politics'): 44,\n",
              " ('empowering', 'Politics'): 5,\n",
              " ('lgbt', 'Politics'): 16,\n",
              " ('combating', 'Politics'): 6,\n",
              " ('homophobia', 'Politics'): 4,\n",
              " ('sport', 'Politics'): 11,\n",
              " ('qampa', 'Politics'): 9,\n",
              " ('feb21', 'Politics'): 5,\n",
              " ('09est/14utc', 'Politics'): 6,\n",
              " ('valentine', 'Politics'): 9,\n",
              " ('fell', 'Politics'): 2,\n",
              " ('love', 'Politics'): 21,\n",
              " ('abroad', 'Politics'): 8,\n",
              " ('youreurope', 'Politics'): 1,\n",
              " ('offer', 'Politics'): 9,\n",
              " ('info', 'Politics'): 8,\n",
              " ('marriage', 'Politics'): 2,\n",
              " ('registered', 'Politics'): 2,\n",
              " ('partnership', 'Politics'): 7,\n",
              " ('eu', 'Politics'): 68,\n",
              " ('eyc2013', 'Politics'): 1,\n",
              " ('@2fiddyracer', 'Sports'): 1,\n",
              " ('racing', 'Sports'): 5,\n",
              " ('terror', 'Politics'): 8,\n",
              " ('error', 'Politics'): 3,\n",
              " ('multiple', 'Politics'): 1,\n",
              " ('political', 'Politics'): 14,\n",
              " ('bickering', 'Politics'): 1,\n",
              " ('apathetic', 'Politics'): 2,\n",
              " ('response', 'Politics'): 5,\n",
              " ('terrorism', 'Politics'): 9,\n",
              " ('we', 'Politics'): 36,\n",
              " ('people', 'Politics'): 112,\n",
              " ('8', 'Politics'): 25,\n",
              " ('freedomampour', 'Politics'): 1,\n",
              " ('right', 'Politics'): 53,\n",
              " ('gain', 'Politics'): 3,\n",
              " ('full', 'Politics'): 19,\n",
              " ('meaning', 'Politics'): 3,\n",
              " ('succeed', 'Politics'): 3,\n",
              " ('overcoming', 'Politics'): 2,\n",
              " ('divisionsampinequalities', 'Politics'): 1,\n",
              " ('our', 'Politics'): 23,\n",
              " ('past', 'Politics'): 11,\n",
              " ('nm', 'Politics'): 12,\n",
              " ('dbt', 'Politics'): 2,\n",
              " ('process', 'Politics'): 9,\n",
              " ('roll', 'Politics'): 7,\n",
              " ('pilot', 'Politics'): 2,\n",
              " ('important', 'Politics'): 19,\n",
              " ('learn', 'Politics'): 24,\n",
              " ('telephone', 'Politics'): 11,\n",
              " ('conversation', 'Politics'): 22,\n",
              " ('ukraine', 'Politics'): 3,\n",
              " ('viktor', 'Politics'): 3,\n",
              " ('yanukovych', 'Politics'): 2,\n",
              " ('23', 'Politics'): 6,\n",
              " ('v', 'Politics'): 12,\n",
              " ('much', 'Politics'): 23,\n",
              " ('murray', 'Sports'): 26,\n",
              " ('one\\\\s', 'Sports'): 1,\n",
              " ('slam', 'Sports'): 10,\n",
              " ('immediate', 'Sports'): 3,\n",
              " ('winning', 'Sports'): 22,\n",
              " ('easiest', 'Sports'): 1,\n",
              " ('got', 'Sports'): 40,\n",
              " ('extremely', 'Sports'): 3,\n",
              " ('close', 'Sports'): 13,\n",
              " ('approach', 'Politics'): 10,\n",
              " ('turning', 'Politics'): 1,\n",
              " ('back', 'Politics'): 44,\n",
              " ('europe', 'Politics'): 28,\n",
              " ('about', 'Politics'): 7,\n",
              " ('making', 'Politics'): 21,\n",
              " ('competitive', 'Politics'): 6,\n",
              " ('open', 'Politics'): 24,\n",
              " ('flexible', 'Politics'): 4,\n",
              " ('amp', 'Politics'): 49,\n",
              " ('securing', 'Politics'): 4,\n",
              " ('uk\\\\u2019s', 'Politics'): 2,\n",
              " ('place', 'Politics'): 27,\n",
              " ('within', 'Politics'): 8,\n",
              " ('wef', 'Politics'): 40,\n",
              " ('56', 'Sports'): 11,\n",
              " ('h', 'Sports'): 18,\n",
              " ('singh', 'Sports'): 14,\n",
              " ('e', 'Sports'): 33,\n",
              " ('4', 'Sports'): 109,\n",
              " ('11/0', 'Sports'): 4,\n",
              " ('hm', 'Politics'): 18,\n",
              " ('india', 'Politics'): 90,\n",
              " ('assented', 'Politics'): 1,\n",
              " ('unlawful', 'Politics'): 2,\n",
              " ('activity', 'Politics'): 8,\n",
              " ('prevention', 'Politics'): 6,\n",
              " ('amendt', 'Politics'): 1,\n",
              " ('2012', 'Politics'): 51,\n",
              " ('3113', 'Politics'): 1,\n",
              " ('\\\\u2026', 'Politics'): 8,\n",
              " ('we\\\\u2019ve', 'Politics'): 1,\n",
              " ('reached', 'Politics'): 3,\n",
              " ('bangladesh', 'Politics'): 23,\n",
              " ('border', 'Politics'): 13,\n",
              " ('bcim2013', 'Politics'): 43,\n",
              " ('onehanded', 'Sports'): 1,\n",
              " ('stunner', 'Sports'): 1,\n",
              " ('stahelkar', 'Sports'): 1,\n",
              " ('seal', 'Sports'): 1,\n",
              " ('world', 'Sports'): 64,\n",
              " ('10', 'Sports'): 98,\n",
              " ('wwc2013', 'Sports'): 6,\n",
              " ('imphal', 'Politics'): 10,\n",
              " ('icymi', 'Politics'): 2,\n",
              " ('amb', 'Politics'): 2,\n",
              " ('barton', 'Politics'): 1,\n",
              " ('cspan', 'Politics'): 1,\n",
              " ('yesterday', 'Politics'): 7,\n",
              " ('discussed', 'Politics'): 10,\n",
              " ('major', 'Politics'): 20,\n",
              " ('engagement', 'Politics'): 4,\n",
              " ('vpts', 'Politics'): 2,\n",
              " ('dusty', 'Politics'): 1,\n",
              " ('cargo', 'Politics'): 2,\n",
              " ('handling', 'Politics'): 3,\n",
              " ('getting', 'Politics'): 10,\n",
              " ('increasingly', 'Politics'): 2,\n",
              " ('mechanized', 'Politics'): 1,\n",
              " ('eliminate', 'Politics'): 4,\n",
              " ('pollution', 'Politics'): 3,\n",
              " ('sachin', 'Sports'): 20,\n",
              " ('superb', 'Sports'): 5,\n",
              " ('today', 'Sports'): 81,\n",
              " ('foot', 'Sports'): 8,\n",
              " ('moving', 'Sports'): 3,\n",
              " ('stroke', 'Sports'): 3,\n",
              " ('immaculate', 'Sports'): 1,\n",
              " ('delectably', 'Sports'): 1,\n",
              " ('executed', 'Sports'): 1,\n",
              " ('guy', 'Sports'): 16,\n",
              " ('think', 'Sports'): 50,\n",
              " ('i\\\\u2019m', 'Politics'): 7,\n",
              " ('also', 'Politics'): 26,\n",
              " ('issuing', 'Politics'): 1,\n",
              " ('new', 'Politics'): 153,\n",
              " ('goal', 'Politics'): 13,\n",
              " ('america', 'Politics'): 70,\n",
              " ('let\\\\u2019s', 'Politics'): 4,\n",
              " ('half', 'Politics'): 4,\n",
              " ('energy', 'Politics'): 28,\n",
              " ('wasted', 'Politics'): 3,\n",
              " ('home', 'Politics'): 19,\n",
              " ('led', 'Politics'): 4,\n",
              " ('pres', 'Politics'): 24,\n",
              " ('lincoln', 'Politics'): 20,\n",
              " ('sign', 'Politics'): 23,\n",
              " ('emancipation', 'Politics'): 8,\n",
              " ('proclamation', 'Politics'): 8,\n",
              " ('end', 'Politics'): 25,\n",
              " ('slavery', 'Politics'): 6,\n",
              " ('haroldholzer', 'Politics'): 24,\n",
              " ('candidate', 'Politics'): 12,\n",
              " ('obamas', 'Politics'): 37,\n",
              " ('slogan', 'Politics'): 5,\n",
              " ('2008', 'Politics'): 4,\n",
              " ('\\\\u2018yes', 'Politics'): 1,\n",
              " ('can\\\\u2019', 'Politics'): 1,\n",
              " ('\\\\u2018no', 'Politics'): 1,\n",
              " ('can\\\\u2019t\\\\u2019', 'Politics'): 1,\n",
              " ('ff', 'Politics'): 10,\n",
              " ('_gorka', 'Politics'): 1,\n",
              " ('company', 'Politics'): 24,\n",
              " ('woman', 'Politics'): 51,\n",
              " ('rpf', 'Politics'): 2,\n",
              " ('personnel', 'Politics'): 3,\n",
              " ('set', 'Politics'): 17,\n",
              " ('upanother', 'Politics'): 1,\n",
              " ('strengthen', 'Politics'): 8,\n",
              " ('especially', 'Politics'): 4,\n",
              " ('passenger', 'Politics'): 9,\n",
              " ('joe', 'Politics'): 8,\n",
              " ('biden', 'Politics'): 21,\n",
              " ('oath', 'Politics'): 7,\n",
              " ('office', 'Politics'): 22,\n",
              " ('vice', 'Politics'): 19,\n",
              " ('presidency', 'Politics'): 7,\n",
              " ('the', 'Politics'): 148,\n",
              " ('u', 'Politics'): 131,\n",
              " ('walk', 'Politics'): 4,\n",
              " ('brief', 'Politics'): 6,\n",
              " ('comment', 'Politics'): 8,\n",
              " ('po03su', 'Politics'): 1,\n",
              " ('po04su', 'Politics'): 1,\n",
              " ('po02su', 'Politics'): 1,\n",
              " ('natural', 'Politics'): 7,\n",
              " ('population', 'Politics'): 6,\n",
              " ('growth', 'Politics'): 34,\n",
              " ('recorded', 'Politics'): 1,\n",
              " ('five', 'Politics'): 8,\n",
              " ('month', 'Politics'): 15,\n",
              " ('russia', 'Politics'): 37,\n",
              " ('modern', 'Politics'): 10,\n",
              " ('history', 'Politics'): 21,\n",
              " ('nov', 'Politics'): 10,\n",
              " ('high', 'Politics'): 14,\n",
              " ('they', 'Politics'): 3,\n",
              " ('dont', 'Politics'): 22,\n",
              " ('deal', 'Politics'): 20,\n",
              " ('euco', 'Politics'): 21,\n",
              " ('wife', 'Politics'): 4,\n",
              " ('christmas', 'Politics'): 9,\n",
              " ('service', 'Politics'): 45,\n",
              " ('christ', 'Politics'): 1,\n",
              " ('saviour', 'Politics'): 1,\n",
              " ('cathedral', 'Politics'): 1,\n",
              " ('moscow', 'Politics'): 7,\n",
              " ('prime', 'Politics'): 115,\n",
              " ('minister', 'Politics'): 141,\n",
              " ('head', 'Politics'): 24,\n",
              " ('region', 'Politics'): 17,\n",
              " ('bear', 'Politics'): 6,\n",
              " ('personal', 'Politics'): 7,\n",
              " ('responsibility', 'Politics'): 11,\n",
              " ('raising', 'Politics'): 6,\n",
              " ('teacher', 'Politics'): 10,\n",
              " ('salary', 'Politics'): 5,\n",
              " ('working', 'Politics'): 31,\n",
              " ('breakfast', 'Politics'): 3,\n",
              " ('investor', 'Politics'): 5,\n",
              " ('davos', 'Politics'): 41,\n",
              " ('king', 'Sports'): 5,\n",
              " ('stats', 'Sports'): 13,\n",
              " ('discussion', 'Sports'): 6,\n",
              " ('look', 'Sports'): 68,\n",
              " ('nelson', 'Sports'): 1,\n",
              " ('in', 'Sports'): 91,\n",
              " ('new', 'Sports'): 83,\n",
              " ('light', 'Sports'): 4,\n",
              " ('celebrates', 'Politics'): 4,\n",
              " ('64th', 'Politics'): 2,\n",
              " ('hilarious', 'Sports'): 1,\n",
              " ('make', 'Sports'): 60,\n",
              " ('call', 'Sports'): 12,\n",
              " ('treat', 'Sports'): 5,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from collections import Counter\n",
        "def word_count(data):\n",
        "    freqs = {}\n",
        "    for i in range(data.shape[0]) :\n",
        "        label = data['Label'][i]\n",
        "        for word in  data['TweetText'][i]:\n",
        "            pair = ((word,label))\n",
        "            if pair in freqs :\n",
        "                freqs[pair] += 1\n",
        "            else :\n",
        "                freqs[pair] = 1\n",
        "    return freqs\n",
        "\n",
        "word_count(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "u0f-3IqVvcRQ"
      },
      "outputs": [],
      "source": [
        "df=pd.DataFrame.from_dict(word_count(train), orient='index', columns=['count'])\n",
        "#create the two columns word , label\n",
        "df.reset_index(inplace=True)\n",
        "df[['word','label']] = pd.DataFrame(df['index'].tolist(), index=df.index)\n",
        "df.drop(columns=['index'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "GvZTHEyGvcRQ"
      },
      "outputs": [],
      "source": [
        "#sort by count\n",
        "df.sort_values(by=['count'], ascending=False, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PuZ7OLVdvcRR",
        "outputId": "d33ea036-bc13-41f6-cd1c-78dc9edfebb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   count       word     label\n",
              "0    348  president  Politics\n",
              "1    347    indvaus    Sports\n",
              "2    331       test    Sports\n",
              "3    259        run    Sports\n",
              "4    253         to  Politics"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d1651eb-22cc-4541-ab54-0508a72efc27\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>word</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>348</td>\n",
              "      <td>president</td>\n",
              "      <td>Politics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>347</td>\n",
              "      <td>indvaus</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>331</td>\n",
              "      <td>test</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>259</td>\n",
              "      <td>run</td>\n",
              "      <td>Sports</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>253</td>\n",
              "      <td>to</td>\n",
              "      <td>Politics</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d1651eb-22cc-4541-ab54-0508a72efc27')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4d1651eb-22cc-4541-ab54-0508a72efc27 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-4d1651eb-22cc-4541-ab54-0508a72efc27');\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",
              "\n",
              "\n",
              "<div id=\"df-bdc82556-b073-4346-bee9-2e6c02299f94\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bdc82556-b073-4346-bee9-2e6c02299f94')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bdc82556-b073-4346-bee9-2e6c02299f94 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "#reindex\n",
        "df.reset_index(inplace=True)\n",
        "df.drop(columns=['index'], inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "V5lD75kyvcRR",
        "outputId": "c4cce77a-a66c-4bd9-c44a-2bd2de868278"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   count       word     label   weights\n",
              "0    348  president  Politics  0.010492\n",
              "1    347    indvaus    Sports  0.010096\n",
              "2    331       test    Sports  0.009630\n",
              "3    259        run    Sports  0.007536\n",
              "4    253         to  Politics  0.007628"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2e815b1-2925-47db-bb09-02750f99f392\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>word</th>\n",
              "      <th>label</th>\n",
              "      <th>weights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>348</td>\n",
              "      <td>president</td>\n",
              "      <td>Politics</td>\n",
              "      <td>0.010492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>347</td>\n",
              "      <td>indvaus</td>\n",
              "      <td>Sports</td>\n",
              "      <td>0.010096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>331</td>\n",
              "      <td>test</td>\n",
              "      <td>Sports</td>\n",
              "      <td>0.009630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>259</td>\n",
              "      <td>run</td>\n",
              "      <td>Sports</td>\n",
              "      <td>0.007536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>253</td>\n",
              "      <td>to</td>\n",
              "      <td>Politics</td>\n",
              "      <td>0.007628</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2e815b1-2925-47db-bb09-02750f99f392')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c2e815b1-2925-47db-bb09-02750f99f392 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-c2e815b1-2925-47db-bb09-02750f99f392');\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",
              "\n",
              "\n",
              "<div id=\"df-9bedaa4c-30f9-4095-a05c-0ea1fe2cccd3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9bedaa4c-30f9-4095-a05c-0ea1fe2cccd3')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9bedaa4c-30f9-4095-a05c-0ea1fe2cccd3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "#create the weight for a word in the whole of the dataset grouped by label\n",
        "df['weights']=df['count']/df.groupby('label')['count'].transform('sum')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "id": "qSyMITlkvcRR",
        "outputId": "76dd1ea3-64d0-4565-eda4-0a12e55b2ba5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plot of weith of each word\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('whitegrid')\n",
        "plt.figure(figsize=(15, 8))\n",
        "sns.barplot(x='word', y='weights', hue='label', data=df.head(20))\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgWUXVS-dx1q"
      },
      "source": [
        "# Text Vectorization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKL3GVGldx1q"
      },
      "source": [
        "## TF-IDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "DBP4pe47vcRV"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "3t_Q2wbfdx1q"
      },
      "outputs": [],
      "source": [
        "# Convert list elements to strings and then apply TF-IDF vectorizer\n",
        "tfidfconverter = TfidfVectorizer(max_features=1000, min_df=5, max_df=0.7)\n",
        "X = tfidfconverter.fit_transform(train['TweetText'].apply(' '.join))\n",
        "y = train['Label']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4xqGCqyvcRW"
      },
      "source": [
        "## first model : Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "AVrk6ptI-aoG",
        "outputId": "2eb86f1b-35c2-475a-d3ba-45d77d3a6555"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Initialize the classifier\n",
        "classifier = LogisticRegression()\n",
        "\n",
        "# Train the classifier\n",
        "classifier.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXcSgNEF-lJe",
        "outputId": "604b033e-c0cf-467c-d5e6-af6577e0159a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9419157088122605\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "y_train_pred = classifier.predict(X)\n",
        "# Calculate training accuracy\n",
        "training_accuracy = accuracy_score(y, y_train_pred)\n",
        "print(training_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YT5n38tWOqX_"
      },
      "source": [
        "## confusion matrix for Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "u4oied-POqX_",
        "outputId": "3b1e6fb3-54a7-4276-feba-bb9ba82921c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_train_pred = classifier.predict(X)\n",
        "confusion = confusion_matrix(y, y_train_pred)\n",
        "\n",
        "# plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(confusion, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcmrzbTuvcRW"
      },
      "source": [
        "## second model : XGBOOST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Wv-EpNOTvcRW"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAeiqzKU_RQP",
        "outputId": "1f6c82a9-d104-4afa-ec4f-13478622208b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Training Accuracy: 0.9276628352490421\n"
          ]
        }
      ],
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "# Initialize LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Fit and transform the Label column\n",
        "train['Label_Encoded'] = label_encoder.fit_transform(train['Label'])\n",
        "\n",
        "# Map Sports to 0 and Politics to 1\n",
        "train['Label_Encoded'] = train['Label_Encoded'].map({0: 0, 1: 1})\n",
        "y=train['Label_Encoded']\n",
        "# Train XGBoost classifier\n",
        "xgb_classifier = xgb.XGBClassifier()\n",
        "xgb_classifier.fit(X, y)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Predict on the training data\n",
        "y_train_pred_xgb = xgb_classifier.predict(X)\n",
        "\n",
        "# Calculate training accuracy\n",
        "training_accuracy_xgb = accuracy_score(y, y_train_pred_xgb)\n",
        "print(\"XGBoost Training Accuracy:\", training_accuracy_xgb)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqKAKSfKOqX_"
      },
      "source": [
        "## confusion matrix for XGBOOST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "oFk-HwfpOqX_",
        "outputId": "86c13d0d-8655-465d-e78d-8cdd200dcc7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_train_pred_xgb = xgb_classifier.predict(X)\n",
        "confusion_xgb = confusion_matrix(y, y_train_pred_xgb)\n",
        "\n",
        "# plot the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(confusion_xgb, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title(\"XGBoost Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    },
    "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 0
}