Hatespeech_detection_classifier.ipynb

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        "<a href=\"https://colab.research.google.com/github/devroopsaha744/HateSpeechDetect-text/blob/main/Hatespeech_detection_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "!pip install datasets -q\n",
        "!pip install nltk -q\n",
        "!pip install gensim -q\n",
        "!pip install imbalanced-learn -q"
      ]
    },
    {
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      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 3,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
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              "True"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gensim.models import Word2Vec\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline"
      ],
      "metadata": {
        "id": "xMzL9nUmk3q0"
      },
      "execution_count": 19,
      "outputs": []
    },
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        "from datasets import load_dataset\n",
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              "Downloading readme:   0%|          | 0.00/5.92k [00:00<?, ?B/s]"
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        "data_split = data['train'].train_test_split(test_size = 0.3)"
      ],
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        "id": "A_guUQl0klV1"
      },
      "execution_count": 6,
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    },
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        "train = data_split['train']\n",
        "test = data_split['test']"
      ],
      "metadata": {
        "id": "5G9hRSWzk756"
      },
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        "train"
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      },
      "execution_count": 8,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['count', 'hate_speech_count', 'offensive_language_count', 'neither_count', 'class', 'tweet'],\n",
              "    num_rows: 17348\n",
              "})"
            ]
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        "test"
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              "Dataset({\n",
              "    features: ['count', 'hate_speech_count', 'offensive_language_count', 'neither_count', 'class', 'tweet'],\n",
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              "})"
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        "train_df =  train.to_pandas()\n",
        "test_df = test.to_pandas()"
      ],
      "metadata": {
        "id": "WGn1auVSmgfn"
      },
      "execution_count": 10,
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count  hate_speech_count  ...  class                                              tweet\n",
              "0          6                  1  ...      2  RT @sorryimalex: \"now 5sos is starting to twee...\n",
              "1          3                  0  ...      1  RT @nandotrash: Tell a girl to \"calm down\" she...\n",
              "2          3                  0  ...      1  RT @thecoreyholcomb: Twitter family, I promise...\n",
              "3          6                  0  ...      2  @Notsosweetpea SOO when we hanging out and mak...\n",
              "4          3                  0  ...      1  So, #brony-folk far and wide are throwing bitc...\n",
              "...      ...                ...  ...    ...                                                ...\n",
              "17343      3                  0  ...      1  Nobody wit me im on all alone shit so dont thi...\n",
              "17344      3                  1  ...      1  @GYPMATIC45 i pissed alotta ppl off on fb i wa...\n",
              "17345      3                  0  ...      1                                 Miss ma niggah leo\n",
              "17346      3                  0  ...      1  If eating the pussy makes you happy, I'm all f...\n",
              "17347      3                  0  ...      1  This bitch is really geeking in my messages &#...\n",
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              "[17348 rows x 6 columns]"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 17348,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          7,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hate_speech_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0,\n          6,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"offensive_language_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          3,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neither_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17348,\n        \"samples\": [\n          \"Switch A Roo yo hoe let me hit her\",\n          \"RT @gmaslsk: Oh BO-stop with the speech-like you give one tinkers damn about the kids,the poor,any of the crap you are spouting-you've prov&#8230;\",\n          \"\\\" bitch get up off me \\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4R97vaaCDfbI",
        "outputId": "28dac291-deee-488f-c19f-2f42b21556a6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "1    13441\n",
              "2     2888\n",
              "0     1019\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "79vFGvpenIB5",
        "outputId": "d7632064-efa5-47b2-d3a8-c5f0f668a3ce"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      count  hate_speech_count  offensive_language_count  neither_count  \\\n",
              "0         3                  0                         3              0   \n",
              "1         3                  0                         3              0   \n",
              "2         3                  0                         3              0   \n",
              "3         3                  1                         2              0   \n",
              "4         3                  0                         3              0   \n",
              "...     ...                ...                       ...            ...   \n",
              "7430      3                  0                         0              3   \n",
              "7431      3                  0                         3              0   \n",
              "7432      3                  0                         3              0   \n",
              "7433      3                  1                         2              0   \n",
              "7434      3                  0                         2              1   \n",
              "\n",
              "      class                                              tweet  \n",
              "0         1  @Bitcheslovestar The only one of Mary's friend...  \n",
              "1         1                           No skimp shit here bitch  \n",
              "2         1            these bitches singing this song tho -_-  \n",
              "3         1    @KodyMark1 hahahh thank you fag boy! #represent  \n",
              "4         1  Soon as da money gon dem bitches leavin..You g...  \n",
              "...     ...                                                ...  \n",
              "7430      2  @AustinBurge44 either way both schools are tra...  \n",
              "7431      1            @jballinbutts420 sameeee bitch asss lol  \n",
              "7432      1  @n0tsteven ily 2, I'm at work bitch do you eve...  \n",
              "7433      1  Everyone that says #LetsMakeItAwkward is a que...  \n",
              "7434      1  @piersmorgan @JeremyClarkson You two need to s...  \n",
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              "      <td>@KodyMark1 hahahh thank you fag boy! #represent</td>\n",
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              "      <td>@AustinBurge44 either way both schools are tra...</td>\n",
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              "      <th>7431</th>\n",
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              "      <td>@jballinbutts420 sameeee bitch asss lol</td>\n",
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              "      <td>@n0tsteven ily 2, I'm at work bitch do you eve...</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Everyone that says #LetsMakeItAwkward is a que...</td>\n",
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              "      <td>1</td>\n",
              "      <td>@piersmorgan @JeremyClarkson You two need to s...</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df",
              "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 7435,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6,\n          9,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hate_speech_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"offensive_language_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neither_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7435,\n        \"samples\": [\n          \"#AlexFromTarget had them bitches wet...lmmfao\",\n          \"&#8220;@JayStacks25: I might fuck around and fuck yo bitch &#128520;&#128175;&#128527;&#8221; eat her ass when your steal her so you'll be a real ass nigga\",\n          \"@Barbod6FCB. U lame then a bitch get the fuck with that gay shit\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the stop words\n",
        "stop_words = set(stopwords.words('english'))\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def preprocess_text(text):\n",
        "    # Lowercase the text\n",
        "    text = text.lower()\n",
        "    # Remove special characters and punctuation\n",
        "    text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
        "    # Tokenize the text\n",
        "    tokens = word_tokenize(text)\n",
        "    # Remove stop words and lemmatize the tokens\n",
        "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n",
        "    # Join the tokens back into a single string\n",
        "    text = ' '.join(tokens)\n",
        "    return text\n",
        "\n",
        "# Apply the preprocessing function to the 'text' column\n",
        "train_df['tweet'] = train_df['tweet'].apply(preprocess_text)"
      ],
      "metadata": {
        "id": "QhWJpiCNq0oX"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df['tweet'] = test_df['tweet'].apply(preprocess_text)"
      ],
      "metadata": {
        "id": "soeJXeFZ6vCT"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QXCNZvXS5zPf",
        "outputId": "dfaca421-3812-4155-b5d4-08b7f2db4df8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count  hate_speech_count  ...  class                                              tweet\n",
              "0          6                  1  ...      2  rt sorryimalex 5sos starting tweet hashtag the...\n",
              "1          3                  0  ...      1  rt nandotrash tell girl calm shell act like ki...\n",
              "2          3                  0  ...      1  rt thecoreyholcomb twitter family promise put ...\n",
              "3          6                  0  ...      2            notsosweetpea soo hanging makin brownie\n",
              "4          3                  0  ...      1  bronyfolk far wide throwing bitch fit equestri...\n",
              "...      ...                ...  ...    ...                                                ...\n",
              "17343      3                  0  ...      1  nobody wit im alone shit dont think im actin f...\n",
              "17344      3                  1  ...      1  gypmatic45 pissed alotta ppl fb flooding newsf...\n",
              "17345      3                  0  ...      1                                    miss niggah leo\n",
              "17346      3                  0  ...      1                         eating pussy make happy im\n",
              "17347      3                  0  ...      1    bitch really geeking message 128514128514128514\n",
              "\n",
              "[17348 rows x 6 columns]"
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              "      <td>bitch really geeking message 128514128514128514</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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              "      .colab-df-generate {\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 17348,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          7,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hate_speech_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0,\n          6,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"offensive_language_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          3,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neither_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17295,\n        \"samples\": [\n          \"phone finally back bitch textnow\",\n          \"rt hoe rt youd marry scarlett johansson httptcokmojddil7h\",\n          \"daishialopez could say thing woman two functioning chest table hold peanut butter cracker\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['tokens'] = train_df['tweet'].apply(word_tokenize)\n",
        "\n",
        "# Train Word2Vec model\n",
        "model = Word2Vec(sentences=train_df['tokens'], vector_size=100, window=5, min_count=1, workers=4)\n",
        "\n",
        "# Save the model for future use\n",
        "model.save(\"word2vec.model\")\n",
        "\n",
        "# Load the model\n",
        "# model = Word2Vec.load(\"word2vec.model\")\n"
      ],
      "metadata": {
        "id": "IqN5Xy3F549M"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df['tokens'] = test_df['tweet'].apply(word_tokenize)"
      ],
      "metadata": {
        "id": "J0fPiPYY8GQQ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_average_word2vec(tokens, model, vector_size):\n",
        "    # Initialize an empty vector\n",
        "    vec = np.zeros(vector_size)\n",
        "    count = 0\n",
        "    for word in tokens:\n",
        "        if word in model.wv:\n",
        "            vec += model.wv[word]\n",
        "            count += 1\n",
        "    if count != 0:\n",
        "        vec /= count\n",
        "    return vec\n",
        "\n",
        "vector_size = model.vector_size\n",
        "\n",
        "train_df['text_vec'] = train_df['tokens'].apply(lambda x: get_average_word2vec(x, model, vector_size))\n",
        "test_df['text_vec'] = test_df['tokens'].apply(lambda x: get_average_word2vec(x, model, vector_size))\n",
        "\n",
        "# Convert lists of vectors to a 2D array for the classifier\n",
        "X_train = np.stack(train_df['text_vec'].values)\n",
        "y_train = train_df['class'].values\n",
        "X_test = np.stack(test_df['text_vec'].values)\n",
        "y_test = test_df['class'].values\n"
      ],
      "metadata": {
        "id": "2P7O8pVd6aqs"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Applying data-resampling\n",
        "# Define the resampling pipeline\n",
        "over = SMOTE(sampling_strategy= 'auto')  # Oversample the minority class to 10% of the majority class\n",
        "under = RandomUnderSampler(sampling_strategy='auto')  # Undersample the majority class to 50% of the original\n",
        "\n",
        "# Combine the oversampling and undersampling in a pipeline\n",
        "steps = [('o', over), ('u', under)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "\n",
        "# Apply resampling to the training data\n",
        "X_train_res, y_train_res = pipeline.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "-g44DLqmFnD1"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#On original Data\n",
        "clf1 = SVC()\n",
        "clf2 = LogisticRegression()\n",
        "clf3 = DecisionTreeClassifier()\n",
        "clf4 = RandomForestClassifier()\n",
        "clf5 = GradientBoostingClassifier()\n",
        "clf6 = XGBClassifier()\n",
        "clf7 = AdaBoostClassifier()\n",
        "\n",
        "classifier = [clf1, clf2, clf3, clf4, clf5, clf6, clf7]\n",
        "for clf in classifier:\n",
        "  clf.fit(X_train, y_train)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  print(f\"The Classification report of {clf} is: \\n {classification_report(y_test, y_pred)}\")\n",
        "  print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7UbmYnEz7-8C",
        "outputId": "d33b6de7-e000-4f7a-e6c4-295e245ee2ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Classification report of SVC() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       438\n",
            "           1       0.78      0.99      0.87      5681\n",
            "           2       0.63      0.12      0.20      1316\n",
            "\n",
            "    accuracy                           0.78      7435\n",
            "   macro avg       0.47      0.37      0.36      7435\n",
            "weighted avg       0.71      0.78      0.70      7435\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Classification report of LogisticRegression() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       438\n",
            "           1       0.84      0.97      0.90      5681\n",
            "           2       0.71      0.49      0.58      1316\n",
            "\n",
            "    accuracy                           0.83      7435\n",
            "   macro avg       0.52      0.49      0.49      7435\n",
            "weighted avg       0.77      0.83      0.79      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of DecisionTreeClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.12      0.13      0.12       438\n",
            "           1       0.87      0.88      0.87      5681\n",
            "           2       0.58      0.53      0.55      1316\n",
            "\n",
            "    accuracy                           0.77      7435\n",
            "   macro avg       0.52      0.51      0.52      7435\n",
            "weighted avg       0.77      0.77      0.77      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of RandomForestClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.03      0.06       438\n",
            "           1       0.86      0.96      0.91      5681\n",
            "           2       0.69      0.58      0.63      1316\n",
            "\n",
            "    accuracy                           0.84      7435\n",
            "   macro avg       0.70      0.52      0.53      7435\n",
            "weighted avg       0.81      0.84      0.81      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of GradientBoostingClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.46      0.03      0.06       438\n",
            "           1       0.86      0.95      0.90      5681\n",
            "           2       0.67      0.58      0.62      1316\n",
            "\n",
            "    accuracy                           0.83      7435\n",
            "   macro avg       0.67      0.52      0.53      7435\n",
            "weighted avg       0.80      0.83      0.80      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
            "              num_parallel_tree=None, objective='multi:softprob', ...) is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.46      0.07      0.13       438\n",
            "           1       0.87      0.95      0.91      5681\n",
            "           2       0.69      0.63      0.66      1316\n",
            "\n",
            "    accuracy                           0.84      7435\n",
            "   macro avg       0.68      0.55      0.56      7435\n",
            "weighted avg       0.82      0.84      0.82      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of AdaBoostClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       438\n",
            "           1       0.86      0.95      0.90      5681\n",
            "           2       0.64      0.55      0.59      1316\n",
            "\n",
            "    accuracy                           0.82      7435\n",
            "   macro avg       0.50      0.50      0.50      7435\n",
            "weighted avg       0.77      0.82      0.79      7435\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#On resampled Data\n",
        "clf1 = SVC()\n",
        "clf2 = LogisticRegression()\n",
        "clf3 = DecisionTreeClassifier()\n",
        "clf4 = RandomForestClassifier()\n",
        "clf5 = GradientBoostingClassifier()\n",
        "clf6 = XGBClassifier()\n",
        "clf7 = AdaBoostClassifier()\n",
        "\n",
        "classifier = [clf1, clf2, clf3, clf4, clf5, clf6, clf7]\n",
        "for clf in classifier:\n",
        "  clf.fit(X_train_res, y_train_res)\n",
        "  y_pred = clf.predict(X_test)\n",
        "  print(f\"The Classification report of {clf} is: \\n {classification_report(y_test, y_pred)}\")\n",
        "  print(\"\\n\")"
      ],
      "metadata": {
        "id": "WvfgqqSA8O-C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00e1ceb6-8968-4353-c1e1-10c6d267f788"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Classification report of SVC() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.12      0.26      0.17       411\n",
            "           1       0.94      0.79      0.86      5749\n",
            "           2       0.55      0.73      0.63      1275\n",
            "\n",
            "    accuracy                           0.75      7435\n",
            "   macro avg       0.54      0.60      0.55      7435\n",
            "weighted avg       0.83      0.75      0.78      7435\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Classification report of LogisticRegression() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.15      0.48      0.23       411\n",
            "           1       0.97      0.76      0.85      5749\n",
            "           2       0.62      0.78      0.69      1275\n",
            "\n",
            "    accuracy                           0.75      7435\n",
            "   macro avg       0.58      0.67      0.59      7435\n",
            "weighted avg       0.86      0.75      0.79      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of DecisionTreeClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.10      0.25      0.14       411\n",
            "           1       0.89      0.79      0.84      5749\n",
            "           2       0.56      0.55      0.56      1275\n",
            "\n",
            "    accuracy                           0.72      7435\n",
            "   macro avg       0.51      0.53      0.51      7435\n",
            "weighted avg       0.79      0.72      0.75      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of RandomForestClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.24      0.18      0.21       411\n",
            "           1       0.91      0.89      0.90      5749\n",
            "           2       0.63      0.72      0.67      1275\n",
            "\n",
            "    accuracy                           0.83      7435\n",
            "   macro avg       0.59      0.60      0.59      7435\n",
            "weighted avg       0.82      0.83      0.82      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of GradientBoostingClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.14      0.41      0.21       411\n",
            "           1       0.94      0.79      0.86      5749\n",
            "           2       0.62      0.70      0.66      1275\n",
            "\n",
            "    accuracy                           0.75      7435\n",
            "   macro avg       0.57      0.63      0.57      7435\n",
            "weighted avg       0.84      0.75      0.79      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
            "              num_parallel_tree=None, objective='multi:softprob', ...) is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.20      0.28      0.24       411\n",
            "           1       0.92      0.87      0.89      5749\n",
            "           2       0.64      0.71      0.68      1275\n",
            "\n",
            "    accuracy                           0.81      7435\n",
            "   macro avg       0.59      0.62      0.60      7435\n",
            "weighted avg       0.83      0.81      0.82      7435\n",
            "\n",
            "\n",
            "\n",
            "The Classification report of AdaBoostClassifier() is: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.10      0.34      0.15       411\n",
            "           1       0.94      0.74      0.83      5749\n",
            "           2       0.58      0.69      0.63      1275\n",
            "\n",
            "    accuracy                           0.71      7435\n",
            "   macro avg       0.54      0.59      0.54      7435\n",
            "weighted avg       0.83      0.71      0.76      7435\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "awtsdreCG_uA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}