hate_speech_detection.ipynb

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    "colab": {
      "name": "hate-speech-detection (2).ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXI7Fq0W4A3H"
      },
      "source": [
        "# Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "ZV71_2m54A3Q",
        "outputId": "a0f62110-48e5-4bc5-974e-0a806d0c4d58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "#to data preprocessing\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "#NLP tools\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "#train split and fit models\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import svm\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#model selection\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSYogx6s4A3j"
      },
      "source": [
        "# Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "trusted": true,
        "id": "-AR7Mega4A3m",
        "outputId": "63a52e29-cfce-4715-a646-69daf9c2fd66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dataset = pd.read_csv('/content/labeled_data.csv')\n",
        "dataset.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>count</th>\n",
              "      <th>hate_speech</th>\n",
              "      <th>offensive_language</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>3</td>\n",
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              "      <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n",
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              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0  count  ...  class                                              tweet\n",
              "0           0      3  ...      2  !!! RT @mayasolovely: As a woman you shouldn't...\n",
              "1           1      3  ...      1  !!!!! RT @mleew17: boy dats cold...tyga dwn ba...\n",
              "2           2      3  ...      1  !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...\n",
              "3           3      3  ...      1  !!!!!!!!! RT @C_G_Anderson: @viva_based she lo...\n",
              "4           4      6  ...      1  !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
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    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "vs8TgBgV4A36",
        "outputId": "f5caadc6-68e6-4b0c-c373-d7064a497ae5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 24783 entries, 0 to 24782\n",
            "Data columns (total 7 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   Unnamed: 0          24783 non-null  int64 \n",
            " 1   count               24783 non-null  int64 \n",
            " 2   hate_speech         24783 non-null  int64 \n",
            " 3   offensive_language  24783 non-null  int64 \n",
            " 4   neither             24783 non-null  int64 \n",
            " 5   class               24783 non-null  int64 \n",
            " 6   tweet               24783 non-null  object\n",
            "dtypes: int64(6), object(1)\n",
            "memory usage: 1.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nd1w1ZAC4A4I",
        "outputId": "69ce01ab-bc5e-4f44-c7e7-3a66e176e8dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "dataset.describe().T"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>mean</th>\n",
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              "      <th>min</th>\n",
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              "      <th>50%</th>\n",
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              "      <th>max</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>24783.0</td>\n",
              "      <td>12681.192027</td>\n",
              "      <td>7299.553863</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6372.5</td>\n",
              "      <td>12703.0</td>\n",
              "      <td>18995.5</td>\n",
              "      <td>25296.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>24783.0</td>\n",
              "      <td>3.243473</td>\n",
              "      <td>0.883060</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hate_speech</th>\n",
              "      <td>24783.0</td>\n",
              "      <td>0.280515</td>\n",
              "      <td>0.631851</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>offensive_language</th>\n",
              "      <td>24783.0</td>\n",
              "      <td>2.413711</td>\n",
              "      <td>1.399459</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neither</th>\n",
              "      <td>24783.0</td>\n",
              "      <td>0.549247</td>\n",
              "      <td>1.113299</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>24783.0</td>\n",
              "      <td>1.110277</td>\n",
              "      <td>0.462089</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
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            ],
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              "                      count          mean  ...      75%      max\n",
              "Unnamed: 0          24783.0  12681.192027  ...  18995.5  25296.0\n",
              "count               24783.0      3.243473  ...      3.0      9.0\n",
              "hate_speech         24783.0      0.280515  ...      0.0      7.0\n",
              "offensive_language  24783.0      2.413711  ...      3.0      9.0\n",
              "neither             24783.0      0.549247  ...      0.0      9.0\n",
              "class               24783.0      1.110277  ...      1.0      2.0\n",
              "\n",
              "[6 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AeI8Qvh24A4X"
      },
      "source": [
        "dt_trasformed = dataset[['class', 'tweet']]\n",
        "y = dt_trasformed.iloc[:, :-1].values"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7JKq33-4A4o"
      },
      "source": [
        "### Encoding the Dependent Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MYm001G94A4x"
      },
      "source": [
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')\n",
        "y = np.array(ct.fit_transform(y))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "JkcoeYy04A4_",
        "outputId": "7d4eb14b-e4d0-4a77-edc9-4cb8a0756145",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 1.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " ...\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tdgwj3eg4A5N"
      },
      "source": [
        "I separated this y in two variables that we will use to fit hate speech models and offensive speech models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "_pu0j1wV4A5R"
      },
      "source": [
        "y_df = pd.DataFrame(y)\n",
        "y_hate = np.array(y_df[0])\n",
        "y_offensive = np.array(y_df[1])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "0pvkOAoV4A5e",
        "outputId": "28b33016-e70b-489f-8ad3-240139a15da7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(y_hate)\n",
        "print(y_offensive)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. ... 0. 0. 0.]\n",
            "[0. 1. 1. ... 1. 1. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hp4dM1uc4A5y"
      },
      "source": [
        "## Cleaning the texts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "O3WTpbBk4A52"
      },
      "source": [
        "corpus = []\n",
        "for i in range(0, 24783):\n",
        "  review = re.sub('[^a-zA-Z]', ' ', dt_trasformed['tweet'][i])\n",
        "  review = review.lower()\n",
        "  review = review.split()\n",
        "  ps = PorterStemmer()\n",
        "  all_stopwords = stopwords.words('english')\n",
        "  all_stopwords.remove('not')\n",
        "  review = [ps.stem(word) for word in review if not word in set(all_stopwords)]\n",
        "  review = ' '.join(review)\n",
        "  corpus.append(review)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "LyjT0GeO4A6G"
      },
      "source": [
        "cv = CountVectorizer(max_features = 2000)\n",
        "X = cv.fit_transform(corpus).toarray()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeyGB6uf4A6b"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "w8X27tlK4A6d"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y_hate, test_size = 0.30, random_state = 0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrip21SO4A6p"
      },
      "source": [
        "## Finding the best models to predict hate speech"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8gCnW1T4A6r"
      },
      "source": [
        "**Naive Bayes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "shBGTnf74A6s",
        "outputId": "3971f7d8-2991-4e0b-9c9c-2879a9be6e52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classifier_np = GaussianNB()\n",
        "classifier_np.fit(X_train, y_train)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbG-KGQG4A62"
      },
      "source": [
        "**Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9dPrEDe-4A63",
        "outputId": "7598fb52-b8db-4d4c-aa54-d3716246cb2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "classifier_dt = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier_dt.fit(X_train, y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=0, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i98nGiDs4A6_"
      },
      "source": [
        "**KNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "w-5VGhnX4A7D",
        "outputId": "0a0a43bc-0e55-44e5-dfab-e6a3ac41e2a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "classifier_knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier_knn.fit(X_train, y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Kq4N8Yu4A7K"
      },
      "source": [
        "**Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "liJRh4mS4A7L",
        "outputId": "7dea5a1f-1772-41e9-ec0f-a52741572e9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "classifier_lr = LogisticRegression(random_state = 0)\n",
        "classifier_lr.fit(X_train, y_train)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUHhAjEF4A7Y"
      },
      "source": [
        "**Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "xwFI4fUF4A7a",
        "outputId": "993708c3-d1fa-4330-c040-2586a50cd370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "classifier_rf = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "classifier_rf.fit(X_train, y_train)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='entropy', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PhbW2mMW4A7f"
      },
      "source": [
        "SVM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "huXdBXwc4A7g",
        "outputId": "e4fe7672-52d8-4f6f-a211-fc39fee94d3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "classifier_svm = svm.SVC()\n",
        "classifier_svm.fit(X_train, y_train)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltEeQpWE4A7n"
      },
      "source": [
        "XGBoost Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "M9Z7zfyt4A7o",
        "outputId": "69521222-7b14-4706-8c29-c1da40178373",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "classifier_xgb= XGBClassifier()\n",
        "classifier_xgb.fit(X_train, y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLPQDu1Q4A7s"
      },
      "source": [
        "# Making the Confusion Matrix for each model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8su41ZE14A7s",
        "outputId": "ccd2d409-1332-4487-bc66-0c98e9d3903b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Naive Bayes\n",
        "y_pred_np = classifier_np.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred_np)\n",
        "print(cm)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3289 3719]\n",
            " [ 168  259]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "K1o8TzTN4A7v",
        "outputId": "94a621b6-5d95-446a-c2df-54329cb684eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#KNN\n",
        "y_pred_knn = classifier_knn.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred_knn)\n",
        "print(cm)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6894  114]\n",
            " [ 322  105]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Rg8TubG74A73",
        "outputId": "a164161c-8e7b-4ac4-e7d5-6af99be1464a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#XGBoost Classifier\n",
        "y_pred_xgb = classifier_xgb.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred_xgb)\n",
        "print(cm)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6986   22]\n",
            " [ 387   40]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "cx8OY7qB4A7-",
        "outputId": "87617a35-5507-45bd-8c5c-cb7f2a40fa94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#SVM\n",
        "y_pred_svm = classifier_svm.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred_svm)\n",
        "print(cm)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6964   44]\n",
            " [ 372   55]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "5Wkj0f3f4A8F",
        "outputId": "a66a3516-4c87-422c-e568-b05542097219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Logistic Regression\n",
        "y_pred_lr=classifier_lr.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred_lr)\n",
        "print(cm)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6911   97]\n",
            " [ 347   80]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "VPf9NsfL4A8M",
        "outputId": "bf7bf779-4b1e-4706-b820-abc075ec71ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Decision Tree\n",
        "y_pred_dt = classifier_dt.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred_dt)\n",
        "print(cm)\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6672  336]\n",
            " [ 288  139]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ON87poqh4A8R",
        "outputId": "48c8262c-9a56-43f1-f295-86531b015e55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Random Florest\n",
        "y_pred_rf = classifier_rf.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred_rf)\n",
        "print(cm)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6865  143]\n",
            " [ 330   97]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "R9Jy8JO44A8Y",
        "outputId": "b43c0213-6324-472f-e5c1-76fd952863ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "rf_score = accuracy_score(y_test, y_pred_rf)\n",
        "knn_score = accuracy_score(y_test, y_pred_knn)\n",
        "svm_score = accuracy_score(y_test, y_pred_svm)\n",
        "xgb_score = accuracy_score(y_test, y_pred_xgb)\n",
        "lr_score = accuracy_score(y_test, y_pred_lr)\n",
        "dt_score = accuracy_score(y_test, y_pred_dt)\n",
        "np_score = accuracy_score(y_test, y_pred_np)\n",
        "\n",
        "print('Random Forest Accuracy: ', str(rf_score))\n",
        "print('K Nearest Neighbours Accuracy: ', str(knn_score))\n",
        "print('Support Vector Machine Accuracy: ', str(svm_score))\n",
        "print('XGBoost Classifier Accuracy: ', str(xgb_score))\n",
        "print('Logistic Regression Accuracy: ',str(lr_score))\n",
        "print('Decision Tree Accuracy: ', str(dt_score))\n",
        "print('Naive Bayes Accuracy: ', str(np_score))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest Accuracy:  0.9363819771351715\n",
            "K Nearest Neighbours Accuracy:  0.9413584398117014\n",
            "Support Vector Machine Accuracy:  0.9440484196368527\n",
            "XGBoost Classifier Accuracy:  0.9449899125756557\n",
            "Logistic Regression Accuracy:  0.9402824478816408\n",
            "Decision Tree Accuracy:  0.9160726294552791\n",
            "Naive Bayes Accuracy:  0.4772024209818426\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81nDFw__4A8c"
      },
      "source": [
        "So XGBoost Classifier looks better to predict hate speech based on this dataset. It's important to emphasize Random Forest,KNN and Decision Tree had great results as well. This Dataset looks like a product of artificial intelligence to classify hate and offensive speech."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "cHQTiOGE4A8d",
        "outputId": "340127cf-35dc-44ef-dfb8-3bd5d810c2d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Now finally saving the model with XGB Classifier.\n",
        "import joblib\n",
        "joblib.dump(classifier_xgb, 'hatespeech.pkl')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hatespeech.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEMQqvMDCYoM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}