SMS_spam.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3tokFeDIrNXG"
      },
      "source": [
        "# **IMPORTING LIBRARIES**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8XBi4s8imnsp"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "import seaborn as sns\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline    \n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import precision_score, recall_score, plot_confusion_matrix, classification_report, accuracy_score, f1_score\n",
        "from sklearn import metrics\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import ListedColormap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uh4VZ-xrrUNH"
      },
      "source": [
        "# **LOADING DATA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jhm2_ZHOpQNE",
        "outputId": "d6492853-46df-444d-9177-a2600d3dd36c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     v1                                                 v2 Unnamed: 2  \\\n",
              "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
              "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
              "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
              "\n",
              "  Unnamed: 3 Unnamed: 4  \n",
              "0        NaN        NaN  \n",
              "1        NaN        NaN  \n",
              "2        NaN        NaN  \n",
              "3        NaN        NaN  \n",
              "4        NaN        NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84a257e9-4720-40ba-960f-6933da2572f8\">\n",
              "    <div 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>v1</th>\n",
              "      <th>v2</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84a257e9-4720-40ba-960f-6933da2572f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84a257e9-4720-40ba-960f-6933da2572f8 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-84a257e9-4720-40ba-960f-6933da2572f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMh-O3BDp-Kl",
        "outputId": "e02a12e5-2ee5-4ded-e5e8-d2f9b99e01b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   v1          5572 non-null   object\n",
            " 1   v2          5572 non-null   object\n",
            " 2   Unnamed: 2  50 non-null     object\n",
            " 3   Unnamed: 3  12 non-null     object\n",
            " 4   Unnamed: 4  6 non-null      object\n",
            "dtypes: object(5)\n",
            "memory usage: 217.8+ KB\n"
          ]
        }
      ],
      "source": [
        "#data = pd.read_csv(\"spam.csv\", encoding='latin-1')\n",
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iXiGeWofrFeQ",
        "outputId": "b397ea7e-215b-41f2-fbf7-a4bd81178ec8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Target                                               Text\n",
              "0    ham  Go until jurong point, crazy.. Available only ...\n",
              "1    ham                      Ok lar... Joking wif u oni...\n",
              "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3    ham  U dun say so early hor... U c already then say...\n",
              "4    ham  Nah I don't think he goes to usf, he lives aro..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6403e8d-6d04-4404-9dce-5e8941a31896\">\n",
              "    <div 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>Target</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6403e8d-6d04-4404-9dce-5e8941a31896')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e6403e8d-6d04-4404-9dce-5e8941a31896 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-e6403e8d-6d04-4404-9dce-5e8941a31896');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Dropping the redundent looking collumns (for this project)\n",
        "to_drop = [\"Unnamed: 2\",\"Unnamed: 3\",\"Unnamed: 4\"]\n",
        "data = data.drop(data[to_drop], axis=1)\n",
        "# Renaming the columns because I feel fancy today \n",
        "data.rename(columns = {\"v1\":\"Target\", \"v2\":\"Text\"}, inplace = True)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4RxA-N3roi8"
      },
      "source": [
        "*Bộ dữ liệu bao gồm 5.574 tin nhắn bằng tiếng Anh. Dữ liệu được chỉ định là ham hoặc thư rác. DataFrame có hai cột. Cột đầu tiên là \"Target\" biểu thị lớp tin nhắn là ham hoặc spam và cột \"Text\" thứ hai là chuỗi văn bản.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4ZStZrLr8nv"
      },
      "source": [
        "# **DATA EXPLORATION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "Eg7XGA0EsCS3",
        "outputId": "30609a39-af94-4bcb-f6d7-3db5f5f8ccc4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Data points')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Palette\n",
        "cols= [\"#41DFEF\", \"#FFBD59\"] \n",
        "#first of all let us evaluate the target and find out if our data is imbalanced or not\n",
        "plt.figure(figsize=(6,6))\n",
        "fg = sns.countplot(x= data[\"Target\"], palette= cols)\n",
        "fg.set_title(\"Count Plot of Classes\", color=\"#58508d\")\n",
        "fg.set_xlabel(\"Classes\", color=\"#58508d\")\n",
        "fg.set_ylabel(\"Number of Data points\", color=\"#58508d\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1gERyz4smIc"
      },
      "source": [
        "*Nhận xét: Từ biểu đồ ở trên, sự mất cân bằng dữ liệu là khá rõ ràng.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MaZtYCfxsvnl"
      },
      "source": [
        "## **FEATURE ENGINEERING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rif8sszV0Wsb"
      },
      "source": [
        "Với mục đích khám phá dữ liệu, chúng ta sẽ tạo các thuộc tính mới\n",
        "- No_of_Characters: Số ký tự trong tin nhắn văn bản\n",
        "- No_of_Words: Số từ trong tin nhắn văn bản\n",
        "- No_of_sentence: Số câu trong tin nhắn văn bản"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMcwnBuN1yCU",
        "outputId": "facf0e25-604c-4d7c-db06-458f2a9ed845"
      },
      "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": 6
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "eW34hTtRM9al",
        "outputId": "8ecf7034-d5b7-497c-d5b2-3f491ba73af2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   count       mean        std  min   25%   50%    75%    max\n",
              "No_of_Characters  5572.0  80.118808  59.690841  2.0  36.0  61.0  121.0  910.0\n",
              "No_of_Words       5572.0  18.695621  13.742587  1.0   9.0  15.0   27.0  220.0\n",
              "No_of_sentence    5572.0   1.970747   1.417778  1.0   1.0   1.0    2.0   28.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8a435e9-5a53-4018-a013-f06155ad4f8c\">\n",
              "    <div 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>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No_of_Characters</th>\n",
              "      <td>5572.0</td>\n",
              "      <td>80.118808</td>\n",
              "      <td>59.690841</td>\n",
              "      <td>2.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>910.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No_of_Words</th>\n",
              "      <td>5572.0</td>\n",
              "      <td>18.695621</td>\n",
              "      <td>13.742587</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>220.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No_of_sentence</th>\n",
              "      <td>5572.0</td>\n",
              "      <td>1.970747</td>\n",
              "      <td>1.417778</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8a435e9-5a53-4018-a013-f06155ad4f8c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8a435e9-5a53-4018-a013-f06155ad4f8c 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-b8a435e9-5a53-4018-a013-f06155ad4f8c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#Adding a column of numbers of charachters,words and sentences in each msg\n",
        "data[\"No_of_Characters\"] = data[\"Text\"].apply(len)\n",
        "data[\"No_of_Words\"]=data.apply(lambda row: nltk.word_tokenize(row[\"Text\"]), axis=1).apply(len)\n",
        "data[\"No_of_sentence\"]=data.apply(lambda row: nltk.sent_tokenize(row[\"Text\"]), axis=1).apply(len)\n",
        "\n",
        "data.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "MTq6Z-p217FE",
        "outputId": "9d540ad4-6d46-4ec7-fb0b-c728ebee137d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 603.25x540 with 12 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "fg = sns.pairplot(data=data, hue=\"Target\",palette=cols)\n",
        "plt.show(fg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGWC7EUi2Ze5"
      },
      "source": [
        "*Nhận xét: Từ biểu đồ, chúng ta có thể thấy một vài ngoại lệ trong tất cả các lớp ham. Điều này thật thú vị vì chúng ta có thể đặt một giới hạn cho một trong những điều này. Vì về cơ bản chúng chỉ ra cùng một điều, tức là độ dài của SMS.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Un9y732D213V"
      },
      "source": [
        "## **OUTLIER DETECTION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UesWWUKp26i-",
        "outputId": "79813858-39d1-42fe-b687-e5a6b3cb12d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5548, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#Dropping the outliers. \n",
        "data = data[(data[\"No_of_Characters\"]<350)]\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "mKPOIA1t29xu",
        "outputId": "fceb5fbb-2a91-4101-ed83-a9570305edf1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 603.25x540 with 12 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "fg = sns.pairplot(data=data, hue=\"Target\",palette=cols)\n",
        "plt.show(fg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9M78z2vu3QRx"
      },
      "source": [
        "# **DATA PREPROCESSING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hg2pOEFm3ZK_"
      },
      "source": [
        "Cleaning text ➡ Tokenization ➡ Removing Stopwords ➡ Lemmatization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIJ3sZbY4S-F"
      },
      "source": [
        "## CLEANING TEXT - Làm sạch văn bản\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72LN98Y24q9V"
      },
      "source": [
        "Quá trình làm sạch dữ liệu NLP là rất quan trọng. Máy tính không hiểu văn bản. Đối với máy tính, nó chỉ là một cụm ký hiệu. Để tiếp tục xử lý dữ liệu, chúng tôi cần làm cho dữ liệu sạch hơn.\n",
        "- Trong bước đầu tiên, chúng tôi chỉ trích xuất các ký tự chữ cái, chúng tôi loại bỏ dấu câu và số.\n",
        "- Trong bước tiếp theo, chúng tôi sẽ chuyển đổi tất cả các ký tự thành chữ thường.\n",
        "\n",
        "Văn bản này sau đó sẽ được sử dụng trong phân tích kỹ thuật tiếp theo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LDWZjxg49Ov",
        "outputId": "75b1d079-7949-4c0d-f300-1057b7a990ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[45;1m The First 5 Texts:\u001b[0m\n",
            "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
            "Ok lar... Joking wif u oni...\n",
            "Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n",
            "U dun say so early hor... U c already then say...\n",
            "Nah I don't think he goes to usf, he lives around here though\n"
          ]
        }
      ],
      "source": [
        "#Lets have a look at a sample of texts before cleaning\n",
        "print(\"\\033[1m\\u001b[45;1m The First 5 Texts:\\033[0m\",*data[\"Text\"][:5], sep = \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndzKDIeN5CUU",
        "outputId": "30c62713-c30e-4684-efa4-e5a5d14221b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[45;1m The First 5 Texts after cleaning:\u001b[0m\n",
            "go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat\n",
            "ok lar joking wif u oni\n",
            "free entry in a wkly comp to win fa cup final tkts st may text fa to to receive entry question std txt rate t c s apply over s\n",
            "u dun say so early hor u c already then say\n",
            "nah i don t think he goes to usf he lives around here though\n"
          ]
        }
      ],
      "source": [
        "# Defining a function to clean up the text\n",
        "def Clean(Text):\n",
        "    sms = re.sub('[^a-zA-Z]', ' ', Text) #Replacing all non-alphabetic characters with a space\n",
        "    sms = sms.lower() #converting to lowecase\n",
        "    sms = sms.split()\n",
        "    sms = ' '.join(sms)\n",
        "    return sms\n",
        "\n",
        "data[\"Clean_Text\"] = data[\"Text\"].apply(Clean)\n",
        "#Lets have a look at a sample of texts after cleaning\n",
        "print(\"\\033[1m\\u001b[45;1m The First 5 Texts after cleaning:\\033[0m\",*data[\"Clean_Text\"][:5], sep = \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LyBPZtWt_xyB"
      },
      "source": [
        "## TOKENIZATION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDxREHNV_u2g"
      },
      "source": [
        "Mã hóa là chia nhỏ dữ liệu phức tạp thành các đơn vị nhỏ hơn được gọi là mã thông báo. Nó có thể được thực hiện bằng cách tách đoạn văn thành câu và câu thành từ. Tôi đang tách Clean_Text thành các từ ở bước này."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRXI2M5W_9aO",
        "outputId": "317721d4-3798-4b11-db54-3965512e120d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[45;1m The First 5 Texts after Tokenizing:\u001b[0m\n",
            "['go', 'until', 'jurong', 'point', 'crazy', 'available', 'only', 'in', 'bugis', 'n', 'great', 'world', 'la', 'e', 'buffet', 'cine', 'there', 'got', 'amore', 'wat']\n",
            "['ok', 'lar', 'joking', 'wif', 'u', 'oni']\n",
            "['free', 'entry', 'in', 'a', 'wkly', 'comp', 'to', 'win', 'fa', 'cup', 'final', 'tkts', 'st', 'may', 'text', 'fa', 'to', 'to', 'receive', 'entry', 'question', 'std', 'txt', 'rate', 't', 'c', 's', 'apply', 'over', 's']\n",
            "['u', 'dun', 'say', 'so', 'early', 'hor', 'u', 'c', 'already', 'then', 'say']\n",
            "['nah', 'i', 'don', 't', 'think', 'he', 'goes', 'to', 'usf', 'he', 'lives', 'around', 'here', 'though']\n"
          ]
        }
      ],
      "source": [
        "data[\"Tokenize_Text\"]=data.apply(lambda row: nltk.word_tokenize(row[\"Clean_Text\"]), axis=1)\n",
        "\n",
        "print(\"\\033[1m\\u001b[45;1m The First 5 Texts after Tokenizing:\\033[0m\",*data[\"Tokenize_Text\"][:5], sep = \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYedM7tXBLIw"
      },
      "source": [
        "## REMOVING STOPWORDS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tC23ZEALBUWQ"
      },
      "source": [
        "Từ dừng là những từ xuất hiện thường xuyên (chẳng hạn như ít, là, an, v.v.). Những từ này giữ ý nghĩa trong cấu trúc câu, nhưng không đóng góp nhiều vào quá trình xử lý ngôn ngữ trong NLP. Với mục đích loại bỏ phần dư thừa trong quá trình xử lý của chúng tôi, tôi sẽ loại bỏ những phần đó. Thư viện NLTK có một tập hợp các từ dừng mặc định mà chúng tôi sẽ xóa."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgYQ_6TXBkvC",
        "outputId": "d040ecee-8032-4c00-c1c4-fbe7ffd4786e"
      },
      "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": 14
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "agAsFVViBeP-",
        "outputId": "f5836b7b-8e73-421d-c011-bf9b59c48177"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[45;1m The First 5 Texts after removing the stopwords:\u001b[0m\n",
            "['go', 'jurong', 'point', 'crazy', 'available', 'bugis', 'n', 'great', 'world', 'la', 'e', 'buffet', 'cine', 'got', 'amore', 'wat']\n",
            "['ok', 'lar', 'joking', 'wif', 'u', 'oni']\n",
            "['free', 'entry', 'wkly', 'comp', 'win', 'fa', 'cup', 'final', 'tkts', 'st', 'may', 'text', 'fa', 'receive', 'entry', 'question', 'std', 'txt', 'rate', 'c', 'apply']\n",
            "['u', 'dun', 'say', 'early', 'hor', 'u', 'c', 'already', 'say']\n",
            "['nah', 'think', 'goes', 'usf', 'lives', 'around', 'though']\n"
          ]
        }
      ],
      "source": [
        "# Removing the stopwords function\n",
        "def remove_stopwords(text):\n",
        "    stop_words = set(stopwords.words(\"english\"))\n",
        "    filtered_text = [word for word in text if word not in stop_words]\n",
        "    return filtered_text\n",
        "\n",
        "data[\"Nostopword_Text\"] = data[\"Tokenize_Text\"].apply(remove_stopwords)\n",
        "\n",
        "print(\"\\033[1m\\u001b[45;1m The First 5 Texts after removing the stopwords:\\033[0m\",*data[\"Nostopword_Text\"][:5], sep = \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoAA_xW8BxqZ"
      },
      "source": [
        "## LEMMATIZATION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJpeH7UmB28a"
      },
      "source": [
        "Tạo gốc là quá trình lấy dạng gốc của một từ. Thân hoặc rễ là phần mà các mối liên kết vô hướng được thêm vào. Phần gốc của một từ được tạo bằng cách loại bỏ tiền tố hoặc hậu tố của một từ. Nó quay trở lại từ nguyên của từ này. Ngôn ngữ phát triển theo thời gian. Nhiều ngôn ngữ khác nhau phân nhánh vào nhau; ví dụ, tiếng Anh là một phái sinh của tiếng Latinh. Do đó, việc đặt gốc một từ sẽ đưa nó trở lại từ gốc.\n",
        "\n",
        "lemmatization cũng chuyển một từ về dạng gốc của nó. Tuy nhiên, sự khác biệt là sự bổ sung đảm bảo rằng từ gốc thuộc về ngôn ngữ mà người ta đang xử lý, trong trường hợp của chúng ta, đó là tiếng Anh. Nếu chúng ta sử dụng lemmatization, đầu ra sẽ là tiếng Anh."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-P7PdvZMB-g7",
        "outputId": "83794e20-6715-48ba-cc60-e0daac401573"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fFkLvV0CEjR",
        "outputId": "0699d4e0-677e-4579-b750-2b46f548e8eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('omw-1.4')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKrSrWa5B74G",
        "outputId": "090a1407-b9bc-4da2-d6cc-0a289c35983c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[45;1m The First 5 Texts after lemitization:\u001b[0m\n",
            "['go', 'jurong', 'point', 'crazy', 'available', 'bugis', 'n', 'great', 'world', 'la', 'e', 'buffet', 'cine', 'get', 'amore', 'wat']\n",
            "['ok', 'lar', 'joke', 'wif', 'u', 'oni']\n",
            "['free', 'entry', 'wkly', 'comp', 'win', 'fa', 'cup', 'final', 'tkts', 'st', 'may', 'text', 'fa', 'receive', 'entry', 'question', 'std', 'txt', 'rate', 'c', 'apply']\n",
            "['u', 'dun', 'say', 'early', 'hor', 'u', 'c', 'already', 'say']\n",
            "['nah', 'think', 'go', 'usf', 'live', 'around', 'though']\n"
          ]
        }
      ],
      "source": [
        "lemmatizer = WordNetLemmatizer()\n",
        "# lemmatize string\n",
        "def lemmatize_word(text):\n",
        "    #word_tokens = word_tokenize(text)\n",
        "    # provide context i.e. part-of-speech\n",
        "    lemmas = [lemmatizer.lemmatize(word, pos ='v') for word in text]\n",
        "    return lemmas\n",
        "\n",
        "data[\"Lemmatized_Text\"] = data[\"Nostopword_Text\"].apply(lemmatize_word)\n",
        "print(\"\\033[1m\\u001b[45;1m The First 5 Texts after lemitization:\\033[0m\",*data[\"Lemmatized_Text\"][:5], sep = \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpsOuqpfCnfU"
      },
      "source": [
        "# **VECTORIZE**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkJKxLo0C3Ei"
      },
      "source": [
        "TF-IDF trong NLP là viết tắt của Term Frequency - Inverse document frequency. Trong NLP, dữ liệu được làm sạch cần được chuyển đổi thành một định dạng số trong đó mỗi từ được biểu diễn bằng một ma trận. Điều này còn được gọi là nhúng từ hoặc vector hóa từ.\n",
        "\n",
        "Tần suất thuật ngữ (TF) = (Tần suất của một thuật ngữ trong tài liệu) / (Tổng số thuật ngữ trong tài liệu) Tần suất tài liệu nghịch đảo (IDF) = log ((tổng số tài liệu) / (số tài liệu có thuật ngữ t)) I sẽ sử dụng TfidfVectorizer () để vectơ hóa dữ liệu được xử lý trước.\n",
        "\n",
        "Các bước trong Vectorizing:\n",
        "\n",
        "- Tạo một kho văn bản bị lemmatized\n",
        "- Chuyển đổi kho dữ liệu ở dạng vectơ\n",
        "- Mã hóa nhãn cho các lớp trong Target\n",
        "\n",
        "Lưu ý: Cho đến nay chúng tôi vẫn theo dõi các cột trong dữ liệu của mình với mục đích giải thích"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZAvBuQJ_DH7-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c29119c0-51fa-416e-cec4-275219752d97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[45;1m The First 5 lines in corpus :\u001b[0m\n",
            "go jurong point crazy available bugis n great world la e buffet cine get amore wat\n",
            "ok lar joke wif u oni\n",
            "free entry wkly comp win fa cup final tkts st may text fa receive entry question std txt rate c apply\n",
            "u dun say early hor u c already say\n",
            "nah think go usf live around though\n"
          ]
        }
      ],
      "source": [
        "#Creating a corpus of text feature to encode further into vectorized form\n",
        "corpus= []\n",
        "for i in data[\"Lemmatized_Text\"]:\n",
        "    msg = ' '.join([row for row in i])\n",
        "    corpus.append(msg)\n",
        "    \n",
        "corpus[:5]\n",
        "print(\"\\033[1m\\u001b[45;1m The First 5 lines in corpus :\\033[0m\",*corpus[:5], sep = \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkOSR_QKDLtM",
        "outputId": "9cf2e619-3e75-413a-fd58-3288e4e9c668"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "#Changing text data in to numbers. \n",
        "tfidf = TfidfVectorizer()\n",
        "X = tfidf.fit_transform(corpus).toarray()\n",
        "#Let's have a look at our feature \n",
        "X.dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kFA-7nPPDPFQ"
      },
      "outputs": [],
      "source": [
        "#Label encode the Target and use it as y\n",
        "label_encoder = LabelEncoder()\n",
        "data[\"Target\"] = label_encoder.fit_transform(data[\"Target\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Xs46vUNDShq"
      },
      "source": [
        "# **MODEL BUILDING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "de2lWbfADX6E"
      },
      "source": [
        "Các bước liên quan đến Xây dựng Mô hình\n",
        "- Thiết lập các tính năng và nhắm mục tiêu dưới dạng X và y\n",
        "- Tách bộ kiểm tra và đào tạo\n",
        "- Xây dựng một hệ thống mô hình cho bốn bộ phân loại khác nhau.\n",
        "  1. Naïve Bayes\n",
        "  2. RandomForestClassifier\n",
        "  3. KNeighborsClassifier\n",
        "  4. Support Vector Machines\n",
        "- Phù hợp với tất cả các mô hình trên dữ liệu đào tạo\n",
        "- Nhận xác nhận chéo trên tập hợp đào tạo cho tất cả các mô hình để đảm bảo độ chính xác"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Z1lAXdyDt7c"
      },
      "outputs": [],
      "source": [
        "#Setting values for labels and feature as y and X(we already did X in vectorizing...)\n",
        "y = data[\"Target\"] \n",
        "# Splitting the testing and training sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XKRIbdRoDyOg"
      },
      "outputs": [],
      "source": [
        "#Testing on the following classifiers\n",
        "classifiers = [MultinomialNB(), \n",
        "               RandomForestClassifier(),\n",
        "               KNeighborsClassifier(), \n",
        "               SVC()]\n",
        "for cls in classifiers:\n",
        "    cls.fit(X_train, y_train)\n",
        "\n",
        "# Dictionary of pipelines and model types for ease of reference\n",
        "pipe_dict = {0: \"NaiveBayes\", 1: \"RandomForest\", 2: \"KNeighbours\",3: \"SVC\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwqY5lMzEEu_",
        "outputId": "ff7ac65c-5e86-484b-fbd6-48ce1607a5b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NaiveBayes: 0.967552 \n",
            "RandomForest: 0.975214 \n",
            "KNeighbours: 0.911450 \n",
            "SVC: 0.974086 \n"
          ]
        }
      ],
      "source": [
        "# Cossvalidation \n",
        "for i, model in enumerate(classifiers):\n",
        "    cv_score = cross_val_score(model, X_train,y_train,scoring=\"accuracy\", cv=10)\n",
        "    print(\"%s: %f \" % (pipe_dict[i], cv_score.mean()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cj5ywoTNHGoD"
      },
      "source": [
        "# **EVALUATING MODELS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kp3OyaAOHOPc"
      },
      "source": [
        "\n",
        "Testing the models on Testset\n",
        "- Accuracy Report\n",
        "- Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EXQqYw4-HS3r"
      },
      "outputs": [],
      "source": [
        "# Model Evaluation\n",
        "# creating lists of varios scores\n",
        "precision =[]\n",
        "recall =[]\n",
        "f1_score = []\n",
        "trainset_accuracy = []\n",
        "testset_accuracy = []\n",
        "\n",
        "for i in classifiers:\n",
        "    pred_train = i.predict(X_train)\n",
        "    pred_test = i.predict(X_test)\n",
        "    prec = metrics.precision_score(y_test, pred_test)\n",
        "    recal = metrics.recall_score(y_test, pred_test)\n",
        "    f1_s = metrics.f1_score(y_test, pred_test)\n",
        "    train_accuracy = model.score(X_train,y_train)\n",
        "    test_accuracy = model.score(X_test,y_test)\n",
        "  \n",
        "    #Appending scores\n",
        "    precision.append(prec)\n",
        "    recall.append(recal)\n",
        "    f1_score.append(f1_s)\n",
        "    trainset_accuracy.append(train_accuracy)\n",
        "    testset_accuracy.append(test_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dfjiCL24JG0M"
      },
      "outputs": [],
      "source": [
        "# initialise data of lists.\n",
        "data = {'Precision':precision,\n",
        "'Recall':recall,\n",
        "'F1score':f1_score,\n",
        "'Accuracy on Testset':testset_accuracy,\n",
        "'Accuracy on Trainset':trainset_accuracy}\n",
        "# Creates pandas DataFrame.\n",
        "Results = pd.DataFrame(data, index =[\"NaiveBayes\", \"RandomForest\", \"KNeighbours\",\"SVC\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ZR3NxIsDJJXu",
        "outputId": "591b1a59-e6f6-4aa4-bd1f-39f93a08225f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fbe43b0e890>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_78bff_row0_col0, #T_78bff_row0_col1, #T_78bff_row0_col2, #T_78bff_row1_col0, #T_78bff_row1_col1, #T_78bff_row1_col2, #T_78bff_row3_col0, #T_78bff_row3_col1, #T_78bff_row3_col2 {\n",
              "  background-color: #ffbd59;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_78bff_row0_col3, #T_78bff_row0_col4, #T_78bff_row1_col3, #T_78bff_row1_col4, #T_78bff_row2_col0, #T_78bff_row2_col1, #T_78bff_row2_col2, #T_78bff_row2_col3, #T_78bff_row2_col4, #T_78bff_row3_col3, #T_78bff_row3_col4 {\n",
              "  background-color: #41dfef;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_78bff_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Precision</th>\n",
              "      <th class=\"col_heading level0 col1\" >Recall</th>\n",
              "      <th class=\"col_heading level0 col2\" >F1score</th>\n",
              "      <th class=\"col_heading level0 col3\" >Accuracy on Testset</th>\n",
              "      <th class=\"col_heading level0 col4\" >Accuracy on Trainset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_78bff_level0_row0\" class=\"row_heading level0 row0\" >NaiveBayes</th>\n",
              "      <td id=\"T_78bff_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "      <td id=\"T_78bff_row0_col1\" class=\"data row0 col1\" >0.705882</td>\n",
              "      <td id=\"T_78bff_row0_col2\" class=\"data row0 col2\" >0.827586</td>\n",
              "      <td id=\"T_78bff_row0_col3\" class=\"data row0 col3\" >0.974775</td>\n",
              "      <td id=\"T_78bff_row0_col4\" class=\"data row0 col4\" >0.997521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_78bff_level0_row1\" class=\"row_heading level0 row1\" >RandomForest</th>\n",
              "      <td id=\"T_78bff_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
              "      <td id=\"T_78bff_row1_col1\" class=\"data row1 col1\" >0.801471</td>\n",
              "      <td id=\"T_78bff_row1_col2\" class=\"data row1 col2\" >0.889796</td>\n",
              "      <td id=\"T_78bff_row1_col3\" class=\"data row1 col3\" >0.974775</td>\n",
              "      <td id=\"T_78bff_row1_col4\" class=\"data row1 col4\" >0.997521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_78bff_level0_row2\" class=\"row_heading level0 row2\" >KNeighbours</th>\n",
              "      <td id=\"T_78bff_row2_col0\" class=\"data row2 col0\" >0.977778</td>\n",
              "      <td id=\"T_78bff_row2_col1\" class=\"data row2 col1\" >0.323529</td>\n",
              "      <td id=\"T_78bff_row2_col2\" class=\"data row2 col2\" >0.486188</td>\n",
              "      <td id=\"T_78bff_row2_col3\" class=\"data row2 col3\" >0.974775</td>\n",
              "      <td id=\"T_78bff_row2_col4\" class=\"data row2 col4\" >0.997521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_78bff_level0_row3\" class=\"row_heading level0 row3\" >SVC</th>\n",
              "      <td id=\"T_78bff_row3_col0\" class=\"data row3 col0\" >0.990909</td>\n",
              "      <td id=\"T_78bff_row3_col1\" class=\"data row3 col1\" >0.801471</td>\n",
              "      <td id=\"T_78bff_row3_col2\" class=\"data row3 col2\" >0.886179</td>\n",
              "      <td id=\"T_78bff_row3_col3\" class=\"data row3 col3\" >0.974775</td>\n",
              "      <td id=\"T_78bff_row3_col4\" class=\"data row3 col4\" >0.997521</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "cmap2 = ListedColormap([\"#41DFEF\", \"#FFBD59\"])\n",
        "Results.style.background_gradient(cmap=cmap2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "TdSboTufJM0F",
        "outputId": "dea449ee-7c78-401f-f1a6-5be9e0b54517"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x432 with 8 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "cmap = ListedColormap([\"#41DFEF\", \"#FFBD59\"])\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9,6))\n",
        "\n",
        "for cls, ax in zip(classifiers, axes.flatten()):\n",
        "    plot_confusion_matrix(cls, \n",
        "                          X_test, \n",
        "                          y_test, \n",
        "                          ax=ax, \n",
        "                          cmap= cmap,   \n",
        "                          )\n",
        "    ax.title.set_text(type(cls).__name__)\n",
        "plt.tight_layout()  \n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}