NewsClassification(2).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZP5vk-eqLNH"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "MTKR7yN8i007"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def dummy_npwarn_decorator_factory():\n",
        "  def npwarn_decorator(x):\n",
        "    return x\n",
        "  return npwarn_decorator\n",
        "np._no_nep50_warning = getattr(np, '_no_nep50_warning', dummy_npwarn_decorator_factory)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 895
        },
        "id": "qkH8UnBwjA5x",
        "outputId": "0954d481-f6e9-4f21-c8f8-82b16e440832"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: hazm in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.10.0)\n",
            "Requirement already satisfied: fasttext-wheel<0.10.0,>=0.9.2 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from hazm) (0.9.2)\n",
            "Requirement already satisfied: flashtext<3.0,>=2.7 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from hazm) (2.7)\n",
            "Requirement already satisfied: gensim<5.0.0,>=4.3.1 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from hazm) (4.3.2)\n",
            "Requirement already satisfied: nltk<4.0.0,>=3.8.1 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from hazm) (3.8.1)\n",
            "Requirement already satisfied: numpy==1.24.3 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from hazm) (1.24.3)\n",
            "Requirement already satisfied: python-crfsuite<0.10.0,>=0.9.9 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from hazm) (0.9.10)\n",
            "Requirement already satisfied: scikit-learn<2.0.0,>=1.2.2 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from hazm) (1.4.0)\n",
            "Requirement already satisfied: pybind11>=2.2 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from fasttext-wheel<0.10.0,>=0.9.2->hazm) (2.11.1)\n",
            "Requirement already satisfied: setuptools>=0.7.0 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from fasttext-wheel<0.10.0,>=0.9.2->hazm) (58.1.0)\n",
            "Requirement already satisfied: scipy>=1.7.0 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from gensim<5.0.0,>=4.3.1->hazm) (1.12.0)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from gensim<5.0.0,>=4.3.1->hazm) (6.4.0)\n",
            "Requirement already satisfied: click in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from nltk<4.0.0,>=3.8.1->hazm) (8.1.3)\n",
            "Requirement already satisfied: joblib in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from nltk<4.0.0,>=3.8.1->hazm) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from nltk<4.0.0,>=3.8.1->hazm) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from nltk<4.0.0,>=3.8.1->hazm) (4.66.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from scikit-learn<2.0.0,>=1.2.2->hazm) (3.2.0)\n",
            "Requirement already satisfied: colorama in c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from click->nltk<4.0.0,>=3.8.1->hazm) (0.4.6)\n"
          ]
        }
      ],
      "source": [
        "!pip install hazm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "m9iT1XC4jH8X"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From c:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from hazm import Normalizer, Stemmer, Lemmatizer\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from scipy.sparse import hstack\n",
        "from pandas.api.types import CategoricalDtype\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow import keras\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.models import Sequential\n",
        "from keras import utils\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-aVpZfQaLf4"
      },
      "source": [
        "# Text Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hq4bJK5jjEX4",
        "outputId": "02d321c9-2f75-46fc-abf4-2f6fed1bdc01"
      },
      "outputs": [
        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'google.colab'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m drive\n\u001b[0;32m      2\u001b[0m drive\u001b[38;5;241m.\u001b[39mmount(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/content/drive\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
            "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PGh9vkwZlAEi",
        "outputId": "cef0a23d-5c11-4bb3-dee7-0b3ae1ab564f"
      },
      "outputs": [
        {
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            "text/plain": [
              "        Unnamed: 0                                               Text  \\\n",
              "0                0  \\nخبرنامه دانشگاه علم و صنعت ايران \\nشماره ياز...   \n",
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              "2                2  \\nانجمن توليدكنندگان تجهيزات صنعت نفت تشكيل شد...   \n",
              "3                3  \\nكرتين براي سومين بار نخست وزير كانادا \\nشد \\...   \n",
              "4                4  \\nخداحافظ رفقا \\nنمايندگان اروپاي شرقي در جام ...   \n",
              "...            ...                                                ...   \n",
              "150091      150091  \\nمشكل بزرگ سدسازي \\nوزير نيرو: \\nدر كشور كمبو...   \n",
              "150092      150092  \\nخودسوزي يك هوادار پ ك ك در شهر دياربكر \\nترك...   \n",
              "150093      150093  \\nاز نيمه دوم سال جاري در 600 دستگاه \\nاتوبوس ...   \n",
              "150094      150094  \\nدر كتابخانه مركز دانشگاه \\nتهران و دانشكده ه...   \n",
              "150095      150095  \\nپيام رئيس جمهوري به سران \\nكشورهاي عضو سازما...   \n",
              "\n",
              "                        Category  \n",
              "0            Science and Culture  \n",
              "1                          Sport  \n",
              "2                        Economy  \n",
              "3       Miscellaneous.World News  \n",
              "4                          Sport  \n",
              "...                          ...  \n",
              "150091             Miscellaneous  \n",
              "150092  Miscellaneous.Happenings  \n",
              "150093       Miscellaneous.Urban  \n",
              "150094       Science and Culture  \n",
              "150095                  Politics  \n",
              "\n",
              "[150096 rows x 3 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/train.csv')\n",
        "dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8btiQdCelBSC",
        "outputId": "d25f7877-4e79-41c7-cd5a-6dea88d3c028"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Science and Culture', 'Sport', 'Economy',\n",
              "       'Miscellaneous.World News', 'Miscellaneous.Urban', 'Social.Women',\n",
              "       'Social', 'Literature and Art', 'Politics', 'Miscellaneous',\n",
              "       'Economy.Bank and Bourse', 'Politics.Iran Politics', 'Tourism',\n",
              "       'Social.Religion', 'Miscellaneous.Picture',\n",
              "       'Miscellaneous.Happenings', 'Science and Culture.Science.Book',\n",
              "       'Literature and Art.Art', 'Miscellaneous.Islamic Councils',\n",
              "       'Literature and Art.Art.Cinema',\n",
              "       'Science and Culture.Science.Information and Communication Technology',\n",
              "       'Economy.Oil', 'Economy.Commerce', 'Natural Environment',\n",
              "       'Science and Culture.Science', 'Economy.Industry',\n",
              "       'Economy.Agriculture', 'Sport.World Cup',\n",
              "       'Miscellaneous.Picture.Caricature', 'Literature and Art.Art.Music',\n",
              "       'Literature and Art.Art.Theater',\n",
              "       'Economy.Dwelling and Construction',\n",
              "       'Science and Culture.Science.Medicine and Remedy',\n",
              "       'Literature and Art.Literature'], dtype=object)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe['Category'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pL67aHvEcYIo"
      },
      "source": [
        "there are many categories but some of them are sub-Categories of a main Category like 'Social.women' which is related to 'Social'.In order to clean our dataframe we need to merge this sub-categories into their main."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "H6FpDFSllISj"
      },
      "outputs": [],
      "source": [
        "for i in range(len(dataframe)):\n",
        "    if dataframe.loc[i, 'Category'].startswith('Miscellaneous'):\n",
        "        dataframe.loc[i, 'Category'] = 'Miscellaneous'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Economy'):\n",
        "        dataframe.loc[i, 'Category'] = 'Economy'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Politics'):\n",
        "        dataframe.loc[i, 'Category'] = 'Politics'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Sport'):\n",
        "        dataframe.loc[i, 'Category'] = 'Sport'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Science and Culture'):\n",
        "        dataframe.loc[i, 'Category'] = 'Science and Culture'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Social'):\n",
        "        dataframe.loc[i, 'Category'] = 'Social'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Literature and Art'):\n",
        "        dataframe.loc[i, 'Category'] = 'Literature and Art'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Tourism'):\n",
        "        dataframe.loc[i, 'Category'] = 'Tourism'\n",
        "    elif dataframe.loc[i, 'Category'].startswith('Natural Environment'):\n",
        "        dataframe.loc[i, 'Category'] = 'Natural Environment'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PwNzoVFvgzfQ"
      },
      "source": [
        "We need to check if there is an unlabeled data.if we had a data with no label then we should remove it from our dataframe .Fortunatley , we don't have this issue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4HoxoowLMG7"
      },
      "source": [
        "Now we shall split the data before further modifications"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3897dNd667N"
      },
      "source": [
        "Next Step = Removing Stop Words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Jh16qWfbmGel"
      },
      "outputs": [],
      "source": [
        "# def tokenize_text(text):\n",
        "#     tokens = word_tokenize(text)\n",
        "#     return tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "z9pkARxamRxI"
      },
      "outputs": [],
      "source": [
        "# from hazm import word_tokenize\n",
        "# dataframe['Tokenized_Text'] = dataframe['Text'].apply(tokenize_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nAyuUqLVmShV",
        "outputId": "b3a28626-e074-4799-f282-56bf9ad8b0b9"
      },
      "outputs": [
        {
          "data": {
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              "      <td>\\nخبرنامه دانشگاه علم و صنعت ايران \\nشماره ياز...</td>\n",
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              "      <td>\\nتا پايان سال 1378 دهها زمين فوتبال و \\nسالن ...</td>\n",
              "      <td>Sport</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>\\nانجمن توليدكنندگان تجهيزات صنعت نفت تشكيل شد...</td>\n",
              "      <td>Economy</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>150092</th>\n",
              "      <td>150092</td>\n",
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              "    <tr>\n",
              "      <th>150094</th>\n",
              "      <td>150094</td>\n",
              "      <td>\\nدر كتابخانه مركز دانشگاه \\nتهران و دانشكده ه...</td>\n",
              "      <td>Science and Culture</td>\n",
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              "      <td>\\nپيام رئيس جمهوري به سران \\nكشورهاي عضو سازما...</td>\n",
              "      <td>Politics</td>\n",
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            ],
            "text/plain": [
              "        Unnamed: 0                                               Text  \\\n",
              "0                0  \\nخبرنامه دانشگاه علم و صنعت ايران \\nشماره ياز...   \n",
              "1                1  \\nتا پايان سال 1378 دهها زمين فوتبال و \\nسالن ...   \n",
              "2                2  \\nانجمن توليدكنندگان تجهيزات صنعت نفت تشكيل شد...   \n",
              "3                3  \\nكرتين براي سومين بار نخست وزير كانادا \\nشد \\...   \n",
              "4                4  \\nخداحافظ رفقا \\nنمايندگان اروپاي شرقي در جام ...   \n",
              "...            ...                                                ...   \n",
              "150091      150091  \\nمشكل بزرگ سدسازي \\nوزير نيرو: \\nدر كشور كمبو...   \n",
              "150092      150092  \\nخودسوزي يك هوادار پ ك ك در شهر دياربكر \\nترك...   \n",
              "150093      150093  \\nاز نيمه دوم سال جاري در 600 دستگاه \\nاتوبوس ...   \n",
              "150094      150094  \\nدر كتابخانه مركز دانشگاه \\nتهران و دانشكده ه...   \n",
              "150095      150095  \\nپيام رئيس جمهوري به سران \\nكشورهاي عضو سازما...   \n",
              "\n",
              "                   Category  \n",
              "0       Science and Culture  \n",
              "1                     Sport  \n",
              "2                   Economy  \n",
              "3             Miscellaneous  \n",
              "4                     Sport  \n",
              "...                     ...  \n",
              "150091        Miscellaneous  \n",
              "150092        Miscellaneous  \n",
              "150093        Miscellaneous  \n",
              "150094  Science and Culture  \n",
              "150095             Politics  \n",
              "\n",
              "[150096 rows x 3 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPhuHvfvmWuJ",
        "outputId": "76320ab3-8e94-465d-acd0-eff8bbac2f2f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['و', 'در', 'به', 'از', 'که', 'این', 'را', 'با', 'است', 'برای',\n",
              "       'آن', 'یک', 'خود', 'تا', 'کرد', 'بر', 'هم', 'نیز', 'گفت',\n",
              "       'می\\u200cشود', 'وی', 'شد', 'دارد', 'ما', 'اما', 'یا', 'شده',\n",
              "       'باید', 'هر', 'آنها', 'بود', 'او', 'دیگر', 'دو', 'مورد',\n",
              "       'می\\u200cکند', 'شود', 'کند', 'وجود', 'بین', 'پیش', 'شده_است', 'پس',\n",
              "       'نظر', 'اگر', 'همه', 'یکی', 'حال', 'هستند', 'من', 'کنند', 'نیست',\n",
              "       'باشد', 'چه', 'بی', 'می', 'بخش', 'می\\u200cکنند', 'همین', 'افزود',\n",
              "       'هایی', 'دارند', 'راه', 'همچنین', 'روی', 'داد', 'بیشتر', 'بسیار',\n",
              "       'سه', 'داشت', 'چند', 'سوی', 'تنها', 'هیچ', 'میان', 'اینکه', 'شدن',\n",
              "       'بعد', 'جدید', 'ولی', 'حتی', 'کردن', 'برخی', 'کردند',\n",
              "       'می\\u200cدهد', 'اول', 'نه', 'کرده_است', 'نسبت', 'بیش', 'شما',\n",
              "       'چنین', 'طور', 'افراد', 'تمام', 'درباره', 'بار', 'بسیاری',\n",
              "       'می\\u200cتواند', 'کرده', 'چون', 'ندارد', 'دوم', 'بزرگ', 'طی',\n",
              "       'حدود', 'همان', 'بدون', 'البته', 'آنان', 'می\\u200cگوید', 'دیگری',\n",
              "       'خواهد_شد', 'کنیم', 'قابل', 'یعنی', 'رشد', 'می\\u200cتوان', 'وارد',\n",
              "       'کل', 'ویژه', 'قبل', 'براساس', 'نیاز', 'گذاری', 'هنوز', 'لازم',\n",
              "       'سازی', 'بوده_است', 'چرا', 'می\\u200cشوند', 'وقتی', 'گرفت', 'کم',\n",
              "       'جای', 'حالی', 'تغییر', 'پیدا', 'اکنون', 'تحت', 'باعث', 'مدت',\n",
              "       'فقط', 'زیادی', 'تعداد', 'آیا', 'بیان', 'رو', 'شدند', 'عدم',\n",
              "       'کرده_اند', 'بودن', 'نوع', 'بلکه', 'جاری', 'دهد', 'برابر', 'مهم',\n",
              "       'بوده', 'اخیر', 'مربوط', 'امر', 'زیر', 'گیری', 'شاید', 'خصوص',\n",
              "       'آقای', 'اثر', 'کننده', 'بودند', 'فکر', 'کنار', 'اولین', 'سوم',\n",
              "       'سایر', 'کنید', 'ضمن', 'مانند', 'باز', 'می\\u200cگیرد', 'ممکن',\n",
              "       'حل', 'دارای', 'پی', 'مثل', 'می\\u200cرسد', 'اجرا', 'دور', 'منظور',\n",
              "       'کسی', 'موجب', 'طول', 'امکان', 'آنچه', 'تعیین', 'گفته', 'شوند',\n",
              "       'جمع', 'خیلی', 'علاوه', 'گونه', 'تاکنون', 'رسید', 'ساله', 'گرفته',\n",
              "       'شده_اند', 'علت', 'چهار', 'داشته_باشد', 'خواهد_بود', 'طرف', 'تهیه',\n",
              "       'تبدیل', 'مناسب', 'زیرا', 'مشخص', 'می\\u200cتوانند', 'نزدیک',\n",
              "       'جریان', 'روند', 'بنابراین', 'می\\u200cدهند', 'یافت', 'نخستین',\n",
              "       'بالا', 'پنج', 'ریزی', 'عالی', 'چیزی', 'نخست', 'بیشتری', 'ترتیب',\n",
              "       'شده_بود', 'خاص', 'خوبی', 'خوب', 'شروع', 'فرد', 'کامل', 'غیر',\n",
              "       'می\\u200cرود', 'دهند', 'آخرین', 'دادن', 'جدی', 'بهترین', 'شامل',\n",
              "       'گیرد', 'بخشی', 'باشند', 'تمامی', 'بهتر', 'داده_است', 'حد', 'نبود',\n",
              "       'کسانی', 'می\\u200cکرد', 'داریم', 'علیه', 'می\\u200cباشد', 'دانست',\n",
              "       'ناشی', 'داشتند', 'دهه', 'می\\u200cشد', 'ایشان', 'آنجا',\n",
              "       'گرفته_است', 'دچار', 'می\\u200cآید', 'لحاظ', 'آنکه', 'داده', 'بعضی',\n",
              "       'هستیم', 'اند', 'برداری', 'نباید', 'می\\u200cکنیم', 'نشست', 'سهم',\n",
              "       'همیشه', 'آمد', 'اش', 'وگو', 'می\\u200cکنم', 'حداقل', 'طبق', 'جا',\n",
              "       'خواهد_کرد', 'نوعی', 'چگونه', 'رفت', 'هنگام', 'فوق', 'روش',\n",
              "       'ندارند', 'سعی', 'بندی', 'شمار', 'کلی', 'کافی', 'مواجه', 'همچنان',\n",
              "       'زیاد', 'سمت', 'کوچک', 'داشته_است', 'چیز', 'پشت', 'آورد', 'حالا',\n",
              "       'روبه', 'سال\\u200cهای', 'دادند', 'می\\u200cکردند', 'عهده', 'نیمه',\n",
              "       'جایی', 'دیگران', 'سی', 'بروز', 'یکدیگر', 'آمده_است', 'جز', 'کنم',\n",
              "       'سپس', 'کنندگان', 'خودش', 'همواره', 'یافته', 'شان', 'صرف',\n",
              "       'نمی\\u200cشود', 'رسیدن', 'چهارم', 'یابد', 'متر', 'ساز', 'داشته',\n",
              "       'کرده_بود', 'باره', 'نحوه', 'کردم', 'تو', 'شخصی', 'داشته_باشند',\n",
              "       'محسوب', 'پخش', 'کمی', 'متفاوت', 'سراسر', 'کاملا', 'داشتن', 'نظیر',\n",
              "       'آمده', 'گروهی', 'فردی', 'ع', 'همچون', 'خطر', 'خویش', 'کدام',\n",
              "       'دسته', 'سبب', 'عین', 'آوری', 'متاسفانه', 'بیرون', 'دار', 'ابتدا',\n",
              "       'شش', 'افرادی', 'می\\u200cگویند', 'سالهای', 'درون', 'نیستند',\n",
              "       'یافته_است', 'پر', 'خاطرنشان', 'گاه', 'جمعی', 'اغلب', 'دوباره',\n",
              "       'می\\u200cیابد', 'لذا', 'زاده', 'گردد', 'اینجا'], dtype='<U11')"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Second = remove Stop Words\n",
        "stopwords = np.load('/content/drive/MyDrive/Colab Notebooks/stopwords.npy')\n",
        "stopwords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ZrDw6MelmaPI"
      },
      "outputs": [],
      "source": [
        "def remove_stopwords(text):\n",
        "    # critical = 'برای'\n",
        "    text = ' '.join([i for i in text.lower().split(' ') if i not in stopwords])\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "o5jc0b5fmayQ",
        "outputId": "1d1d7648-395b-459b-acac-ccd1664d9b8e"
      },
      "outputs": [
        {
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              "      <td>\\nتا پايان سال 1378 دهها زمين فوتبال \\nسالن ور...</td>\n",
              "      <td>Sport</td>\n",
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              "    <tr>\n",
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              "      <th>150091</th>\n",
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              "      <th>150092</th>\n",
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              "      <th>150093</th>\n",
              "      <td>150093</td>\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8874ef98-b89d-4ab6-9ced-789fae97fc93 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        Unnamed: 0                                               Text  \\\n",
              "0                0  \\nخبرنامه دانشگاه علم صنعت ايران \\nشماره يازده...   \n",
              "1                1  \\nتا پايان سال 1378 دهها زمين فوتبال \\nسالن ور...   \n",
              "2                2  \\nانجمن توليدكنندگان تجهيزات صنعت نفت تشكيل \\n...   \n",
              "3                3  \\nكرتين براي سومين وزير كانادا \\nشد \\nژان كرتي...   \n",
              "4                4  \\nخداحافظ رفقا \\nنمايندگان اروپاي شرقي جام 200...   \n",
              "...            ...                                                ...   \n",
              "150091      150091  \\nمشكل سدسازي \\nوزير نيرو: \\nدر كشور كمبود اعت...   \n",
              "150092      150092  \\nخودسوزي يك هوادار پ ك ك شهر دياربكر \\nتركيه ...   \n",
              "150093      150093  \\nاز نيمه سال جاري 600 دستگاه \\nاتوبوس كارت بل...   \n",
              "150094      150094  \\nدر كتابخانه مركز دانشگاه \\nتهران دانشكده هنر...   \n",
              "150095      150095  \\nپيام رئيس جمهوري سران \\nكشورهاي عضو سازمان \\...   \n",
              "\n",
              "                   Category  \n",
              "0       Science and Culture  \n",
              "1                     Sport  \n",
              "2                   Economy  \n",
              "3             Miscellaneous  \n",
              "4                     Sport  \n",
              "...                     ...  \n",
              "150091        Miscellaneous  \n",
              "150092        Miscellaneous  \n",
              "150093        Miscellaneous  \n",
              "150094  Science and Culture  \n",
              "150095             Politics  \n",
              "\n",
              "[150096 rows x 3 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe['Text'] = dataframe['Text'].apply(remove_stopwords)\n",
        "dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vzVs1idYme5r",
        "outputId": "1800b6cc-ff76-4138-debc-aafae4770c32"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!\"#$%&\\'()*+-./:;<=>?@\\\\^_`{|}~؟__-ء،ًٌٍَُِّْأإ؟'"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# first = remove punctuation\n",
        "punctuation = '!\"#$%&\\'()*+-./:;<=>?@\\\\^_`{|}~؟__-ء،ًٌٍَُِّْأإ؟'\n",
        "punctuation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "1C_9AVxbmfnr"
      },
      "outputs": [],
      "source": [
        "def remove_punctuation(text):\n",
        "    return ''.join([char for char in text if char not in punctuation ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "TXjlL9RimjGc"
      },
      "outputs": [],
      "source": [
        "dataframe['Text'] = dataframe['Text'].apply(remove_punctuation)\n",
        "# for text in x_train['Text']:\n",
        "#   words = [word for word in text.split() if word.lower() not in punctuation]\n",
        "#   text = \"\".join(words)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "qaH0QdbvmlNk",
        "outputId": "8ada07f3-b1da-4e75-dc1e-3fd371144a6a"
      },
      "outputs": [
        {
          "data": {
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              "    <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>Unnamed: 0</th>\n",
              "      <th>Text</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>\\nخبرنامه دانشگاه علم صنعت ايران \\nشماره يازده...</td>\n",
              "      <td>Science and Culture</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>\\nتا پايان سال 1378 دهها زمين فوتبال \\nسالن ور...</td>\n",
              "      <td>Sport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>\\nانجمن توليدكنندگان تجهيزات صنعت نفت تشكيل \\n...</td>\n",
              "      <td>Economy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>\\nكرتين براي سومين وزير كانادا \\nشد \\nژان كرتي...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>\\nخداحافظ رفقا \\nنمايندگان اروپاي شرقي جام 200...</td>\n",
              "      <td>Sport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>\\nبراي مقابله فرسايش شديد خاك \\nبزرگترين جزيره...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>\\nنيمي مردم جهان تحرك كافي \\nهشدار سازمان بهدا...</td>\n",
              "      <td>Sport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>\\nزني پسوند پيشوند \\nجايگاه زن سينماي ايران وگ...</td>\n",
              "      <td>Social</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>\\nسن بلوغ ايران چيست \\n \\nبه بهانه همايش مسايل...</td>\n",
              "      <td>Social</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>\\nرهبران پشتون تاجيك افغانستان \\nانتظار تصميم ...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16451849-386f-4a9e-8914-63ea00978734')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-16451849-386f-4a9e-8914-63ea00978734 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-16451849-386f-4a9e-8914-63ea00978734');\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",
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              "\n",
              "\n",
              "<div id=\"df-e428c357-5293-46d2-a6b3-896cf62fae00\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e428c357-5293-46d2-a6b3-896cf62fae00')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e428c357-5293-46d2-a6b3-896cf62fae00 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Unnamed: 0                                               Text  \\\n",
              "0           0  \\nخبرنامه دانشگاه علم صنعت ايران \\nشماره يازده...   \n",
              "1           1  \\nتا پايان سال 1378 دهها زمين فوتبال \\nسالن ور...   \n",
              "2           2  \\nانجمن توليدكنندگان تجهيزات صنعت نفت تشكيل \\n...   \n",
              "3           3  \\nكرتين براي سومين وزير كانادا \\nشد \\nژان كرتي...   \n",
              "4           4  \\nخداحافظ رفقا \\nنمايندگان اروپاي شرقي جام 200...   \n",
              "5           5  \\nبراي مقابله فرسايش شديد خاك \\nبزرگترين جزيره...   \n",
              "6           6  \\nنيمي مردم جهان تحرك كافي \\nهشدار سازمان بهدا...   \n",
              "7           7  \\nزني پسوند پيشوند \\nجايگاه زن سينماي ايران وگ...   \n",
              "8           8  \\nسن بلوغ ايران چيست \\n \\nبه بهانه همايش مسايل...   \n",
              "9           9  \\nرهبران پشتون تاجيك افغانستان \\nانتظار تصميم ...   \n",
              "\n",
              "              Category  \n",
              "0  Science and Culture  \n",
              "1                Sport  \n",
              "2              Economy  \n",
              "3        Miscellaneous  \n",
              "4                Sport  \n",
              "5        Miscellaneous  \n",
              "6                Sport  \n",
              "7               Social  \n",
              "8               Social  \n",
              "9        Miscellaneous  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHpM0-juVstM"
      },
      "source": [
        "Normalizing \\\n",
        "When we normalize text, we attempt to reduce its randomness, bringing it closer to a predefined “standard”. This helps us to reduce the amount of different information that the computer has to deal with, and therefore improves efficiency. The goal of normalization techniques like stemming and lemmatization is to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "j9iJl6TImzMz"
      },
      "outputs": [],
      "source": [
        "normalizer = Normalizer()\n",
        "lemmatizer = Lemmatizer()\n",
        "\n",
        "dataframe['Text'] = dataframe['Text'].apply(normalizer.normalize)\n",
        "dataframe['Text'] = dataframe['Text'].apply(lemmatizer.lemmatize)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "kXRsaAqEmzxU"
      },
      "outputs": [],
      "source": [
        "# dataframe = dataframe.drop(['Tokenized_Text'] , axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xjo2cFoS6U9"
      },
      "source": [
        "# Train , Test and Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0HrS050tm3Gl",
        "outputId": "d0b58354-d81a-4a83-fc6a-b7271f6432e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Miscellaneous          68733\n",
              "Economy                21346\n",
              "Politics               17803\n",
              "Sport                  14328\n",
              "Science and Culture    11959\n",
              "Social                  8785\n",
              "Literature and Art      5949\n",
              "Natural Environment      851\n",
              "Tourism                  342\n",
              "Name: Category, dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe['Category'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "v3KfwhEPm8gs"
      },
      "outputs": [],
      "source": [
        "x= dataframe.iloc[:,0:2]\n",
        "y= dataframe.iloc[:,2:3]\n",
        "X_train, X_val, y_train, y_val = train_test_split(x, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "ZWAlzai9s4dR",
        "outputId": "2a4ab117-b82c-4269-b067-02f3c55888a3"
      },
      "outputs": [
        {
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              "      <td>14473</td>\n",
              "      <td>\\nيادداشت \\nسخن سنجيده طهماسب مظاهري راجدي بگي...</td>\n",
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              "      <td>60813</td>\n",
              "      <td>\\nهشت تيم خارجي دووميداني \\n سرويس ورزشي تيم ه...</td>\n",
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              "      <th>121958</th>\n",
              "      <td>121958</td>\n",
              "      <td>\\n طرح 1100 عمراني صنعتي امسال استان \\nكرمانشا...</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        Unnamed: 0                                               Text\n",
              "5864          5864  \\nبيماريهاي قلبي عروقي نخستين مرگ \\nو مير كشور...\n",
              "47463        47463  \\nاجراي طرح ملي جنگلكاري دامنه جنوبي \\nالبرز \\...\n",
              "14473        14473  \\nيادداشت \\nسخن سنجيده طهماسب مظاهري راجدي بگي...\n",
              "60813        60813  \\nهشت تيم خارجي دووميداني \\n سرويس ورزشي تيم ه...\n",
              "146598      146598  \\nاجراي طرح زيست محيطي منطقه 22 تهران \\nايرنا ...\n",
              "...            ...                                                ...\n",
              "119879      119879  \\nتحقيق استيضاح \\nبيل كلينتون مجلس نمايندگان \\...\n",
              "103694      103694  \\nارزشمند كودكان بياموزيم \\nموضوع اين جمله معر...\n",
              "131932      131932  \\nفهرستي براي حمله شصت كشور \\nآمريكاي شمالي \\n...\n",
              "146867      146867  \\nاتهام تازه اسرائيل عليه سوريه \\nاسرائيل جريا...\n",
              "121958      121958  \\n طرح 1100 عمراني صنعتي امسال استان \\nكرمانشا...\n",
              "\n",
              "[120076 rows x 2 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train"
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    },
    {
      "cell_type": "code",
      "execution_count": 21,
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        "id": "lCz_o8Xzwwp-",
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      "outputs": [
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              "    [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",
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              "\n",
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              "        document.querySelector('#df-7241a6f8-6efe-44a6-8fc6-b987c47f8d06 button.colab-df-convert');\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f734bfee-30e5-4a9e-bd15-e0b7489fc8a3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f734bfee-30e5-4a9e-bd15-e0b7489fc8a3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f734bfee-30e5-4a9e-bd15-e0b7489fc8a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             Category\n",
              "5864    Miscellaneous\n",
              "47463   Miscellaneous\n",
              "14473         Economy\n",
              "60813           Sport\n",
              "146598  Miscellaneous\n",
              "...               ...\n",
              "119879  Miscellaneous\n",
              "103694         Social\n",
              "131932       Politics\n",
              "146867  Miscellaneous\n",
              "121958  Miscellaneous\n",
              "\n",
              "[120076 rows x 1 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "JHzqcsh0sbfB",
        "outputId": "67e8747f-1ccf-4511-92b7-49e9e4cd1f72"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Text</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5864</th>\n",
              "      <td>5864</td>\n",
              "      <td>\\nبيماريهاي قلبي عروقي نخستين مرگ \\nو مير كشور...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47463</th>\n",
              "      <td>47463</td>\n",
              "      <td>\\nاجراي طرح ملي جنگلكاري دامنه جنوبي \\nالبرز \\...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14473</th>\n",
              "      <td>14473</td>\n",
              "      <td>\\nيادداشت \\nسخن سنجيده طهماسب مظاهري راجدي بگي...</td>\n",
              "      <td>Economy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60813</th>\n",
              "      <td>60813</td>\n",
              "      <td>\\nهشت تيم خارجي دووميداني \\n سرويس ورزشي تيم ه...</td>\n",
              "      <td>Sport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146598</th>\n",
              "      <td>146598</td>\n",
              "      <td>\\nاجراي طرح زيست محيطي منطقه 22 تهران \\nايرنا ...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119879</th>\n",
              "      <td>119879</td>\n",
              "      <td>\\nتحقيق استيضاح \\nبيل كلينتون مجلس نمايندگان \\...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103694</th>\n",
              "      <td>103694</td>\n",
              "      <td>\\nارزشمند كودكان بياموزيم \\nموضوع اين جمله معر...</td>\n",
              "      <td>Social</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131932</th>\n",
              "      <td>131932</td>\n",
              "      <td>\\nفهرستي براي حمله شصت كشور \\nآمريكاي شمالي \\n...</td>\n",
              "      <td>Politics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146867</th>\n",
              "      <td>146867</td>\n",
              "      <td>\\nاتهام تازه اسرائيل عليه سوريه \\nاسرائيل جريا...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121958</th>\n",
              "      <td>121958</td>\n",
              "      <td>\\n طرح 1100 عمراني صنعتي امسال استان \\nكرمانشا...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120076 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79fab1d0-702c-49f3-8f21-2de27207a993')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-79fab1d0-702c-49f3-8f21-2de27207a993 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-79fab1d0-702c-49f3-8f21-2de27207a993');\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",
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              "\n",
              "\n",
              "<div id=\"df-4347747f-4422-4997-a394-ae3d597ca524\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4347747f-4422-4997-a394-ae3d597ca524')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4347747f-4422-4997-a394-ae3d597ca524 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        Unnamed: 0                                               Text  \\\n",
              "5864          5864  \\nبيماريهاي قلبي عروقي نخستين مرگ \\nو مير كشور...   \n",
              "47463        47463  \\nاجراي طرح ملي جنگلكاري دامنه جنوبي \\nالبرز \\...   \n",
              "14473        14473  \\nيادداشت \\nسخن سنجيده طهماسب مظاهري راجدي بگي...   \n",
              "60813        60813  \\nهشت تيم خارجي دووميداني \\n سرويس ورزشي تيم ه...   \n",
              "146598      146598  \\nاجراي طرح زيست محيطي منطقه 22 تهران \\nايرنا ...   \n",
              "...            ...                                                ...   \n",
              "119879      119879  \\nتحقيق استيضاح \\nبيل كلينتون مجلس نمايندگان \\...   \n",
              "103694      103694  \\nارزشمند كودكان بياموزيم \\nموضوع اين جمله معر...   \n",
              "131932      131932  \\nفهرستي براي حمله شصت كشور \\nآمريكاي شمالي \\n...   \n",
              "146867      146867  \\nاتهام تازه اسرائيل عليه سوريه \\nاسرائيل جريا...   \n",
              "121958      121958  \\n طرح 1100 عمراني صنعتي امسال استان \\nكرمانشا...   \n",
              "\n",
              "             Category  \n",
              "5864    Miscellaneous  \n",
              "47463   Miscellaneous  \n",
              "14473         Economy  \n",
              "60813           Sport  \n",
              "146598  Miscellaneous  \n",
              "...               ...  \n",
              "119879  Miscellaneous  \n",
              "103694         Social  \n",
              "131932       Politics  \n",
              "146867  Miscellaneous  \n",
              "121958  Miscellaneous  \n",
              "\n",
              "[120076 rows x 3 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "balanced_train = pd.concat([X_train , y_train], axis=1)\n",
        "balanced_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zn0f5eOVxToV",
        "outputId": "1f6906db-0b06-4da1-bb12-e0150818e596"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Miscellaneous          54950\n",
              "Economy                17125\n",
              "Politics               14247\n",
              "Sport                  11392\n",
              "Science and Culture     9566\n",
              "Social                  7080\n",
              "Literature and Art      4759\n",
              "Natural Environment      684\n",
              "Tourism                  273\n",
              "Name: Category, dtype: int64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "balanced_train['Category'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NaC7k9K1ohAZ"
      },
      "source": [
        "we choose 8000 values of 4 most populated class and all instances of other classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Jpo0OyQAofxG"
      },
      "outputs": [],
      "source": [
        "class1 = balanced_train[balanced_train['Category'] == 'Miscellaneous']\n",
        "class1 = class1.sample(8000, replace=False)\n",
        "class2 = balanced_train[balanced_train['Category'] == 'Economy']\n",
        "class2 = class2.sample(8000, replace=False)\n",
        "class3 = balanced_train[balanced_train['Category'] == 'Politics']\n",
        "class3 = class3.sample(8000, replace=False)\n",
        "class4 = balanced_train[balanced_train['Category'] == 'Sport']\n",
        "class4 = class4.sample(8000, replace=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "yWkPJCANqZy9"
      },
      "outputs": [],
      "source": [
        "class5 = balanced_train[balanced_train['Category'] == 'Science and Culture']\n",
        "class5 = class5.sample(8000,replace = False)\n",
        "class6 = balanced_train[balanced_train['Category'] == 'Social']\n",
        "class6 = class6.sample(len(class6),replace = False)\n",
        "class7 = balanced_train[balanced_train['Category'] == 'Literature and Art']\n",
        "class7 = class7.sample(len(class7),replace = False)\n",
        "class8 = balanced_train[balanced_train['Category'] == 'Natural Environment']\n",
        "class8 = class8.sample(len(class8),replace = False)\n",
        "class9 = balanced_train[balanced_train['Category'] == 'Tourism']\n",
        "class9 = class9.sample(len(class9),replace = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "RDMOCE07rX_5"
      },
      "outputs": [],
      "source": [
        "balanced_data = pd.concat([class1, class2, class3, class4, class5, class6, class7, class8, class9], axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7UWrWumOsFXO",
        "outputId": "2f3a5303-0ab5-460d-a205-cbe71d9ac7ce"
      },
      "outputs": [
        {
          "data": {
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Unnamed: 0</th>\n",
              "      <th>Text</th>\n",
              "      <th>Category</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10112</th>\n",
              "      <td>10112</td>\n",
              "      <td>\\nبا مردم \\nضرورت اطلاع رساني \\nهشدار وزير محت...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114406</th>\n",
              "      <td>114406</td>\n",
              "      <td>\\nهي بي \\nنام علمي speciosa hibe\\nخانواده scro...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57738</th>\n",
              "      <td>57738</td>\n",
              "      <td>\\nمناره بازار گسكر ملي \\nمناره بازار گسكر دوره...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87433</th>\n",
              "      <td>87433</td>\n",
              "      <td>\\nكلاسهاي آموزشي دوره تابستان خانه \\nفرهنگ شمش...</td>\n",
              "      <td>Miscellaneous</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127894</th>\n",
              "      <td>127894</td>\n",
              "      <td>\\nاصلاح جاده خاوران ترين ضرورت اين \\nايام \\nسا...</td>\n",
              "      <td>Miscellaneous</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15361</th>\n",
              "      <td>15361</td>\n",
              "      <td>\\nرئيس سازمان ايرانگردي وجهانگردي اعلام كرد \\n...</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50310</th>\n",
              "      <td>50310</td>\n",
              "      <td>\\nنمايشگاه  طبيعت نشاط زندگي  ايلام \\nگشايش يا...</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58490</th>\n",
              "      <td>58490</td>\n",
              "      <td>\\nمدير طرح گسترش فضاي گردشگري غار عليصدر \\nاعل...</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5457</th>\n",
              "      <td>5457</td>\n",
              "      <td>\\nمعاون نظارت سازمان ايرانگردي وجهانگردي \\nهمه...</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96464</th>\n",
              "      <td>96464</td>\n",
              "      <td>\\nآستانه حضرت عبدالعظيم ع \\nآستانه مبارك حضرت ...</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>52796 rows × 3 columns</p>\n",
              "</div>\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
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              "\n",
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              "        document.querySelector('#df-1dafeb44-2be9-4bfc-9213-7167b6774cc4 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-1dafeb44-2be9-4bfc-9213-7167b6774cc4');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-48b82a78-2e8c-4e40-8dec-5b14f0a9efe5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-48b82a78-2e8c-4e40-8dec-5b14f0a9efe5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        Unnamed: 0                                               Text  \\\n",
              "10112        10112  \\nبا مردم \\nضرورت اطلاع رساني \\nهشدار وزير محت...   \n",
              "114406      114406  \\nهي بي \\nنام علمي speciosa hibe\\nخانواده scro...   \n",
              "57738        57738  \\nمناره بازار گسكر ملي \\nمناره بازار گسكر دوره...   \n",
              "87433        87433  \\nكلاسهاي آموزشي دوره تابستان خانه \\nفرهنگ شمش...   \n",
              "127894      127894  \\nاصلاح جاده خاوران ترين ضرورت اين \\nايام \\nسا...   \n",
              "...            ...                                                ...   \n",
              "15361        15361  \\nرئيس سازمان ايرانگردي وجهانگردي اعلام كرد \\n...   \n",
              "50310        50310  \\nنمايشگاه  طبيعت نشاط زندگي  ايلام \\nگشايش يا...   \n",
              "58490        58490  \\nمدير طرح گسترش فضاي گردشگري غار عليصدر \\nاعل...   \n",
              "5457          5457  \\nمعاون نظارت سازمان ايرانگردي وجهانگردي \\nهمه...   \n",
              "96464        96464  \\nآستانه حضرت عبدالعظيم ع \\nآستانه مبارك حضرت ...   \n",
              "\n",
              "             Category  \n",
              "10112   Miscellaneous  \n",
              "114406  Miscellaneous  \n",
              "57738   Miscellaneous  \n",
              "87433   Miscellaneous  \n",
              "127894  Miscellaneous  \n",
              "...               ...  \n",
              "15361         Tourism  \n",
              "50310         Tourism  \n",
              "58490         Tourism  \n",
              "5457          Tourism  \n",
              "96464         Tourism  \n",
              "\n",
              "[52796 rows x 3 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "balanced_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W5ebs_3z3ONQ"
      },
      "source": [
        "# free Unnecessary Space"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Y7uhKJykshPi"
      },
      "outputs": [],
      "source": [
        "del dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Qo0EQYOM16fd"
      },
      "outputs": [],
      "source": [
        "del X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "0Wnca6g019uI"
      },
      "outputs": [],
      "source": [
        "del y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "M4wNlqfa3Jvi"
      },
      "outputs": [],
      "source": [
        "del x,y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "UALonyg12qiW"
      },
      "outputs": [],
      "source": [
        "del class1,class2,class3,class4,class5,class6,class7,class8,class9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "3xKk0p5UE-3-"
      },
      "outputs": [],
      "source": [
        "del balanced_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_e5NjTHUSam0"
      },
      "source": [
        "# Tokenization and Feature Extraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "4Ze2YfqFm-fB"
      },
      "outputs": [],
      "source": [
        "# import required module\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from scipy.sparse import hstack\n",
        "\n",
        "text = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))\n",
        "number_of_features = text.fit_transform(balanced_train.loc[:,'Text'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "z8MSr49rm9TQ"
      },
      "outputs": [],
      "source": [
        "categories = pd.get_dummies(balanced_train['Category']).values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTxuDex0gFUD"
      },
      "source": [
        "in order to avoid errors , we need to first convert the 'Text' column from a csr_matrix object back to a list of strings using toarray() method and then apply the TFIDF function to each string"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87oLRStd0fWQ"
      },
      "source": [
        "# ANN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "DqYtR53n3FCg"
      },
      "outputs": [],
      "source": [
        "del balanced_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucSXzdlO0Pyh",
        "outputId": "7ab5ad17-edeb-4468-d91a-29503742e458"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"Negar's ANN\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input (Dense)               (None, 25)                75025     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 25)                0         \n",
            "                                                                 \n",
            " layer1 (Dense)              (None, 16)                416       \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 16)                0         \n",
            "                                                                 \n",
            " output (Dense)              (None, 9)                 153       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75594 (295.29 KB)\n",
            "Trainable params: 75594 (295.29 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# using tensorflow's keras\n",
        "model = Sequential(\n",
        "    [\n",
        "        Dense(units=25, input_dim=number_of_features.toarray().shape[-1], activation=\"relu\", name=\"input\"),\n",
        "        # Dense(units=64, activation=\"relu\", name=\"layer1\"),\n",
        "        Dropout(0.4),\n",
        "        Dense(units=16, activation=\"relu\", name=\"layer1\"),\n",
        "        # Dense(units=64, activation=\"relu\", name=\"layer3\"),\n",
        "        Dropout(0.2),\n",
        "        Dense(units=9, activation=\"softmax\", name=\"output\")\n",
        "    ],\n",
        "    name=\"negar_model\"\n",
        ")\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "q2JvMXHj0qkU",
        "outputId": "faa47a22-b232-4fca-8761-20d18a93ac9e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "utils.plot_model(model, show_shapes=True, show_layer_names=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "NJ6O6AhYCTnp"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2LXxJR7sCUoZ"
      },
      "outputs": [],
      "source": [
        "history = model.fit(number_of_features.toarray(),categories , validation_data=(X_val, y_val), batch_size=64, epochs=10, verbose=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkxVKhJzDcdt"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glH3P_ebD6fn"
      },
      "source": [
        "Cross-Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k-RuugcrDfUk"
      },
      "outputs": [],
      "source": [
        "f = plt.figure(figsize=(10,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['accuracy'],\n",
        "         label='train accuracy',\n",
        "         c='orange', ls='-')\n",
        "plt.plot(history.history['val_accuracy'],\n",
        "         label='val accuracy',\n",
        "         c='dodgerblue', ls='-')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'],\n",
        "         label='train loss',\n",
        "         c='orange', ls='--')\n",
        "plt.plot(history.history['val_loss'],\n",
        "         label='val loss',\n",
        "         c='dodgerblue', ls='--')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oh8KZCSoHXQS"
      },
      "source": [
        "Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "dYdpGs-XHafM",
        "outputId": "5e8ed731-c77c-4cac-c28f-cbcea225d13e"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics\n",
        "prediction = model.predict(X_val)\n",
        "confusion_matrix = metrics.confusion_matrix(y_val, prediction)\n",
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "cm_display.plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WB2OLpDiIco0"
      },
      "outputs": [],
      "source": [
        "def kfold_indices(data, k):\n",
        "    fold_size = len(data) // k\n",
        "    indices = np.arange(len(data))\n",
        "    folds = []\n",
        "    for i in range(k):\n",
        "        test_indices = indices[i * fold_size: (i + 1) * fold_size]\n",
        "        train_indices = np.concatenate([indices[:i * fold_size], indices[(i + 1) * fold_size:]])\n",
        "        folds.append((train_indices, test_indices))\n",
        "    return folds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qN9hAU4QWV9W"
      },
      "outputs": [],
      "source": [
        "# Define the number of folds (K)\n",
        "k = 10\n",
        "\n",
        "# Get the fold indices\n",
        "fold_indices = kfold_indices(number_of_features.toarray(), k)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bk7EAB5QaRJ4"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "# Initialize a list to store the evaluation scores\n",
        "scores = []\n",
        "\n",
        "# Iterate through each fold\n",
        "for train_indices, test_indices in fold_indices:\n",
        "    X_train, y_train = number_of_features.toarray()[train_indices], categories[train_indices]\n",
        "    X_test, y_test = number_of_features.toarray()[test_indices], categories[test_indices]\n",
        "\n",
        "    # Train the model on the training data\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Calculate the accuracy score for this fold\n",
        "    fold_score = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    # Append the fold score to the list of scores\n",
        "    scores.append(fold_score)\n",
        "\n",
        "# Calculate the mean accuracy across all folds\n",
        "mean_accuracy = np.mean(scores)\n",
        "print(\"K-Fold Cross-Validation Scores:\", scores)\n",
        "print(\"Mean Accuracy:\", mean_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZqcbZpxfvXS"
      },
      "source": [
        "# Building a user Interface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "gSpFdNz4f3GL"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: exceptiongroup>=1.0.2 in d:\\python\\python39\\lib\\site-packages (from anyio->httpx->gradio) (1.1.1)\n",
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          ]
        }
      ],
      "source": [
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2K4sL3irgUXG"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "d:\\Python\\Python39\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running on local URL:  http://127.0.0.1:7860\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": []
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "IMPORTANT: You are using gradio version 4.16.0, however version 4.29.0 is available, please upgrade.\n",
            "--------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Exception in thread Thread-7:\n",
            "Traceback (most recent call last):\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_exceptions.py\", line 10, in map_exceptions\n",
            "    yield\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_backends\\sync.py\", line 206, in connect_tcp\n",
            "    sock = socket.create_connection(\n",
            "  File \"d:\\Python\\Python39\\lib\\socket.py\", line 844, in create_connection\n",
            "    raise err\n",
            "  File \"d:\\Python\\Python39\\lib\\socket.py\", line 832, in create_connection\n",
            "    sock.connect(sa)\n",
            "socket.timeout: timed out\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 67, in map_httpcore_exceptions\n",
            "    yield\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 231, in handle_request\n",
            "    resp = self._pool.handle_request(req)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py\", line 268, in handle_request\n",
            "    raise exc\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py\", line 251, in handle_request\n",
            "    response = connection.handle_request(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection.py\", line 99, in handle_request\n",
            "    raise exc\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection.py\", line 76, in handle_request\n",
            "    stream = self._connect(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection.py\", line 124, in _connect\n",
            "    stream = self._network_backend.connect_tcp(**kwargs)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_backends\\sync.py\", line 213, in connect_tcp\n",
            "    sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)\n",
            "  File \"d:\\Python\\Python39\\lib\\contextlib.py\", line 137, in __exit__\n",
            "    self.gen.throw(typ, value, traceback)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_exceptions.py\", line 14, in map_exceptions\n",
            "    raise to_exc(exc) from exc\n",
            "httpcore.ConnectTimeout: timed out\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"d:\\Python\\Python39\\lib\\threading.py\", line 973, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python39\\site-packages\\ipykernel\\ipkernel.py\", line 761, in run_closure\n",
            "    _threading_Thread_run(self)\n",
            "  File \"d:\\Python\\Python39\\lib\\threading.py\", line 910, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\gradio\\analytics.py\", line 61, in _do_normal_analytics_request\n",
            "    data[\"ip_address\"] = get_local_ip_address()\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\gradio\\analytics.py\", line 117, in get_local_ip_address\n",
            "    ip_address = httpx.get(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_api.py\", line 196, in get\n",
            "    return request(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_api.py\", line 104, in request\n",
            "    return client.request(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 828, in request\n",
            "    return self.send(request, auth=auth, follow_redirects=follow_redirects)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 915, in send\n",
            "    response = self._send_handling_auth(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 943, in _send_handling_auth\n",
            "    response = self._send_handling_redirects(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 980, in _send_handling_redirects\n",
            "    response = self._send_single_request(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 1016, in _send_single_request\n",
            "    response = transport.handle_request(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 231, in handle_request\n",
            "    resp = self._pool.handle_request(req)\n",
            "  File \"d:\\Python\\Python39\\lib\\contextlib.py\", line 137, in __exit__\n",
            "    self.gen.throw(typ, value, traceback)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 84, in map_httpcore_exceptions\n",
            "    raise mapped_exc(message) from exc\n",
            "httpx.ConnectTimeout: timed out\n",
            "Exception in thread Thread-9:\n",
            "Traceback (most recent call last):\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_exceptions.py\", line 10, in map_exceptions\n",
            "    yield\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_backends\\sync.py\", line 206, in connect_tcp\n",
            "    sock = socket.create_connection(\n",
            "  File \"d:\\Python\\Python39\\lib\\socket.py\", line 844, in create_connection\n",
            "    raise err\n",
            "  File \"d:\\Python\\Python39\\lib\\socket.py\", line 832, in create_connection\n",
            "    sock.connect(sa)\n",
            "socket.timeout: timed out\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 67, in map_httpcore_exceptions\n",
            "    yield\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 231, in handle_request\n",
            "    resp = self._pool.handle_request(req)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py\", line 268, in handle_request\n",
            "    raise exc\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py\", line 251, in handle_request\n",
            "    response = connection.handle_request(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection.py\", line 99, in handle_request\n",
            "    raise exc\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection.py\", line 76, in handle_request\n",
            "    stream = self._connect(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_sync\\connection.py\", line 124, in _connect\n",
            "    stream = self._network_backend.connect_tcp(**kwargs)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_backends\\sync.py\", line 213, in connect_tcp\n",
            "    sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)\n",
            "  File \"d:\\Python\\Python39\\lib\\contextlib.py\", line 137, in __exit__\n",
            "    self.gen.throw(typ, value, traceback)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpcore\\_exceptions.py\", line 14, in map_exceptions\n",
            "    raise to_exc(exc) from exc\n",
            "httpcore.ConnectTimeout: timed out\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"d:\\Python\\Python39\\lib\\threading.py\", line 973, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python39\\site-packages\\ipykernel\\ipkernel.py\", line 761, in run_closure\n",
            "    _threading_Thread_run(self)\n",
            "  File \"d:\\Python\\Python39\\lib\\threading.py\", line 910, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\gradio\\analytics.py\", line 61, in _do_normal_analytics_request\n",
            "    data[\"ip_address\"] = get_local_ip_address()\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\gradio\\analytics.py\", line 117, in get_local_ip_address\n",
            "    ip_address = httpx.get(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_api.py\", line 196, in get\n",
            "    return request(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_api.py\", line 104, in request\n",
            "    return client.request(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 828, in request\n",
            "    return self.send(request, auth=auth, follow_redirects=follow_redirects)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 915, in send\n",
            "    response = self._send_handling_auth(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 943, in _send_handling_auth\n",
            "    response = self._send_handling_redirects(\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 980, in _send_handling_redirects\n",
            "    response = self._send_single_request(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_client.py\", line 1016, in _send_single_request\n",
            "    response = transport.handle_request(request)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 231, in handle_request\n",
            "    resp = self._pool.handle_request(req)\n",
            "  File \"d:\\Python\\Python39\\lib\\contextlib.py\", line 137, in __exit__\n",
            "    self.gen.throw(typ, value, traceback)\n",
            "  File \"d:\\Python\\Python39\\lib\\site-packages\\httpx\\_transports\\default.py\", line 84, in map_httpcore_exceptions\n",
            "    raise mapped_exc(message) from exc\n",
            "httpx.ConnectTimeout: timed out\n"
          ]
        }
      ],
      "source": [
        "import gradio as gr\n",
        "\n",
        "def greet(name, intensity):\n",
        "    return \"Hello , Tell Me Your News And I will Tell you it's Category !\"\n",
        "\n",
        "demo = gr.Interface(\n",
        "    fn=greet,\n",
        "    inputs=[\"text\", \"text\"],\n",
        "    outputs=[\"text\"],\n",
        ")\n",
        "\n",
        "demo.launch()\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "_ZP5vk-eqLNH"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}