text-classification-for-category-prediction.ipynb

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    "# <p><center style=\"font-family:newtimeroman;font-size:180%;\"> Text Classification for Category Prediction using SVM, RandomForest, and XGBoost classifier: A Case Study with Yektanet Dataset </center></p>\n",
    "### Table of contents:\n",
    "\n",
    "* [Introduction](#1)\n",
    "* [Import Libraries](#2)\n",
    "* [Import Dataset](#3)\n",
    "* [Preprocessing & Feature Engineering](#4)\n",
    "* [Train Some Models](#5)\n",
    "* [End](#6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d44305",
   "metadata": {},
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    "<a id=\"1\"></a>\n",
    "# <p style=\"background-image: url(https://i.postimg.cc/K87ByXmr/stage5.jpg);font-family:camtasia;font-size:120%;color:white;text-align:center;border-radius:15px 50px; padding:7px\"> Introduction</p>"
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    "<html>\n",
    "<head>\n",
    "</head>\n",
    "<body>\n",
    "  <h1>Text Classification for Category Prediction using SVM: A Case Study with Yektanet Dataset</h1>\n",
    "  <p>Welcome to an industrial exercise in the application of machine learning in Natural Language Processing (NLP). In this exercise, we have access to real Persian web data that has been refined and collected by the Yektanet platform. The goal of the exercise is to build a machine-learning model that can predict the categorical topic of a document based on the available text in a link, such as the title, description, complete text content, etc. </p>\n",
    "</body>\n",
    "</html>"
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   "source": [
    "<h2>\n",
    "<font>\n",
    "DataSet\n",
    "</font>\n",
    "</h2>\n",
    "<p>\n",
    "<font>\n",
    "Each instance in this dataset is accompanied by the features described in the table below. The \"category\" column serves as the target variable of the problem, indicating the topic of the content.</font>\n",
    "</p>\n",
    "<center>\n",
    "<div>\n",
    "<font>\n",
    "    \n",
    "|column|Description|\n",
    "|:------:|:---:|\n",
    "|<code>category</code>| Target |\n",
    "|<code>description</code>| Description |\n",
    "|<code>text_content</code>| text content |\n",
    "|<code>title</code>| Title |\n",
    "|<code>h1</code>| Tag content (h1) |\n",
    "|<code>h2</code>|Tag content (h2) |\n",
    "|<code>url</code>| Link address|\n",
    "|<code>domain</code>|Website domain |\n",
    "|<code>id</code>|link id|\n",
    "\n",
    "</font>\n",
    "</div>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675a6aa4",
   "metadata": {},
   "source": [
    "<a id=\"2\"></a>\n",
    "# <p style=\"background-image: url(https://i.postimg.cc/K87ByXmr/stage5.jpg);font-family:camtasia;font-size:120%;color:white;text-align:center;border-radius:15px 50px; padding:7px\">Import Libraries </p>"
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  {
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   "execution_count": 42,
   "id": "1c06ccba",
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   "outputs": [],
   "source": [
    "# import the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import plotly.graph_objects as go\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import nltk\n",
    "import string\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import re\n",
    "import hazm\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cabbe59d",
   "metadata": {},
   "source": [
    "<a id=\"3\"></a>\n",
    "# <p style=\"background-image: url(https://i.postimg.cc/K87ByXmr/stage5.jpg);font-family:camtasia;font-size:120%;color:white;text-align:center;border-radius:15px 50px; padding:7px\">Import Dataset </p>\n",
    "<a class=\"btn\" href=\"#home\">Tabel of Contents</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3847203d",
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       "      <td>معنی از شوبنده ها | جدول یاب از شوبنده ها 381</td>\n",
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       "      <td>jadvalyab.ir/search?q=%D8%A7%D8%B2+%D8%B4%D9%8...</td>\n",
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       "      <td>نرم افزار Geph برای اندروید یک پلت‌فرم چندسکوی...</td>\n",
       "      <td>دانلود تحریم‌گذر Geph برای اندروید خانه/اندروی...</td>\n",
       "      <td>دانلود تحریم‌گذر Geph برای اندروید</td>\n",
       "      <td>دانلود تحریم‌گذر Geph برای اندروید</td>\n",
       "      <td>دانلود نرم افزار Geph</td>\n",
       "      <td>palexe.site/dl/geph-android/</td>\n",
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       "      <td>راه‌اندازی تلویزیون همگام‌سازی کنترل اتصال به ...</td>\n",
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       "              category                                        description  \\\n",
       "0        کتاب و ادبیات  از شوبنده ها: جستجو معنی \"از شوبنده ها\" در فره...   \n",
       "1       تجارت و اقتصاد  بیت‌کوین کش یک ارز مجازی مشهور است و بیت‌کوین ...   \n",
       "2                سلامت  نوبت دهی دکتر مهناز عابدینی متخصص رادیولوژی و ...   \n",
       "3  تکنولوژی و کامپبوتر  نرم افزار Geph برای اندروید یک پلت‌فرم چندسکوی...   \n",
       "4  تکنولوژی و کامپبوتر  سری جدید تلویزیون‌های هوشمند سامسونگ که با نام...   \n",
       "\n",
       "                                        text_content  \\\n",
       "0      معنی از شوبنده ها | جدول یاب از شوبنده ها 381   \n",
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       "4  ترفندهای پرکاربرد تلویزیون‌‌های هوشمند سامسونگ...   \n",
       "\n",
       "                                               title  \\\n",
       "0                       معنی از شوبنده ها | جدول یاب   \n",
       "1                       عکس بیت‌کوین کش برای پروفایل   \n",
       "2  دکتر مهناز عابدینی متخصص رادیولوژی و سونوگرافی...   \n",
       "3                 دانلود تحریم‌گذر Geph برای اندروید   \n",
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       "                                               h1  \\\n",
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       "\n",
       "                                                  h2  \\\n",
       "0                         از شوبنده ها در معادل ابجد   \n",
       "1  عکس بیت کوین با کیفیت 4K عکس ارزهای دیجیتال عک...   \n",
       "2  آدرس و تلفن دکتر مهناز عابدینی نظرات و تجربیات...   \n",
       "3                              دانلود نرم افزار Geph   \n",
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     "metadata": {},
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   ],
   "source": [
    "#import dataset\n",
    "train = pd.read_csv('yektanet_train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "419a4041",
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4789 entries, 0 to 4788\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   category      4789 non-null   object\n",
      " 1   description   4743 non-null   object\n",
      " 2   text_content  4789 non-null   object\n",
      " 3   title         4789 non-null   object\n",
      " 4   h1            4431 non-null   object\n",
      " 5   h2            3350 non-null   object\n",
      " 6   url           4789 non-null   object\n",
      " 7   domain        4784 non-null   object\n",
      " 8   id            4789 non-null   int64 \n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 336.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get more Information about DataSet\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ff2fe0",
   "metadata": {},
   "source": [
    "<a id=\"4\"></a>\n",
    "# <p style=\"background-image: url(https://i.postimg.cc/K87ByXmr/stage5.jpg);font-family:camtasia;font-size:120%;color:white;text-align:center;border-radius:15px 50px; padding:7px\">Preprocessing & Feature Engineering </p>\n",
    "<a class=\"btn\" href=\"#home\">Tabel of Contents</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "03a60101",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete duplicated rows in dataset\n",
    "def duplicated_rows(df):\n",
    "    df=df.drop_duplicates(keep='first')\n",
    "    return df\n",
    "\n",
    "train= duplicated_rows(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9236c835",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Constant Columns from Training Data\n",
    "train = train.drop(columns=['id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7f16d750",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4738 entries, 0 to 4788\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   category      4738 non-null   object\n",
      " 1   description   4738 non-null   object\n",
      " 2   text_content  4738 non-null   object\n",
      " 3   title         4738 non-null   object\n",
      " 4   h1            4388 non-null   object\n",
      " 5   h2            3322 non-null   object\n",
      " 6   url           4738 non-null   object\n",
      " 7   domain        4738 non-null   object\n",
      "dtypes: object(8)\n",
      "memory usage: 333.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Removing NaN Values from 'description' ,'domain' Columns in Training Data\n",
    "train = train.dropna(subset=['description' ,'domain'])\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d19bc1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Spaces from Category Column\n",
    "def remove_spaces(string):\n",
    "    return string.strip()\n",
    "train['category'] = train['category'].apply(remove_spaces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "409150ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding 'category' and 'domain' Columns in the training data\n",
    "def encode_col(df, cols):\n",
    "    encoder = LabelEncoder()\n",
    "    for col in cols:\n",
    "        df[col] = encoder.fit_transform(df[col])\n",
    "    return df\n",
    "\n",
    "columns_to_encode = ['category', 'domain']\n",
    "train = encode_col(train, columns_to_encode)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c0459d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling Missing Values in specified Columns\n",
    "cols = ['h1', 'h2','description']\n",
    "def fillna_with_value(df):\n",
    "    for col in cols:\n",
    "        df[col] = df[col].fillna('unknown')\n",
    "    return df\n",
    "\n",
    "train = fillna_with_value(train)\n"
   ]
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   "id": "1deb90be",
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Visualizing Class Distribution using a Bar Chart\n",
    "def plot_class_distribution(data):\n",
    "    class_counts = data['category'].value_counts()\n",
    "    class_labels = class_counts.index\n",
    "\n",
    "    fig = go.Figure(data=[go.Bar(x=class_labels, y=class_counts)])\n",
    "\n",
    "    fig.update_layout(\n",
    "        xaxis_title='Category',\n",
    "        yaxis_title='Count',\n",
    "        title='Class Distribution'\n",
    "    )\n",
    "\n",
    "    fig.show()\n",
    "\n",
    "plot_class_distribution(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a2dee8",
   "metadata": {},
   "source": [
    "<h1>Hazm: A Comprehensive NLP Library for Persian Text Processing and Analysis</h1>\n",
    "<p>\n",
    "Hazm is a Python library specifically designed for natural language processing (NLP) tasks in the Persian language. It provides a wide range of functionalities and tools to preprocess and analyze Persian text data. The library offers various features such as text normalization, tokenization, lemmatization, stemming, part-of-speech tagging, and stop-word removal. It is developed based on the linguistic rules and patterns of the Persian language.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0e11fa3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing Persian Text Using Hazm Library\n",
    "\n",
    "def preprocessing_text(text):\n",
    "    # Remove non-Persian characters\n",
    "    normalizer = hazm.Normalizer()\n",
    "    text = normalizer.normalize(text)\n",
    "    \n",
    "    # Tokenize\n",
    "    tokenizer = hazm.word_tokenize\n",
    "    tokens = tokenizer(text)\n",
    "    \n",
    "    # Lemmatization\n",
    "    lemmatizer = hazm.Lemmatizer()\n",
    "    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
    "    \n",
    "    # Removal of stopwords\n",
    "    stopwords = hazm.stopwords_list()\n",
    "    filtered_tokens = [token for token in lemmatized_tokens if token not in stopwords]\n",
    "    filtered_sentence = ' '.join(filtered_tokens)\n",
    "    \n",
    "    return filtered_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "94333e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing Text in the 'title' Column\n",
    "train['title']= train['title'].apply(preprocessing_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "002443f4",
   "metadata": {},
   "source": [
    "<html>\n",
    "<head>\n",
    "</head>\n",
    "<body>\n",
    "  <h1>Textual Data Extraction and Vectorization for Machine Learning Training</h1>\n",
    "  <p>\n",
    "In this step, we extract textual data from other features and use vectorization to convert it into a numerical format suitable for training machine learning models.\n",
    "  </p>\n",
    "    <h1>TF-IDF (Term Frequency-Inverse Document Frequency) vectorization</h1>\n",
    "  <p>\n",
    "TF-IDF (Term Frequency-Inverse Document Frequency) vectorization is a popular technique used in natural language processing (NLP) to convert textual data into numerical representations. It assigns weights to words based on their frequency in a document and their rarity across all documents in a corpus. The TF-IDF score reflects the importance of a word in a specific document relative to its occurrence in other documents. This vectorization method helps capture the significance of words in a document and is commonly used for tasks such as text classification, information retrieval, and clustering in machine learning.\n",
    "  </p>\n",
    "</body>\n",
    "</html>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e20801eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Text Vectorization using TF-IDF for the 'title' Column\n",
    "tfidf_vectorizer = TfidfVectorizer(ngram_range=(1, 2))\n",
    "vec_tiltle = tfidf_vectorizer.fit_transform(train.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31fc50c",
   "metadata": {},
   "source": [
    "<h1>SMOTE: Addressing Class Imbalance through Synthetic Minority Over-sampling Technique</h1>\n",
    "<p>\n",
    "SMOTE (Synthetic Minority Over-sampling Technique) is an oversampling method implemented in the imblearn library. It addresses the class imbalance in datasets by generating synthetic samples for the minority class. SMOTE works by interpolating between feature vectors of minority class instances, expanding the feature space, and balancing the class distribution. By increasing the representation of the minority class, SMOTE helps prevent bias in machine learning models and improves their performance on imbalanced datasets.\n",
    "</p>\n",
    "</body>\n",
    "</html>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "038f58eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Oversampling with SMOTE for Text Classification\n",
    "smote = SMOTE(sampling_strategy='auto', k_neighbors=4, random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(vec_tiltle, train.category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d56abc9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split Dataset to train and validation\n",
    "x_train ,x_val, y_train, y_val = train_test_split(X_resampled, y_resampled, test_size=0.2, shuffle=True, random_state=24) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0adb6c5",
   "metadata": {},
   "source": [
    "<a id=\"5\"></a>\n",
    "# <p style=\"background-image: url(https://i.postimg.cc/K87ByXmr/stage5.jpg);font-family:camtasia;font-size:120%;color:white;text-align:center;border-radius:15px 50px; padding:7px\">Training and Evaluation of Models</p>\n",
    "<a class=\"btn\" href=\"#home\">Tabel of Contents</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6c0fb92f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.9808703188654513\n"
     ]
    }
   ],
   "source": [
    "# Training an XGBoost Text Classification Model\n",
    "xgb_model = XGBClassifier()\n",
    "xgb_model.fit(x_train, y_train)\n",
    "\n",
    "y_pred = txt_model.predict(x_train)\n",
    "\n",
    "# Calculate F1-score for training\n",
    "f1 = f1_score(y_train, y_pred, average='weighted')\n",
    "\n",
    "print(\"F1-score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "57d7a4ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.9962460183427732\n"
     ]
    }
   ],
   "source": [
    "# Training an SVM Text Classification Model\n",
    "svm_model = svm.SVC(kernel='linear')\n",
    "svm_model.fit(x_train, y_train)\n",
    "\n",
    "y_pred = svm_model.predict(x_train) \n",
    "\n",
    "# Calculate F1-score\n",
    "f1 = f1_score(y_train, y_pred, average='weighted')\n",
    "print(\"F1-score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "631a9184",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.9980300318719476\n"
     ]
    }
   ],
   "source": [
    "# Training an RandomForestClassifier Text Classification Model\n",
    "RF_model = RandomForestClassifier()\n",
    "RF_model.fit(x_train, y_train)\n",
    "\n",
    "y_pred =RF_model.predict(x_train)\n",
    "\n",
    "# Calculate F1-score\n",
    "f1 = f1_score(y_train, y_pred, average='weighted')\n",
    "print(\"F1-score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9d880a7f",
   "metadata": {},
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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     "metadata": {},
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   ],
   "source": [
    "# Evaluated the models by Validation Dataset\n",
    "\n",
    "def evaluate_classifier(classifier, x_train, y_train, x_val, y_val):\n",
    "    # Train the classifier\n",
    "    classifier.fit(x_train, y_train)\n",
    "\n",
    "    # Predict on the validation set\n",
    "    y_pred = classifier.predict(x_val)\n",
    "\n",
    "    # Calculate evaluation metrics\n",
    "    accuracy = accuracy_score(y_val, y_pred)\n",
    "    precision = precision_score(y_val, y_pred, average='weighted')\n",
    "    recall = recall_score(y_val, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_val, y_pred, average='weighted')\n",
    "\n",
    "    # Return the evaluation results\n",
    "    return accuracy, precision, recall, f1\n",
    "\n",
    "# Define the classifiers\n",
    "classifiers = [\n",
    "    XGBClassifier(),\n",
    "    svm.SVC(kernel='linear'),\n",
    "    RandomForestClassifier()\n",
    "]\n",
    "\n",
    "# Initialize lists to store the metric values\n",
    "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score']\n",
    "accuracy_values = []\n",
    "precision_values = []\n",
    "recall_values = []\n",
    "f1_values = []\n",
    "\n",
    "# Evaluate each classifier and store the metric values\n",
    "for classifier in classifiers:\n",
    "    accuracy, precision, recall, f1 = evaluate_classifier(classifier, x_train, y_train, x_val, y_val)\n",
    "    accuracy_values.append(accuracy)\n",
    "    precision_values.append(precision)\n",
    "    recall_values.append(recall)\n",
    "    f1_values.append(f1)\n",
    "\n",
    "# Create a bar chart using Plotly\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(x=[type(classifier).__name__ for classifier in classifiers], y=accuracy_values, name='Accuracy'))\n",
    "fig.add_trace(go.Bar(x=[type(classifier).__name__ for classifier in classifiers], y=precision_values, name='Precision'))\n",
    "fig.add_trace(go.Bar(x=[type(classifier).__name__ for classifier in classifiers], y=recall_values, name='Recall'))\n",
    "fig.add_trace(go.Bar(x=[type(classifier).__name__ for classifier in classifiers], y=f1_values, name='F1 Score'))\n",
    "\n",
    "# Customize the chart layout\n",
    "fig.update_layout(\n",
    "    title='Classifier Evaluation Metrics',\n",
    "    xaxis_title='Classifier',\n",
    "    yaxis_title='Score',\n",
    "    barmode='group',\n",
    "    legend=dict(orientation='h', x=0.1, y=1.1)\n",
    ")\n",
    "\n",
    "fig.show()"
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    "<a id=\"6\"></a>\n",
    "# <p style=\"background-image: url(https://i.postimg.cc/K87ByXmr/stage5.jpg);font-family:camtasia;font-size:120%;color:white;text-align:center;border-radius:15px 50px; padding:7px\">Thank you for taking the time to review my notebook. If you have any questions or criticisms, please kindly let me know in the comments section.  </p>"
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