Assignment2_Q2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bc410624",
   "metadata": {},
   "source": [
    "# Q2.1. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d25e514",
   "metadata": {},
   "source": [
    "### 1. Reading the csv format dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "725e5f0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kesarwani\\python_files\\IR_Assignment_2\\learn-ai-bbc\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# current directory\n",
    "curr_dir = os.getcwd()\n",
    "\n",
    "# dataset path\n",
    "\n",
    "path = curr_dir+'\\learn-ai-bbc'\n",
    "print(path)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57f4afd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>ArticleId</th>\n",
       "      <th>Text</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1833</td>\n",
       "      <td>worldcom ex-boss launches defence lawyers defe...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>154</td>\n",
       "      <td>german business confidence slides german busin...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1101</td>\n",
       "      <td>bbc poll indicates economic gloom citizens in ...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976</td>\n",
       "      <td>lifestyle  governs mobile choice  faster  bett...</td>\n",
       "      <td>tech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>917</td>\n",
       "      <td>enron bosses in $168m payout eighteen former e...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ArticleId                                               Text  Category\n",
       "0       1833  worldcom ex-boss launches defence lawyers defe...  business\n",
       "1        154  german business confidence slides german busin...  business\n",
       "2       1101  bbc poll indicates economic gloom citizens in ...  business\n",
       "3       1976  lifestyle  governs mobile choice  faster  bett...      tech\n",
       "4        917  enron bosses in $168m payout eighteen former e...  business"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_train= pd.read_csv(path+\"\\BBC News Train.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1601cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1490, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01c67c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>ArticleId</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1018</td>\n",
       "      <td>qpr keeper day heads for preston queens park r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1319</td>\n",
       "      <td>software watching while you work software that...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1138</td>\n",
       "      <td>d arcy injury adds to ireland woe gordon d arc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459</td>\n",
       "      <td>india s reliance family feud heats up the ongo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1020</td>\n",
       "      <td>boro suffer morrison injury blow middlesbrough...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ArticleId                                               Text\n",
       "0       1018  qpr keeper day heads for preston queens park r...\n",
       "1       1319  software watching while you work software that...\n",
       "2       1138  d arcy injury adds to ireland woe gordon d arc...\n",
       "3        459  india s reliance family feud heats up the ongo...\n",
       "4       1020  boro suffer morrison injury blow middlesbrough..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test=pd.read_csv(path+\"\\BBC News Test.csv\")\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2e36ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(735, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2867f1ce",
   "metadata": {},
   "source": [
    "### 2. Removing unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "762819ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the 'ArticleId' column\n",
    "data = df_train.drop(columns=['ArticleId'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2302c0fc",
   "metadata": {},
   "source": [
    "### 3 & 4. Text cleaning and performing stemming and lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aaf23616",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to C:\\Users\\Rohit\n",
      "[nltk_data]     Kesarwani\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to C:\\Users\\Rohit\n",
      "[nltk_data]     Kesarwani\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import string\n",
    "\n",
    "# Download the stopwords and punkt tokenizer\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "\n",
    "# Create a list of stopwords and punctuation marks\n",
    "stopwords_list = stopwords.words('english') + list(string.punctuation)\n",
    "\n",
    "# Initialize the WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# function to clean and tokenize the text\n",
    "def clean_tokenize_text(text):\n",
    "    # Convert the text to lowercase\n",
    "    text = text.lower()\n",
    "\n",
    "    # Tokenize the text\n",
    "    words = word_tokenize(text)\n",
    "\n",
    "    # Remove the stopwords and punctuation marks\n",
    "    words = [word for word in words if word not in stopwords_list]\n",
    "\n",
    "    # Lemmatize the words\n",
    "    words = [lemmatizer.lemmatize(word) for word in words]\n",
    "\n",
    "    return words\n",
    "\n",
    "# Apply the clean_tokenize_text function to the 'Text' column\n",
    "data['Text'] = data['Text'].apply(clean_tokenize_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a99cc8",
   "metadata": {},
   "source": [
    "### 5. TFICF-Weighing Scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee0b3421",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Convert the list of words back to a string for the TfidfVectorizer\n",
    "data['Text'] = data['Text'].apply(' '.join)\n",
    "\n",
    "# Initialize the TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer()\n",
    "\n",
    "# Fit and transform the vectorizer on the 'Text' column\n",
    "tfidf_matrix = vectorizer.fit_transform(data['Text'])\n",
    "\n",
    "# Convert the sparse matrix to a dense matrix\n",
    "tfidf_matrix = tfidf_matrix.toarray()\n",
    "\n",
    "# Create a DataFrame from the dense matrix\n",
    "tfidf_df = pd.DataFrame(tfidf_matrix, columns=vectorizer.get_feature_names())\n",
    "\n",
    "# Add the 'Category' column to the DataFrame\n",
    "tfidf_df['Category'] = data['Category']\n",
    "\n",
    "# Group the DataFrame by category and calculate the mean of each feature\n",
    "tficf_df = tfidf_df.groupby('Category').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b371119e",
   "metadata": {},
   "source": [
    "# Q2.2. Dataset Split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95825159",
   "metadata": {},
   "source": [
    "### Split the dataset into training and testing sets into 70:30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e17f37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(tfidf_df.drop('Category', axis=1), tfidf_df['Category'], test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be130324",
   "metadata": {},
   "source": [
    "# Q2.3. Training Naive-Bayes with TF-ICF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b18be6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['business', 'politics', 'politics', 'politics', 'tech', 'business',\n",
       "       'tech', 'entertainment', 'business', 'tech', 'politics',\n",
       "       'entertainment', 'business', 'sport', 'tech', 'politics',\n",
       "       'business', 'sport', 'politics', 'business', 'tech',\n",
       "       'entertainment', 'sport', 'business', 'sport', 'tech', 'business',\n",
       "       'sport', 'tech', 'business', 'sport', 'business', 'business',\n",
       "       'politics', 'sport', 'tech', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'sport', 'business', 'politics',\n",
       "       'business', 'sport', 'business', 'politics', 'tech', 'politics',\n",
       "       'business', 'sport', 'sport', 'business', 'sport', 'politics',\n",
       "       'entertainment', 'entertainment', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'business', 'politics', 'tech', 'tech',\n",
       "       'tech', 'business', 'tech', 'politics', 'politics', 'politics',\n",
       "       'sport', 'sport', 'sport', 'entertainment', 'entertainment',\n",
       "       'business', 'politics', 'business', 'business', 'business',\n",
       "       'sport', 'sport', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'business', 'business', 'tech', 'entertainment', 'business',\n",
       "       'politics', 'tech', 'business', 'politics', 'tech', 'tech',\n",
       "       'entertainment', 'politics', 'entertainment', 'politics',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'politics', 'sport',\n",
       "       'business', 'tech', 'tech', 'business', 'tech', 'entertainment',\n",
       "       'tech', 'business', 'politics', 'business', 'entertainment',\n",
       "       'business', 'politics', 'sport', 'business', 'politics',\n",
       "       'business', 'business', 'tech', 'tech', 'politics',\n",
       "       'entertainment', 'business', 'sport', 'entertainment', 'sport',\n",
       "       'business', 'politics', 'business', 'entertainment', 'business',\n",
       "       'entertainment', 'business', 'entertainment', 'tech', 'sport',\n",
       "       'entertainment', 'politics', 'business', 'tech', 'politics',\n",
       "       'business', 'sport', 'business', 'tech', 'sport', 'entertainment',\n",
       "       'tech', 'tech', 'sport', 'tech', 'sport', 'sport', 'politics',\n",
       "       'business', 'tech', 'politics', 'politics', 'tech', 'sport',\n",
       "       'sport', 'tech', 'politics', 'tech', 'tech', 'entertainment',\n",
       "       'business', 'tech', 'entertainment', 'sport', 'business',\n",
       "       'business', 'sport', 'sport', 'tech', 'entertainment',\n",
       "       'entertainment', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'politics', 'sport', 'sport', 'politics', 'tech', 'sport', 'sport',\n",
       "       'politics', 'sport', 'sport', 'business', 'entertainment',\n",
       "       'politics', 'sport', 'entertainment', 'business', 'sport', 'tech',\n",
       "       'tech', 'business', 'business', 'sport', 'politics', 'business',\n",
       "       'entertainment', 'sport', 'politics', 'politics', 'sport', 'tech',\n",
       "       'politics', 'sport', 'tech', 'business', 'business',\n",
       "       'entertainment', 'tech', 'business', 'politics', 'politics',\n",
       "       'politics', 'politics', 'sport', 'sport', 'business', 'business',\n",
       "       'tech', 'business', 'sport', 'sport', 'politics', 'politics',\n",
       "       'entertainment', 'entertainment', 'entertainment', 'business',\n",
       "       'business', 'entertainment', 'entertainment', 'sport',\n",
       "       'entertainment', 'sport', 'politics', 'tech', 'sport', 'politics',\n",
       "       'tech', 'entertainment', 'sport', 'tech', 'business', 'business',\n",
       "       'politics', 'tech', 'tech', 'tech', 'sport', 'sport', 'business',\n",
       "       'politics', 'entertainment', 'sport', 'sport', 'tech', 'sport',\n",
       "       'sport', 'politics', 'sport', 'sport', 'business', 'politics',\n",
       "       'entertainment', 'business', 'business', 'politics', 'business',\n",
       "       'business', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'business', 'business', 'business', 'business', 'politics',\n",
       "       'entertainment', 'tech', 'entertainment', 'sport', 'business',\n",
       "       'politics', 'sport', 'politics', 'business', 'business',\n",
       "       'entertainment', 'business', 'entertainment', 'entertainment',\n",
       "       'sport', 'tech', 'tech', 'politics', 'tech', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'entertainment',\n",
       "       'politics', 'entertainment', 'entertainment', 'politics',\n",
       "       'business', 'tech', 'entertainment', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'business', 'business', 'politics',\n",
       "       'business', 'sport', 'politics', 'business', 'politics',\n",
       "       'entertainment', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'entertainment', 'sport', 'business', 'business', 'politics',\n",
       "       'politics', 'sport', 'sport', 'entertainment', 'politics', 'sport',\n",
       "       'sport', 'tech', 'tech', 'sport', 'politics', 'politics', 'tech',\n",
       "       'tech', 'business', 'entertainment', 'sport', 'business',\n",
       "       'entertainment', 'politics', 'entertainment', 'sport', 'politics',\n",
       "       'sport', 'tech', 'entertainment', 'politics', 'entertainment',\n",
       "       'politics', 'sport', 'politics', 'tech', 'sport', 'entertainment',\n",
       "       'sport', 'sport', 'sport', 'sport', 'sport', 'business', 'sport',\n",
       "       'sport', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'entertainment', 'entertainment', 'business', 'sport', 'sport',\n",
       "       'tech', 'politics', 'entertainment', 'business', 'business',\n",
       "       'entertainment', 'entertainment', 'business', 'entertainment',\n",
       "       'business', 'business', 'business', 'sport', 'politics',\n",
       "       'politics', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'politics', 'politics', 'sport', 'entertainment', 'politics',\n",
       "       'tech', 'politics', 'tech', 'sport', 'business', 'tech',\n",
       "       'politics', 'business', 'politics', 'business', 'politics',\n",
       "       'entertainment', 'business', 'entertainment', 'politics',\n",
       "       'business', 'politics', 'business', 'sport', 'sport',\n",
       "       'entertainment', 'tech'], dtype='<U13')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# Initialize the Naive Bayes classifier\n",
    "clf = MultinomialNB()\n",
    "\n",
    "# Fit the classifier on the training data\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict the categories of the testing data\n",
    "y_pred = clf.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4914fe77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.970917225950783\n",
      "Confusion matrix:\n",
      " <function confusion_matrix at 0x000001CF89169940>\n",
      "Precision: 0.9718\n",
      "Recall: 0.9691\n",
      "F1 score: 0.9702\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "# Calculate the accuracy score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# Print the accuracy score\n",
    "print('Accuracy:', accuracy)\n",
    "\n",
    "# Print the confusion matrix\n",
    "confusion_matrix1 = confusion_matrix(y_test, y_pred)\n",
    "print('Confusion matrix:\\n', confusion_matrix)\n",
    "\n",
    "\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(f'Precision: {precision:.4f}')\n",
    "print(f'Recall: {recall:.4f}')\n",
    "print(f'F1 score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38bb5aa",
   "metadata": {},
   "source": [
    "### Split the dataset into training and testing sets into 80:20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3096fa9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(tfidf_df.drop('Category', axis=1), tfidf_df['Category'], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "acd34fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['business', 'politics', 'politics', 'politics', 'sport',\n",
       "       'business', 'tech', 'entertainment', 'business', 'tech',\n",
       "       'politics', 'entertainment', 'business', 'sport', 'tech',\n",
       "       'politics', 'business', 'sport', 'politics', 'business', 'tech',\n",
       "       'entertainment', 'sport', 'business', 'sport', 'tech', 'business',\n",
       "       'sport', 'tech', 'business', 'sport', 'business', 'business',\n",
       "       'politics', 'sport', 'tech', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'sport', 'business', 'politics',\n",
       "       'business', 'sport', 'business', 'politics', 'tech', 'politics',\n",
       "       'business', 'sport', 'sport', 'business', 'sport', 'politics',\n",
       "       'entertainment', 'entertainment', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'business', 'politics', 'tech', 'tech',\n",
       "       'tech', 'business', 'tech', 'politics', 'politics', 'politics',\n",
       "       'sport', 'sport', 'sport', 'entertainment', 'entertainment',\n",
       "       'business', 'politics', 'business', 'business', 'business',\n",
       "       'sport', 'sport', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'business', 'business', 'tech', 'entertainment', 'business',\n",
       "       'politics', 'tech', 'business', 'politics', 'tech', 'tech',\n",
       "       'entertainment', 'politics', 'entertainment', 'politics',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'politics', 'sport',\n",
       "       'business', 'tech', 'tech', 'business', 'tech', 'entertainment',\n",
       "       'tech', 'business', 'politics', 'business', 'entertainment',\n",
       "       'business', 'politics', 'sport', 'business', 'politics',\n",
       "       'business', 'business', 'tech', 'tech', 'politics',\n",
       "       'entertainment', 'business', 'sport', 'entertainment', 'sport',\n",
       "       'business', 'politics', 'business', 'entertainment', 'business',\n",
       "       'entertainment', 'business', 'entertainment', 'tech', 'sport',\n",
       "       'entertainment', 'politics', 'business', 'tech', 'politics',\n",
       "       'business', 'sport', 'business', 'tech', 'sport', 'entertainment',\n",
       "       'tech', 'tech', 'sport', 'tech', 'sport', 'sport', 'politics',\n",
       "       'business', 'business', 'politics', 'politics', 'tech', 'sport',\n",
       "       'sport', 'tech', 'politics', 'tech', 'tech', 'entertainment',\n",
       "       'business', 'tech', 'entertainment', 'sport', 'business',\n",
       "       'business', 'sport', 'sport', 'tech', 'entertainment',\n",
       "       'entertainment', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'politics', 'sport', 'sport', 'politics', 'tech', 'sport', 'sport',\n",
       "       'politics', 'sport', 'sport', 'business', 'entertainment',\n",
       "       'politics', 'sport', 'entertainment', 'business', 'sport', 'tech',\n",
       "       'tech', 'business', 'business', 'sport', 'politics', 'business',\n",
       "       'entertainment', 'sport', 'politics', 'politics', 'sport', 'tech',\n",
       "       'politics', 'sport', 'tech', 'business', 'business',\n",
       "       'entertainment', 'tech', 'business', 'politics', 'politics',\n",
       "       'politics', 'politics', 'sport', 'sport', 'business', 'business',\n",
       "       'tech', 'business', 'sport', 'sport', 'politics', 'politics',\n",
       "       'entertainment', 'entertainment', 'entertainment', 'business',\n",
       "       'business', 'entertainment', 'entertainment', 'sport',\n",
       "       'entertainment', 'sport', 'politics', 'tech', 'sport', 'politics',\n",
       "       'tech', 'entertainment', 'sport', 'tech', 'business', 'business',\n",
       "       'politics', 'tech', 'tech', 'tech', 'sport', 'sport', 'business',\n",
       "       'politics', 'entertainment', 'sport', 'sport', 'tech', 'sport',\n",
       "       'sport', 'politics', 'sport', 'sport', 'business', 'politics',\n",
       "       'entertainment', 'business', 'business', 'politics', 'business',\n",
       "       'business', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'business', 'business', 'business', 'business', 'politics',\n",
       "       'entertainment', 'tech', 'entertainment'], dtype='<U13')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Naive Bayes classifier\n",
    "clf2 = MultinomialNB()\n",
    "\n",
    "# Fit the classifier on the training data\n",
    "clf2.fit(X_train2, y_train2)\n",
    "\n",
    "# Predict the categories of the testing data\n",
    "y_pred2 = clf2.predict(X_test2)\n",
    "y_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3fb641b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.959731543624161\n",
      "Confusion matrix:\n",
      " [[73  0  1  0  1]\n",
      " [ 1 44  1  0  0]\n",
      " [ 2  0 53  1  0]\n",
      " [ 0  0  0 63  0]\n",
      " [ 1  0  3  1 53]]\n",
      "Precision: 0.9625\n",
      "Recall: 0.9580\n",
      "F1 score: 0.9598\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "accuracy2 = accuracy_score(y_test2, y_pred2)\n",
    "\n",
    "# Print the accuracy score\n",
    "print('Accuracy:', accuracy2)\n",
    "\n",
    "# Print the confusion matrix\n",
    "confusion_matrix2 = confusion_matrix(y_test2, y_pred2)\n",
    "print('Confusion matrix:\\n', confusion_matrix2)\n",
    "\n",
    "\n",
    "precision2 = precision_score(y_test2, y_pred2, average='macro')\n",
    "recall2 = recall_score(y_test2, y_pred2, average='macro')\n",
    "f1_second = f1_score(y_test2, y_pred2, average='macro')\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(f'Precision: {precision2:.4f}')\n",
    "print(f'Recall: {recall2:.4f}')\n",
    "print(f'F1 score: {f1_second:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc55fdf",
   "metadata": {},
   "source": [
    "### Split the dataset into training and testing sets into 60:40\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2c822fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(tfidf_df.drop('Category', axis=1), tfidf_df['Category'], test_size=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "18d542dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['business', 'politics', 'politics', 'politics', 'sport',\n",
       "       'business', 'tech', 'entertainment', 'business', 'tech',\n",
       "       'politics', 'entertainment', 'business', 'sport', 'tech',\n",
       "       'politics', 'business', 'sport', 'politics', 'business', 'tech',\n",
       "       'entertainment', 'sport', 'business', 'sport', 'tech', 'business',\n",
       "       'sport', 'tech', 'business', 'sport', 'business', 'business',\n",
       "       'politics', 'sport', 'tech', 'politics', 'business',\n",
       "       'entertainment', 'business', 'sport', 'business', 'politics',\n",
       "       'business', 'sport', 'business', 'politics', 'tech', 'politics',\n",
       "       'business', 'sport', 'sport', 'business', 'sport', 'politics',\n",
       "       'entertainment', 'entertainment', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'business', 'politics', 'tech', 'tech',\n",
       "       'tech', 'business', 'tech', 'politics', 'politics', 'politics',\n",
       "       'sport', 'sport', 'sport', 'entertainment', 'entertainment',\n",
       "       'business', 'politics', 'business', 'business', 'business',\n",
       "       'sport', 'sport', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'business', 'business', 'tech', 'entertainment', 'business',\n",
       "       'politics', 'tech', 'business', 'politics', 'tech', 'tech',\n",
       "       'entertainment', 'politics', 'entertainment', 'politics',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'politics', 'sport',\n",
       "       'business', 'tech', 'tech', 'business', 'tech', 'entertainment',\n",
       "       'tech', 'business', 'politics', 'business', 'entertainment',\n",
       "       'business', 'politics', 'sport', 'business', 'politics',\n",
       "       'business', 'business', 'tech', 'tech', 'politics',\n",
       "       'entertainment', 'business', 'sport', 'entertainment', 'sport',\n",
       "       'business', 'politics', 'business', 'entertainment', 'business',\n",
       "       'entertainment', 'business', 'entertainment', 'tech', 'sport',\n",
       "       'entertainment', 'politics', 'business', 'tech', 'politics',\n",
       "       'business', 'sport', 'business', 'tech', 'sport', 'entertainment',\n",
       "       'tech', 'tech', 'sport', 'tech', 'sport', 'sport', 'politics',\n",
       "       'business', 'business', 'politics', 'politics', 'politics',\n",
       "       'sport', 'sport', 'tech', 'politics', 'tech', 'tech',\n",
       "       'entertainment', 'business', 'tech', 'entertainment', 'sport',\n",
       "       'business', 'business', 'sport', 'sport', 'tech', 'entertainment',\n",
       "       'entertainment', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'politics', 'sport', 'sport', 'politics', 'tech', 'sport', 'sport',\n",
       "       'politics', 'sport', 'sport', 'business', 'entertainment',\n",
       "       'politics', 'sport', 'entertainment', 'business', 'sport', 'tech',\n",
       "       'tech', 'business', 'business', 'sport', 'politics', 'business',\n",
       "       'entertainment', 'sport', 'politics', 'politics', 'sport', 'tech',\n",
       "       'politics', 'sport', 'tech', 'business', 'business',\n",
       "       'entertainment', 'business', 'business', 'politics', 'politics',\n",
       "       'politics', 'politics', 'sport', 'sport', 'business', 'business',\n",
       "       'tech', 'business', 'sport', 'sport', 'politics', 'politics',\n",
       "       'entertainment', 'entertainment', 'entertainment', 'business',\n",
       "       'business', 'entertainment', 'entertainment', 'sport',\n",
       "       'entertainment', 'sport', 'politics', 'tech', 'sport', 'politics',\n",
       "       'tech', 'entertainment', 'sport', 'tech', 'business', 'business',\n",
       "       'politics', 'tech', 'tech', 'tech', 'sport', 'sport', 'business',\n",
       "       'politics', 'entertainment', 'sport', 'sport', 'tech', 'sport',\n",
       "       'sport', 'politics', 'sport', 'sport', 'business', 'politics',\n",
       "       'entertainment', 'business', 'business', 'politics', 'business',\n",
       "       'business', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'business', 'business', 'business', 'business', 'politics',\n",
       "       'entertainment', 'tech', 'entertainment', 'sport', 'business',\n",
       "       'politics', 'sport', 'politics', 'business', 'business',\n",
       "       'entertainment', 'business', 'entertainment', 'entertainment',\n",
       "       'sport', 'tech', 'tech', 'politics', 'tech', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'entertainment',\n",
       "       'politics', 'entertainment', 'entertainment', 'politics',\n",
       "       'business', 'tech', 'entertainment', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'business', 'business', 'politics',\n",
       "       'business', 'sport', 'politics', 'business', 'politics',\n",
       "       'entertainment', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'entertainment', 'sport', 'business', 'business', 'politics',\n",
       "       'politics', 'sport', 'sport', 'entertainment', 'politics', 'sport',\n",
       "       'sport', 'tech', 'tech', 'sport', 'politics', 'politics', 'tech',\n",
       "       'tech', 'business', 'entertainment', 'sport', 'business',\n",
       "       'entertainment', 'politics', 'entertainment', 'sport', 'politics',\n",
       "       'sport', 'tech', 'entertainment', 'politics', 'entertainment',\n",
       "       'politics', 'sport', 'politics', 'tech', 'sport', 'entertainment',\n",
       "       'sport', 'sport', 'sport', 'sport', 'sport', 'business', 'sport',\n",
       "       'sport', 'sport', 'business', 'sport', 'sport', 'entertainment',\n",
       "       'entertainment', 'business', 'sport', 'sport', 'tech', 'politics',\n",
       "       'entertainment', 'business', 'business', 'entertainment',\n",
       "       'entertainment', 'business', 'entertainment', 'business',\n",
       "       'business', 'business', 'sport', 'politics', 'politics', 'sport',\n",
       "       'business', 'entertainment', 'sport', 'politics', 'politics',\n",
       "       'sport', 'entertainment', 'politics', 'tech', 'politics', 'tech',\n",
       "       'sport', 'business', 'tech', 'politics', 'business', 'politics',\n",
       "       'business', 'politics', 'entertainment', 'business',\n",
       "       'entertainment', 'politics', 'business', 'politics', 'business',\n",
       "       'sport', 'sport', 'entertainment', 'tech', 'sport', 'tech', 'tech',\n",
       "       'politics', 'business', 'sport', 'entertainment', 'tech',\n",
       "       'politics', 'business', 'tech', 'business', 'business', 'tech',\n",
       "       'tech', 'business', 'tech', 'sport', 'entertainment', 'business',\n",
       "       'business', 'politics', 'entertainment', 'politics', 'sport',\n",
       "       'entertainment', 'tech', 'tech', 'sport', 'entertainment',\n",
       "       'entertainment', 'tech', 'sport', 'business', 'sport', 'politics',\n",
       "       'entertainment', 'sport', 'sport', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'sport', 'politics', 'entertainment',\n",
       "       'business', 'entertainment', 'business', 'entertainment', 'tech',\n",
       "       'sport', 'business', 'business', 'tech', 'business', 'politics',\n",
       "       'tech', 'business', 'business', 'politics', 'business',\n",
       "       'entertainment', 'sport', 'business', 'business', 'tech', 'sport',\n",
       "       'sport', 'tech', 'sport', 'tech', 'entertainment', 'tech',\n",
       "       'entertainment', 'sport', 'politics', 'tech', 'sport', 'sport',\n",
       "       'politics', 'politics', 'business', 'tech', 'sport', 'sport',\n",
       "       'sport', 'entertainment', 'tech', 'tech', 'entertainment',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'entertainment',\n",
       "       'politics', 'entertainment', 'politics', 'entertainment', 'sport',\n",
       "       'politics', 'business', 'sport', 'sport', 'tech', 'business',\n",
       "       'entertainment', 'tech', 'politics', 'tech', 'tech', 'sport',\n",
       "       'tech', 'tech', 'business', 'entertainment', 'politics',\n",
       "       'entertainment', 'sport', 'tech', 'entertainment', 'sport',\n",
       "       'entertainment', 'entertainment', 'tech', 'politics', 'politics',\n",
       "       'business', 'sport', 'tech', 'sport', 'politics', 'tech',\n",
       "       'business', 'tech', 'tech', 'sport', 'business', 'sport',\n",
       "       'business', 'entertainment', 'politics', 'tech', 'politics',\n",
       "       'tech', 'politics', 'business', 'entertainment'], dtype='<U13')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Naive Bayes classifier\n",
    "clf3 = MultinomialNB()\n",
    "\n",
    "# Fit the classifier on the training data\n",
    "clf3.fit(X_train3, y_train3)\n",
    "\n",
    "# Predict the categories of the testing data\n",
    "y_pred3 = clf3.predict(X_test3)\n",
    "y_pred3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "debcf649",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9580536912751678\n",
      "Confusion matrix:\n",
      " [[134   0   1   0   2]\n",
      " [  1 102   2   4   0]\n",
      " [  2   0 104   3   0]\n",
      " [  0   0   0 129   0]\n",
      " [  2   0   5   3 102]]\n",
      "Precision: 0.9603\n",
      "Recall: 0.9557\n",
      "F1 score: 0.9572\n"
     ]
    }
   ],
   "source": [
    "accuracy3 = accuracy_score(y_test3, y_pred3)\n",
    "\n",
    "# Print the accuracy score\n",
    "print('Accuracy:', accuracy3)\n",
    "\n",
    "# Print the confusion matrix\n",
    "confusion_matrix3 = confusion_matrix(y_test3, y_pred3)\n",
    "print('Confusion matrix:\\n', confusion_matrix3)\n",
    "\n",
    "\n",
    "precision3 = precision_score(y_test3, y_pred3, average='macro')\n",
    "recall3 = recall_score(y_test3, y_pred3, average='macro')\n",
    "f1_third = f1_score(y_test3, y_pred3, average='macro')\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(f'Precision: {precision3:.4f}')\n",
    "print(f'Recall: {recall3:.4f}')\n",
    "print(f'F1 score: {f1_third:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92a11a3",
   "metadata": {},
   "source": [
    "### Split the dataset into training and testing sets into 50:50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3fa01c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train4, X_test4, y_train4, y_test4 = train_test_split(tfidf_df.drop('Category', axis=1), tfidf_df['Category'], test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "05c58390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['business', 'politics', 'politics', 'politics', 'sport',\n",
       "       'business', 'tech', 'entertainment', 'business', 'tech',\n",
       "       'politics', 'entertainment', 'business', 'sport', 'tech',\n",
       "       'politics', 'business', 'sport', 'politics', 'business', 'tech',\n",
       "       'entertainment', 'sport', 'business', 'sport', 'tech', 'business',\n",
       "       'sport', 'tech', 'business', 'sport', 'business', 'business',\n",
       "       'politics', 'sport', 'tech', 'politics', 'business',\n",
       "       'entertainment', 'business', 'sport', 'business', 'politics',\n",
       "       'business', 'sport', 'business', 'politics', 'tech', 'politics',\n",
       "       'business', 'sport', 'sport', 'business', 'sport', 'politics',\n",
       "       'entertainment', 'entertainment', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'business', 'entertainment', 'tech',\n",
       "       'tech', 'tech', 'business', 'tech', 'politics', 'politics',\n",
       "       'politics', 'sport', 'sport', 'sport', 'entertainment',\n",
       "       'entertainment', 'business', 'politics', 'business', 'business',\n",
       "       'business', 'sport', 'sport', 'sport', 'business', 'entertainment',\n",
       "       'sport', 'business', 'business', 'tech', 'entertainment',\n",
       "       'business', 'politics', 'tech', 'business', 'politics', 'tech',\n",
       "       'tech', 'entertainment', 'politics', 'entertainment', 'politics',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'politics', 'sport',\n",
       "       'business', 'tech', 'tech', 'business', 'tech', 'entertainment',\n",
       "       'tech', 'business', 'politics', 'business', 'entertainment',\n",
       "       'business', 'politics', 'sport', 'business', 'politics',\n",
       "       'business', 'business', 'tech', 'tech', 'politics',\n",
       "       'entertainment', 'business', 'sport', 'entertainment', 'sport',\n",
       "       'business', 'politics', 'business', 'entertainment', 'business',\n",
       "       'entertainment', 'business', 'entertainment', 'tech', 'sport',\n",
       "       'entertainment', 'politics', 'business', 'tech', 'politics',\n",
       "       'business', 'sport', 'business', 'tech', 'sport', 'entertainment',\n",
       "       'tech', 'tech', 'sport', 'tech', 'sport', 'sport', 'politics',\n",
       "       'business', 'business', 'politics', 'politics', 'tech', 'sport',\n",
       "       'sport', 'tech', 'politics', 'tech', 'tech', 'entertainment',\n",
       "       'business', 'tech', 'entertainment', 'sport', 'business',\n",
       "       'business', 'sport', 'sport', 'tech', 'entertainment',\n",
       "       'entertainment', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'politics', 'sport', 'sport', 'politics', 'tech', 'sport', 'sport',\n",
       "       'politics', 'sport', 'sport', 'business', 'entertainment',\n",
       "       'politics', 'sport', 'entertainment', 'business', 'sport', 'tech',\n",
       "       'tech', 'business', 'business', 'sport', 'politics', 'business',\n",
       "       'entertainment', 'sport', 'politics', 'politics', 'sport', 'tech',\n",
       "       'politics', 'sport', 'tech', 'business', 'business',\n",
       "       'entertainment', 'business', 'business', 'politics', 'politics',\n",
       "       'politics', 'politics', 'sport', 'sport', 'business', 'business',\n",
       "       'tech', 'business', 'sport', 'sport', 'politics', 'politics',\n",
       "       'entertainment', 'entertainment', 'entertainment', 'business',\n",
       "       'business', 'entertainment', 'entertainment', 'sport',\n",
       "       'entertainment', 'sport', 'politics', 'tech', 'sport', 'politics',\n",
       "       'tech', 'entertainment', 'sport', 'tech', 'business', 'business',\n",
       "       'politics', 'tech', 'tech', 'tech', 'sport', 'sport', 'business',\n",
       "       'politics', 'entertainment', 'sport', 'sport', 'tech', 'sport',\n",
       "       'sport', 'politics', 'sport', 'sport', 'business', 'politics',\n",
       "       'entertainment', 'business', 'business', 'politics', 'business',\n",
       "       'business', 'business', 'tech', 'politics', 'entertainment',\n",
       "       'business', 'business', 'business', 'business', 'politics',\n",
       "       'entertainment', 'tech', 'entertainment', 'sport', 'business',\n",
       "       'politics', 'sport', 'politics', 'business', 'business',\n",
       "       'entertainment', 'business', 'entertainment', 'entertainment',\n",
       "       'sport', 'tech', 'tech', 'politics', 'tech', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'entertainment',\n",
       "       'politics', 'entertainment', 'entertainment', 'politics',\n",
       "       'business', 'tech', 'entertainment', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'business', 'business', 'politics',\n",
       "       'business', 'sport', 'politics', 'business', 'politics',\n",
       "       'entertainment', 'sport', 'business', 'entertainment', 'sport',\n",
       "       'entertainment', 'sport', 'business', 'business', 'politics',\n",
       "       'politics', 'sport', 'sport', 'entertainment', 'politics', 'sport',\n",
       "       'sport', 'tech', 'tech', 'sport', 'politics', 'politics', 'tech',\n",
       "       'tech', 'business', 'entertainment', 'sport', 'business',\n",
       "       'entertainment', 'politics', 'entertainment', 'sport', 'politics',\n",
       "       'sport', 'tech', 'entertainment', 'politics', 'entertainment',\n",
       "       'politics', 'sport', 'politics', 'tech', 'sport', 'entertainment',\n",
       "       'sport', 'sport', 'sport', 'sport', 'sport', 'business', 'sport',\n",
       "       'sport', 'sport', 'business', 'sport', 'sport', 'entertainment',\n",
       "       'entertainment', 'business', 'sport', 'sport', 'tech', 'politics',\n",
       "       'entertainment', 'business', 'business', 'entertainment',\n",
       "       'entertainment', 'business', 'entertainment', 'business',\n",
       "       'business', 'business', 'sport', 'politics', 'politics', 'sport',\n",
       "       'business', 'entertainment', 'sport', 'politics', 'politics',\n",
       "       'sport', 'entertainment', 'politics', 'tech', 'politics', 'tech',\n",
       "       'sport', 'business', 'tech', 'politics', 'business', 'politics',\n",
       "       'business', 'politics', 'entertainment', 'business',\n",
       "       'entertainment', 'politics', 'business', 'politics', 'business',\n",
       "       'sport', 'sport', 'entertainment', 'tech', 'sport', 'tech', 'tech',\n",
       "       'politics', 'business', 'sport', 'entertainment', 'tech',\n",
       "       'politics', 'business', 'tech', 'business', 'business', 'tech',\n",
       "       'tech', 'business', 'tech', 'sport', 'entertainment', 'business',\n",
       "       'business', 'politics', 'entertainment', 'politics', 'sport',\n",
       "       'entertainment', 'tech', 'tech', 'sport', 'entertainment',\n",
       "       'entertainment', 'tech', 'sport', 'business', 'sport', 'politics',\n",
       "       'entertainment', 'sport', 'sport', 'politics', 'business',\n",
       "       'entertainment', 'tech', 'sport', 'politics', 'entertainment',\n",
       "       'business', 'entertainment', 'business', 'entertainment', 'tech',\n",
       "       'sport', 'business', 'business', 'tech', 'business', 'politics',\n",
       "       'tech', 'business', 'business', 'politics', 'business',\n",
       "       'entertainment', 'sport', 'business', 'business', 'tech', 'sport',\n",
       "       'sport', 'tech', 'sport', 'tech', 'entertainment', 'tech',\n",
       "       'entertainment', 'sport', 'politics', 'tech', 'sport', 'sport',\n",
       "       'politics', 'politics', 'business', 'tech', 'sport', 'sport',\n",
       "       'sport', 'entertainment', 'tech', 'tech', 'entertainment',\n",
       "       'entertainment', 'sport', 'tech', 'sport', 'entertainment',\n",
       "       'politics', 'entertainment', 'business', 'entertainment', 'sport',\n",
       "       'politics', 'business', 'sport', 'sport', 'tech', 'business',\n",
       "       'entertainment', 'tech', 'politics', 'tech', 'tech', 'sport',\n",
       "       'tech', 'tech', 'business', 'entertainment', 'politics',\n",
       "       'entertainment', 'sport', 'tech', 'entertainment', 'sport',\n",
       "       'entertainment', 'entertainment', 'tech', 'tech', 'politics',\n",
       "       'business', 'sport', 'tech', 'sport', 'politics', 'tech',\n",
       "       'business', 'tech', 'tech', 'sport', 'business', 'sport',\n",
       "       'business', 'entertainment', 'sport', 'tech', 'sport', 'tech',\n",
       "       'politics', 'business', 'entertainment', 'sport', 'tech',\n",
       "       'politics', 'tech', 'tech', 'sport', 'politics', 'entertainment',\n",
       "       'tech', 'business', 'sport', 'politics', 'entertainment',\n",
       "       'business', 'business', 'business', 'sport', 'politics',\n",
       "       'politics', 'entertainment', 'business', 'business', 'sport',\n",
       "       'sport', 'business', 'politics', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'politics', 'politics', 'business',\n",
       "       'tech', 'business', 'sport', 'entertainment', 'tech', 'politics',\n",
       "       'politics', 'business', 'business', 'tech', 'sport', 'politics',\n",
       "       'sport', 'entertainment', 'business', 'business', 'business',\n",
       "       'sport', 'business', 'entertainment', 'business', 'sport',\n",
       "       'politics', 'sport', 'sport', 'entertainment', 'business', 'sport',\n",
       "       'entertainment', 'sport', 'politics', 'entertainment', 'politics',\n",
       "       'business', 'business', 'politics', 'business', 'tech', 'tech',\n",
       "       'politics', 'entertainment', 'sport', 'entertainment', 'tech',\n",
       "       'tech', 'sport', 'sport', 'tech', 'sport', 'entertainment',\n",
       "       'politics', 'politics', 'politics', 'entertainment', 'sport',\n",
       "       'sport', 'tech', 'entertainment', 'tech', 'business', 'tech',\n",
       "       'tech', 'sport', 'sport', 'business', 'politics', 'politics',\n",
       "       'tech', 'business', 'sport', 'business', 'entertainment',\n",
       "       'entertainment', 'politics', 'sport', 'tech', 'sport',\n",
       "       'entertainment', 'business', 'sport', 'business', 'tech', 'sport',\n",
       "       'politics', 'business', 'tech', 'tech', 'sport', 'tech',\n",
       "       'business', 'business', 'politics', 'tech', 'business', 'politics',\n",
       "       'politics', 'politics', 'politics', 'politics', 'business',\n",
       "       'politics', 'sport', 'business', 'entertainment', 'business',\n",
       "       'sport', 'entertainment', 'business', 'tech', 'politics', 'sport',\n",
       "       'tech', 'sport', 'entertainment', 'business', 'politics'],\n",
       "      dtype='<U13')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Naive Bayes classifier\n",
    "clf4 = MultinomialNB()\n",
    "\n",
    "# Fit the classifier on the training data\n",
    "clf4.fit(X_train4, y_train4)\n",
    "\n",
    "# Predict the categories of the testing data\n",
    "y_pred4 = clf4.predict(X_test4)\n",
    "y_pred4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d8c9ca79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9651006711409396\n",
      "Confusion matrix:\n",
      " [[168   0   1   0   2]\n",
      " [  1 124   0   5   0]\n",
      " [  4   0 134   4   0]\n",
      " [  0   0   0 164   0]\n",
      " [  3   0   3   3 129]]\n",
      "Precision: 0.9684\n",
      "Recall: 0.9629\n",
      "F1 score: 0.9651\n"
     ]
    }
   ],
   "source": [
    "accuracy4 = accuracy_score(y_test4, y_pred4)\n",
    "\n",
    "# Print the accuracy score\n",
    "print('Accuracy:', accuracy4)\n",
    "\n",
    "# Print the confusion matrix\n",
    "confusion_matrix4 = confusion_matrix(y_test4, y_pred4)\n",
    "print('Confusion matrix:\\n', confusion_matrix4)\n",
    "\n",
    "\n",
    "precision4 = precision_score(y_test4, y_pred4, average='macro')\n",
    "recall4 = recall_score(y_test4, y_pred4, average='macro')\n",
    "f1_fourth = f1_score(y_test4, y_pred4, average='macro')\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(f'Precision: {precision4:.4f}')\n",
    "print(f'Recall: {recall4:.4f}')\n",
    "print(f'F1 score: {f1_fourth:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7ff3abc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: PrettyTable in d:\\anaconda\\lib\\site-packages (3.6.0)\n",
      "Requirement already satisfied: wcwidth in d:\\anaconda\\lib\\site-packages (from PrettyTable) (0.2.5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -ip (d:\\anaconda\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\anaconda\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\anaconda\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\anaconda\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\anaconda\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\anaconda\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install PrettyTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c690cc4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------------------------------------------------------------------------------+\n",
      "|                             Comparison Among different Splits                              |\n",
      "+--------+--------------------+--------------------+--------------------+--------------------+\n",
      "| Splits |      Accuracy      |     Precision      |       Recall       |      F1-score      |\n",
      "+--------+--------------------+--------------------+--------------------+--------------------+\n",
      "| 70:30  | 0.970917225950783  | 0.9717560217560217 | 0.9691219398977251 | 0.970230855958358  |\n",
      "| 80:20  | 0.959731543624161  | 0.9625114604424949 | 0.9580153494681231 | 0.9597864452798662 |\n",
      "| 60:40  | 0.9580536912751678 | 0.9602853980551822 | 0.9557449464752084 | 0.9572293239000491 |\n",
      "| 50:50  | 0.9651006711409396 | 0.968422190708948  | 0.9629493749447338 | 0.965126842151743  |\n",
      "+--------+--------------------+--------------------+--------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "# Comparision among different splits\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "myTable = PrettyTable()\n",
    "\n",
    "myTable.title='Comparison Among different Splits'\n",
    "myTable.add_column('Splits', ['70:30', '80:20', '60:40', '50:50'])\n",
    "myTable.add_column('Accuracy', [accuracy, accuracy2, accuracy3, accuracy4])\n",
    "myTable.add_column('Precision', [precision, precision2, precision3, precision4])\n",
    "myTable.add_column('Recall', [recall, recall2, recall3, recall4])\n",
    "myTable.add_column('F1-score', [f1, f1_second, f1_third, f1_fourth])\n",
    "\n",
    "print(myTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d216381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrices:\n",
      "\n",
      "70:30\n",
      "  [[105   0   1   0   2]\n",
      " [  1  75   1   2   0]\n",
      " [  2   0  83   1   0]\n",
      " [  0   0   0 101   0]\n",
      " [  0   0   3   0  70]]\n",
      "80:20\n",
      "  [[73  0  1  0  1]\n",
      " [ 1 44  1  0  0]\n",
      " [ 2  0 53  1  0]\n",
      " [ 0  0  0 63  0]\n",
      " [ 1  0  3  1 53]]\n",
      "60:40\n",
      "  [[134   0   1   0   2]\n",
      " [  1 102   2   4   0]\n",
      " [  2   0 104   3   0]\n",
      " [  0   0   0 129   0]\n",
      " [  2   0   5   3 102]]\n",
      "50:50\n",
      "  [[168   0   1   0   2]\n",
      " [  1 124   0   5   0]\n",
      " [  4   0 134   4   0]\n",
      " [  0   0   0 164   0]\n",
      " [  3   0   3   3 129]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrices of all splits\n",
    "print('Confusion matrices:\\n')\n",
    "print('70:30\\n ', confusion_matrix1)\n",
    "print('80:20\\n ',confusion_matrix2)\n",
    "print('60:40\\n ', confusion_matrix3)\n",
    "print('50:50\\n ', confusion_matrix4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5dcbeef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category frequency:\n",
      " sport            0.234899\n",
      "business         0.218600\n",
      "entertainment    0.186002\n",
      "tech             0.180249\n",
      "politics         0.180249\n",
      "Name: Category, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the frequency of each category in the training set\n",
    "category_freq = y_train.value_counts(normalize=True)\n",
    "\n",
    "# Print the frequency of each category\n",
    "print('Category frequency:\\n', category_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b38b95d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "1       german business confidence slide german busine...\n",
       "2       bbc poll indicates economic gloom citizen majo...\n",
       "3       lifestyle governs mobile choice faster better ...\n",
       "4       enron boss 168m payout eighteen former enron d...\n",
       "                              ...                        \n",
       "1485    double eviction big brother model caprice holb...\n",
       "1486    dj double act revamp chart show dj duo jk joel...\n",
       "1487    weak dollar hit reuters revenue medium group r...\n",
       "1488    apple ipod family expands market apple expande...\n",
       "1489    santy worm make unwelcome visit thousand websi...\n",
       "Name: Text, Length: 1490, dtype: object"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data['Text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bce40d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "701     entertainment\n",
       "1142             tech\n",
       "490              tech\n",
       "10           politics\n",
       "147          business\n",
       "            ...      \n",
       "1130         politics\n",
       "1294         business\n",
       "860          politics\n",
       "1459    entertainment\n",
       "1126            sport\n",
       "Name: Category, Length: 1043, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d3bbcc1",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1043 rows × 22194 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       00       000  0001  000bn  000m  000th  001  001and  001st  0051  ...  \\\n",
       "701   0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "1142  0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "490   0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "10    0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "147   0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "...   ...       ...   ...    ...   ...    ...  ...     ...    ...   ...  ...   \n",
       "1130  0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "1294  0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "860   0.0  0.031895   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "1459  0.0  0.036481   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "1126  0.0  0.000000   0.0    0.0   0.0    0.0  0.0     0.0    0.0   0.0  ...   \n",
       "\n",
       "      zombie  zone  zonealarm  zoom  zooropa  zorro  zuluaga  zurich  zutons  \\\n",
       "701      0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "1142     0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "490      0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "10       0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "147      0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "...      ...   ...        ...   ...      ...    ...      ...     ...     ...   \n",
       "1130     0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "1294     0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "860      0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "1459     0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "1126     0.0   0.0        0.0   0.0      0.0    0.0      0.0     0.0     0.0   \n",
       "\n",
       "      zvonareva  \n",
       "701         0.0  \n",
       "1142        0.0  \n",
       "490         0.0  \n",
       "10          0.0  \n",
       "147         0.0  \n",
       "...         ...  \n",
       "1130        0.0  \n",
       "1294        0.0  \n",
       "860         0.0  \n",
       "1459        0.0  \n",
       "1126        0.0  \n",
       "\n",
       "[1043 rows x 22194 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e08769c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entertainment: said - 0.0279\n",
      "entertainment: oscar - 0.0284\n",
      "entertainment: music - 0.0294\n",
      "entertainment: band - 0.0312\n",
      "entertainment: actor - 0.0317\n",
      "entertainment: star - 0.0327\n",
      "entertainment: show - 0.0343\n",
      "entertainment: best - 0.0455\n",
      "entertainment: award - 0.0459\n",
      "entertainment: film - 0.0758\n",
      "tech: computer - 0.0310\n",
      "tech: software - 0.0318\n",
      "tech: user - 0.0323\n",
      "tech: service - 0.0352\n",
      "tech: technology - 0.0353\n",
      "tech: game - 0.0354\n",
      "tech: said - 0.0354\n",
      "tech: people - 0.0403\n",
      "tech: phone - 0.0471\n",
      "tech: mobile - 0.0568\n",
      "politics: minister - 0.0390\n",
      "politics: tory - 0.0399\n",
      "politics: would - 0.0409\n",
      "politics: government - 0.0424\n",
      "politics: party - 0.0458\n",
      "politics: blair - 0.0518\n",
      "politics: labour - 0.0519\n",
      "politics: said - 0.0601\n",
      "politics: election - 0.0603\n",
      "politics: mr - 0.0796\n",
      "business: mr - 0.0249\n",
      "business: share - 0.0260\n",
      "business: market - 0.0263\n",
      "business: year - 0.0267\n",
      "business: economy - 0.0273\n",
      "business: growth - 0.0273\n",
      "business: bank - 0.0277\n",
      "business: company - 0.0311\n",
      "business: firm - 0.0331\n",
      "business: said - 0.0402\n",
      "sport: chelsea - 0.0225\n",
      "sport: team - 0.0231\n",
      "sport: champion - 0.0238\n",
      "sport: cup - 0.0240\n",
      "sport: match - 0.0263\n",
      "sport: said - 0.0266\n",
      "sport: win - 0.0286\n",
      "sport: player - 0.0288\n",
      "sport: england - 0.0368\n",
      "sport: game - 0.0376\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import numpy as np\n",
    "# Initialize the TF-IDF vectorizer\n",
    "tfidf = TfidfVectorizer()\n",
    "\n",
    "# Calculate the TF-IDF values for each feature in the training set\n",
    "X_train_tfidf = tfidf.fit_transform(data['Text'])\n",
    "\n",
    "# Loop over the unique categories in the training set\n",
    "for category in y_train.unique():\n",
    "    # Get the indices of the training samples that belong to this category\n",
    "    category_indices = y_train[y_train == category].index\n",
    "    # Calculate the average TF-IDF value for each feature in documents belonging to this category\n",
    "    avg_tfidf = X_train_tfidf[category_indices].mean(axis=0)\n",
    "    # Convert the average TF-IDF value to a 1D array\n",
    "    avg_tfidf = np.squeeze(np.asarray(avg_tfidf))\n",
    "    # Print the feature names and their corresponding average TF-IDF values\n",
    "    feature_names = tfidf.get_feature_names()\n",
    "    for feature_idx in np.argsort(avg_tfidf)[-10:]:\n",
    "        feature_name = feature_names[feature_idx]\n",
    "        feature_tfidf = avg_tfidf[feature_idx]\n",
    "        print(f'{category}: {feature_name} - {feature_tfidf:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1718c965",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = df_train.drop(columns=['ArticleId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c2a86133",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of stopwords and punctuation marks\n",
    "stopwords_list = stopwords.words('english') + list(string.punctuation)\n",
    "\n",
    "# Initialize the WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# function to clean and tokenize the text\n",
    "def clean_tokenize_text(text):\n",
    "    # Convert the text to lowercase\n",
    "    text = text.lower()\n",
    "\n",
    "    # Tokenize the text\n",
    "    words = word_tokenize(text)\n",
    "\n",
    "    # Remove the stopwords and punctuation marks\n",
    "    words = [word for word in words if word not in stopwords_list]\n",
    "\n",
    "    # Lemmatize the words\n",
    "    words = [lemmatizer.lemmatize(word) for word in words]\n",
    "\n",
    "    return words\n",
    "\n",
    "# Apply the clean_tokenize_text function to the 'Text' column\n",
    "data2['Text'] = data2['Text'].apply(clean_tokenize_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d9642c",
   "metadata": {},
   "source": [
    "### Using Ngram-feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b1cbc6eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "ngram_range = (1,2)\n",
    "\n",
    "# Convert the list of words back to a string for the TfidfVectorizer\n",
    "data2['Text'] = data2['Text'].apply(' '.join)\n",
    "\n",
    "# Initialize the TfidfVectorizer\n",
    "vectorizer2 = TfidfVectorizer(ngram_range = ngram_range)\n",
    "\n",
    "# Fit and transform the vectorizer on the 'Text' column\n",
    "tfidf_matrix2 = vectorizer2.fit_transform(data2['Text'])\n",
    "\n",
    "# Convert the sparse matrix to a dense matrix\n",
    "tfidf_matrix2 = tfidf_matrix2.toarray()\n",
    "\n",
    "# Create a DataFrame from the dense matrix\n",
    "tfidf_df2 = pd.DataFrame(tfidf_matrix2, columns=vectorizer2.get_feature_names())\n",
    "\n",
    "# Add the 'Category' column to the DataFrame\n",
    "tfidf_df2['Category'] = data2['Category']\n",
    "\n",
    "# Group the DataFrame by category and calculate the mean of each feature\n",
    "tficf_df2 = tfidf_df2.groupby('Category').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "642d6646",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train5, X_test5, y_train5, y_test5 = train_test_split(tfidf_df2.drop('Category', axis=1), tfidf_df2['Category'], test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1078db8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the Naive Bayes classifier\n",
    "clf5 = MultinomialNB()\n",
    "\n",
    "# Fit the classifier on the training data\n",
    "clf5.fit(X_train5, y_train5)\n",
    "\n",
    "# Predict the categories of the testing data\n",
    "y_pred5 = clf5.predict(X_test5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cefdff7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9574944071588367\n",
      "Confusion matrix:\n",
      " [[104   0   2   0   2]\n",
      " [  2  72   0   5   0]\n",
      " [  2   0  81   1   2]\n",
      " [  0   0   0 101   0]\n",
      " [  0   0   2   1  70]]\n",
      "Precision: 0.9594\n",
      "Recall: 0.9550\n",
      "F1 score: 0.9566\n"
     ]
    }
   ],
   "source": [
    "accuracy5 = accuracy_score(y_test5, y_pred5)\n",
    "\n",
    "# Print the accuracy score\n",
    "print('Accuracy:', accuracy5)\n",
    "\n",
    "# Print the confusion matrix\n",
    "confusion_matrix5 = confusion_matrix(y_test5, y_pred5)\n",
    "print('Confusion matrix:\\n', confusion_matrix5)\n",
    "\n",
    "\n",
    "precision5 = precision_score(y_test5, y_pred5, average='macro')\n",
    "recall5 = recall_score(y_test5, y_pred5, average='macro')\n",
    "f1_fifth = f1_score(y_test5, y_pred5, average='macro')\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(f'Precision: {precision5:.4f}')\n",
    "print(f'Recall: {recall5:.4f}')\n",
    "print(f'F1 score: {f1_fifth:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "91d9de3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------------------------------------------------------------------------------+\n",
      "|                             Comparison Among different Splits                              |\n",
      "+--------+--------------------+--------------------+--------------------+--------------------+\n",
      "| Splits |      Accuracy      |     Precision      |       Recall       |      F1-score      |\n",
      "+--------+--------------------+--------------------+--------------------+--------------------+\n",
      "| 70:30  | 0.970917225950783  | 0.9717560217560217 | 0.9691219398977251 | 0.970230855958358  |\n",
      "| 80:20  | 0.959731543624161  | 0.9625114604424949 | 0.9580153494681231 | 0.9597864452798662 |\n",
      "| 60:40  | 0.9580536912751678 | 0.9602853980551822 | 0.9557449464752084 | 0.9572293239000491 |\n",
      "| 50:50  | 0.9651006711409396 | 0.968422190708948  | 0.9629493749447338 | 0.965126842151743  |\n",
      "| N-gram | 0.9574944071588367 | 0.9594070541129364 | 0.9550239885463148 |  0.9565723795072   |\n",
      "+--------+--------------------+--------------------+--------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "# Comparision among different splits\n",
    "myTable1 = PrettyTable()\n",
    "\n",
    "myTable1.title='Comparison Among different Splits'\n",
    "myTable1.add_column('Splits', ['70:30', '80:20', '60:40', '50:50', 'N-gram'])\n",
    "myTable1.add_column('Accuracy', [accuracy, accuracy2, accuracy3, accuracy4, accuracy5])\n",
    "myTable1.add_column('Precision', [precision, precision2, precision3, precision4, precision5])\n",
    "myTable1.add_column('Recall', [recall, recall2, recall3, recall4, recall5])\n",
    "myTable1.add_column('F1-score', [f1, f1_second, f1_third, f1_fourth, f1_fifth])\n",
    "\n",
    "print(myTable1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "da6ffc61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrices:\n",
      "\n",
      "70:30\n",
      "  [[105   0   1   0   2]\n",
      " [  1  75   1   2   0]\n",
      " [  2   0  83   1   0]\n",
      " [  0   0   0 101   0]\n",
      " [  0   0   3   0  70]]\n",
      "80:20\n",
      "  [[73  0  1  0  1]\n",
      " [ 1 44  1  0  0]\n",
      " [ 2  0 53  1  0]\n",
      " [ 0  0  0 63  0]\n",
      " [ 1  0  3  1 53]]\n",
      "60:40\n",
      "  [[134   0   1   0   2]\n",
      " [  1 102   2   4   0]\n",
      " [  2   0 104   3   0]\n",
      " [  0   0   0 129   0]\n",
      " [  2   0   5   3 102]]\n",
      "50:50\n",
      "  [[168   0   1   0   2]\n",
      " [  1 124   0   5   0]\n",
      " [  4   0 134   4   0]\n",
      " [  0   0   0 164   0]\n",
      " [  3   0   3   3 129]]\n",
      "N-gram\n",
      "  [[104   0   2   0   2]\n",
      " [  2  72   0   5   0]\n",
      " [  2   0  81   1   2]\n",
      " [  0   0   0 101   0]\n",
      " [  0   0   2   1  70]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrices of all splits\n",
    "print('Confusion matrices:\\n')\n",
    "print('70:30\\n ', confusion_matrix1)\n",
    "print('80:20\\n ',confusion_matrix2)\n",
    "print('60:40\\n ', confusion_matrix3)\n",
    "print('50:50\\n ', confusion_matrix4)\n",
    "print('N-gram\\n ', confusion_matrix5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d5ccbfe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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