news_clustering_unsupervised_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing all the necessary Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "AadPNXaono4u"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "import string\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn_extra.cluster import KMedoids\n",
    "import scipy.cluster.hierarchy as shc\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.metrics import silhouette_score\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "71cRYfP1oTKI"
   },
   "outputs": [],
   "source": [
    "news_data = pd.read_table('./news_data.tsv', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 684
    },
    "id": "25YTR2Dzob12",
    "outputId": "bfd484b4-9329-4cbd-c5c5-7300ee666e94"
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   "outputs": [
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       "      <td>Denver</td>\n",
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       "      <td>1123.json</td>\n",
       "      <td>false</td>\n",
       "      <td>Health care reform legislation is likely to ma...</td>\n",
       "      <td>health-care</td>\n",
       "      <td>blog-posting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>a news release</td>\n",
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       "      <td>9028.json</td>\n",
       "      <td>half-true</td>\n",
       "      <td>The economic turnaround started at the end of ...</td>\n",
       "      <td>economy,jobs</td>\n",
       "      <td>charlie-crist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Florida</td>\n",
       "      <td>democrat</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>an interview on CNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12465.json</td>\n",
       "      <td>true</td>\n",
       "      <td>The Chicago Bears have had more starting quart...</td>\n",
       "      <td>education</td>\n",
       "      <td>robin-vos</td>\n",
       "      <td>Wisconsin Assembly speaker</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>republican</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>a an online opinion-piece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10234</th>\n",
       "      <td>5473.json</td>\n",
       "      <td>mostly-true</td>\n",
       "      <td>There are a larger number of shark attacks in ...</td>\n",
       "      <td>animals,elections</td>\n",
       "      <td>aclu-florida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Florida</td>\n",
       "      <td>none</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>interview on \"The Colbert Report\"</td>\n",
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       "    <tr>\n",
       "      <th>10235</th>\n",
       "      <td>3408.json</td>\n",
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       "      <td>Democrats have now become the party of the [At...</td>\n",
       "      <td>elections</td>\n",
       "      <td>alan-powell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>republican</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>an interview</td>\n",
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       "      <td>3959.json</td>\n",
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       "      <td>retirement,social-security</td>\n",
       "      <td>herman-cain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>republican</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>a Republican presidential debate</td>\n",
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       "      <th>10237</th>\n",
       "      <td>2253.json</td>\n",
       "      <td>false</td>\n",
       "      <td>On lifting the U.S. Cuban embargo and allowing...</td>\n",
       "      <td>florida,foreign-policy</td>\n",
       "      <td>jeff-greene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Florida</td>\n",
       "      <td>democrat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a televised debate on Miami's WPLG-10 against ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10238</th>\n",
       "      <td>1155.json</td>\n",
       "      <td>pants-fire</td>\n",
       "      <td>The Department of Veterans Affairs has a manua...</td>\n",
       "      <td>health-care,veterans</td>\n",
       "      <td>michael-steele</td>\n",
       "      <td>chairman of the Republican National Committee</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>republican</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>a Fox News interview</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10239 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        2635.json        false  \\\n",
       "0      10540.json    half-true   \n",
       "1        324.json  mostly-true   \n",
       "2       1123.json        false   \n",
       "3       9028.json    half-true   \n",
       "4      12465.json         true   \n",
       "...           ...          ...   \n",
       "10234   5473.json  mostly-true   \n",
       "10235   3408.json  mostly-true   \n",
       "10236   3959.json    half-true   \n",
       "10237   2253.json        false   \n",
       "10238   1155.json   pants-fire   \n",
       "\n",
       "      Says the Annies List political group supports third-trimester abortions on demand.  \\\n",
       "0      When did the decline of coal start? It started...                                   \n",
       "1      Hillary Clinton agrees with John McCain \"by vo...                                   \n",
       "2      Health care reform legislation is likely to ma...                                   \n",
       "3      The economic turnaround started at the end of ...                                   \n",
       "4      The Chicago Bears have had more starting quart...                                   \n",
       "...                                                  ...                                   \n",
       "10234  There are a larger number of shark attacks in ...                                   \n",
       "10235  Democrats have now become the party of the [At...                                   \n",
       "10236  Says an alternative to Social Security that op...                                   \n",
       "10237  On lifting the U.S. Cuban embargo and allowing...                                   \n",
       "10238  The Department of Veterans Affairs has a manua...                                   \n",
       "\n",
       "                                 abortion    dwayne-bohac  \\\n",
       "0      energy,history,job-accomplishments  scott-surovell   \n",
       "1                          foreign-policy    barack-obama   \n",
       "2                             health-care    blog-posting   \n",
       "3                            economy,jobs   charlie-crist   \n",
       "4                               education       robin-vos   \n",
       "...                                   ...             ...   \n",
       "10234                   animals,elections    aclu-florida   \n",
       "10235                           elections     alan-powell   \n",
       "10236          retirement,social-security     herman-cain   \n",
       "10237              florida,foreign-policy     jeff-greene   \n",
       "10238                health-care,veterans  michael-steele   \n",
       "\n",
       "                                State representative      Texas  republican  \\\n",
       "0                                     State delegate   Virginia    democrat   \n",
       "1                                          President   Illinois    democrat   \n",
       "2                                                NaN        NaN        none   \n",
       "3                                                NaN    Florida    democrat   \n",
       "4                         Wisconsin Assembly speaker  Wisconsin  republican   \n",
       "...                                              ...        ...         ...   \n",
       "10234                                            NaN    Florida        none   \n",
       "10235                                            NaN    Georgia  republican   \n",
       "10236                                            NaN    Georgia  republican   \n",
       "10237                                            NaN    Florida    democrat   \n",
       "10238  chairman of the Republican National Committee   Maryland  republican   \n",
       "\n",
       "          0     1    0.1    0.2   0.3  \\\n",
       "0       0.0   0.0    1.0    1.0   0.0   \n",
       "1      70.0  71.0  160.0  163.0   9.0   \n",
       "2       7.0  19.0    3.0    5.0  44.0   \n",
       "3      15.0   9.0   20.0   19.0   2.0   \n",
       "4       0.0   3.0    2.0    5.0   1.0   \n",
       "...     ...   ...    ...    ...   ...   \n",
       "10234   0.0   1.0    1.0    1.0   0.0   \n",
       "10235   0.0   0.0    0.0    1.0   0.0   \n",
       "10236   4.0  11.0    5.0    3.0   3.0   \n",
       "10237   3.0   1.0    3.0    0.0   0.0   \n",
       "10238   0.0   1.0    1.0    0.0   2.0   \n",
       "\n",
       "                                                a mailer  \n",
       "0                                        a floor speech.  \n",
       "1                                                 Denver  \n",
       "2                                         a news release  \n",
       "3                                    an interview on CNN  \n",
       "4                              a an online opinion-piece  \n",
       "...                                                  ...  \n",
       "10234                  interview on \"The Colbert Report\"  \n",
       "10235                                       an interview  \n",
       "10236                   a Republican presidential debate  \n",
       "10237  a televised debate on Miami's WPLG-10 against ...  \n",
       "10238                               a Fox News interview  \n",
       "\n",
       "[10239 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping all the un-necessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "UwuFAO8Yp1mq"
   },
   "outputs": [],
   "source": [
    "news_data.drop(columns=['2635.json', 'abortion', 'dwayne-bohac', 'State representative', 'Texas', 'republican', '0', '1', '0.1', '0.2', '0.3', 'a mailer'], inplace=True)"
   ]
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   "execution_count": 5,
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    },
    "id": "MhavkeJFqm-S",
    "outputId": "b6cbf807-1f3e-49cd-9d01-21844328b46f"
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   "outputs": [
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       "         false  \\\n",
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       "  Says the Annies List political group supports third-trimester abortions on demand.  \n",
       "0  When did the decline of coal start? It started...                                  \n",
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    "news_data.columns = ['label', 'news_text']"
   ]
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   "cell_type": "code",
   "execution_count": 7,
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       "         label                                          news_text\n",
       "0    half-true  When did the decline of coal start? It started...\n",
       "1  mostly-true  Hillary Clinton agrees with John McCain \"by vo...\n",
       "2        false  Health care reform legislation is likely to ma...\n",
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   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z6LcILs-uunN",
    "outputId": "61a752af-6415-4621-f7da-09f9fd849576"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10239, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for null values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nPxD4ni6u0Qc",
    "outputId": "22c16607-c15c-4157-cbb6-68ced2b4f1c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label        0\n",
       "news_text    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting the total number of labels present in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "FgkcFFQMsvcb"
   },
   "outputs": [],
   "source": [
    "each_label_count = news_data['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1nt4_ShMuRNq",
    "outputId": "8273af7b-6075-48ed-b62a-a961561c4cde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "half-true      2114\n",
       "false          1994\n",
       "mostly-true    1962\n",
       "true           1676\n",
       "barely-true    1654\n",
       "pants-fire      839\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "each_label_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a balanced dataset of 204 datapoints in total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "news_data_half_true_only = news_data[news_data['label'] == 'half-true']\n",
    "news_data_false_only = news_data[news_data['label'] == 'false']\n",
    "news_data_mostly_true_only = news_data[news_data['label'] == 'mostly-true']\n",
    "news_data_true_only = news_data[news_data['label'] == 'true']\n",
    "news_data_barely_true_only = news_data[news_data['label'] == 'barely-true']\n",
    "news_data_pants_fire_only = news_data[news_data['label'] == 'pants-fire']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "news_data_half_true_only_sample = news_data_half_true_only[0:34]\n",
    "news_data_false_only_sample = news_data_false_only[0:34]\n",
    "news_data_mostly_true_only_sample = news_data_mostly_true_only[0:34]\n",
    "news_data_true_only_sample = news_data_true_only[0:34]\n",
    "news_data_barely_true_only_sample = news_data_barely_true_only[0:34]\n",
    "news_data_pants_fire_only_sample = news_data_pants_fire_only[0:34]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_news_dataset = pd.concat([news_data_half_true_only_sample, news_data_false_only_sample, news_data_mostly_true_only_sample, news_data_true_only_sample, news_data_barely_true_only_sample, news_data_pants_fire_only_sample], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>label</th>\n",
       "      <th>news_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>half-true</td>\n",
       "      <td>When did the decline of coal start? It started...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>half-true</td>\n",
       "      <td>The economic turnaround started at the end of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>half-true</td>\n",
       "      <td>I'm the only person on this stage who has work...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>half-true</td>\n",
       "      <td>However, it took $19.5 million in Oregon Lotte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>half-true</td>\n",
       "      <td>Since 2000, nearly 12 million Americans have s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        label                                          news_text\n",
       "0   half-true  When did the decline of coal start? It started...\n",
       "3   half-true  The economic turnaround started at the end of ...\n",
       "6   half-true  I'm the only person on this stage who has work...\n",
       "7   half-true  However, it took $19.5 million in Oregon Lotte...\n",
       "10  half-true  Since 2000, nearly 12 million Americans have s..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_news_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_news_dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing all the news texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ewfULazm5SGC",
    "outputId": "96bbcecc-f4f6-45ca-9518-06dd8c07e85f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Somenath\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rKX2tUh75eJt",
    "outputId": "34dff77d-ee16-4171-823b-03e3dba4c64d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Somenath\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "LZJRZ_OD5giS"
   },
   "outputs": [],
   "source": [
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "LM5ZatSm5k2X"
   },
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "\n",
    "  text = text.lower()\n",
    "\n",
    "  text = nltk.word_tokenize(text)\n",
    "\n",
    "  word_arr = []\n",
    "\n",
    "  for i in text:\n",
    "    if i.isalnum():\n",
    "      word_arr.append(i)\n",
    "\n",
    "  text = word_arr[:]\n",
    "\n",
    "  word_arr.clear()\n",
    "\n",
    "  for i in text:\n",
    "    if i not in stopwords.words('english') and i not in string.punctuation:\n",
    "      word_arr.append(i)\n",
    "\n",
    "  text = word_arr[:]\n",
    "\n",
    "  word_arr.clear()\n",
    "\n",
    "  for i in text:\n",
    "    word_arr.append(ps.stem(i))\n",
    "\n",
    "  return \" \".join(word_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a new column in the dataset and inserting all the pre-proccessed texts inside it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "5UTwlv3p58Qh"
   },
   "outputs": [],
   "source": [
    "new_news_dataset['preprocessed_news_text'] = new_news_dataset['news_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "RFtihLpR6RxD",
    "outputId": "698e6a11-52a8-4c75-8367-c71ee5b15fb7"
   },
   "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>label</th>\n",
       "      <th>news_text</th>\n",
       "      <th>preprocessed_news_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>half-true</td>\n",
       "      <td>When did the decline of coal start? It started...</td>\n",
       "      <td>declin coal start start natur ga took start be...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>half-true</td>\n",
       "      <td>The economic turnaround started at the end of ...</td>\n",
       "      <td>econom turnaround start end term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>half-true</td>\n",
       "      <td>I'm the only person on this stage who has work...</td>\n",
       "      <td>person stage work activ last year pass along r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>half-true</td>\n",
       "      <td>However, it took $19.5 million in Oregon Lotte...</td>\n",
       "      <td>howev took million oregon lotteri fund port ne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>half-true</td>\n",
       "      <td>Since 2000, nearly 12 million Americans have s...</td>\n",
       "      <td>sinc 2000 nearli 12 million american slip midd...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        label                                          news_text  \\\n",
       "0   half-true  When did the decline of coal start? It started...   \n",
       "3   half-true  The economic turnaround started at the end of ...   \n",
       "6   half-true  I'm the only person on this stage who has work...   \n",
       "7   half-true  However, it took $19.5 million in Oregon Lotte...   \n",
       "10  half-true  Since 2000, nearly 12 million Americans have s...   \n",
       "\n",
       "                               preprocessed_news_text  \n",
       "0   declin coal start start natur ga took start be...  \n",
       "3                    econom turnaround start end term  \n",
       "6   person stage work activ last year pass along r...  \n",
       "7   howev took million oregon lotteri fund port ne...  \n",
       "10  sinc 2000 nearli 12 million american slip midd...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_news_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping the plain un-proccessed 'news_text' and the 'label' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "8UGQqywr6cSW"
   },
   "outputs": [],
   "source": [
    "new_news_dataset.drop(columns=['news_text', 'label'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "3A-hUV7ekuvM",
    "outputId": "3911a07c-fe33-49cc-a9a1-983383b61714"
   },
   "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>preprocessed_news_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>declin coal start start natur ga took start be...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>econom turnaround start end term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>person stage work activ last year pass along r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>howev took million oregon lotteri fund port ne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sinc 2000 nearli 12 million american slip midd...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               preprocessed_news_text\n",
       "0   declin coal start start natur ga took start be...\n",
       "3                    econom turnaround start end term\n",
       "6   person stage work activ last year pass along r...\n",
       "7   howev took million oregon lotteri fund port ne...\n",
       "10  sinc 2000 nearli 12 million american slip midd..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_news_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializing TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "autsDgl06jF2"
   },
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorizing all the pre-proccessed news texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "N1D-GJg08ExD"
   },
   "outputs": [],
   "source": [
    "X = vectorizer.fit_transform(new_news_dataset['preprocessed_news_text']).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Principal Component Analysis(PCA) on the vectorized text to reduce it from high dimension to low dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing k-means algorithm and finding the optimal number of clusters for k-means algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K6EsC1tb8Q1l",
    "outputId": "a0d49966-73d8-41a4-d394-9efbe7af949d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "\n",
    "  kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
    "\n",
    "  kmeans.fit(X)\n",
    "\n",
    "  wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "ZFSTOxQQBOtB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.213262416759263,\n",
       " 3.480173567965202,\n",
       " 2.0397436362091304,\n",
       " 1.561084035474699,\n",
       " 1.2661923890496474,\n",
       " 1.0048428509859741,\n",
       " 0.8756050402702884,\n",
       " 0.7543980346494221,\n",
       " 0.6596261435684572,\n",
       " 0.5657216613471671]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the elbow point graph of the k-means algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "B6KbWIGRCNkG"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "\n",
    "plt.plot(range(1, 11), wcss)\n",
    "\n",
    "plt.title('The Elbow Point Graph')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this elbow graph, we can see that after n_clusters=6, the graph is getting stabilized. Therefore, the optimal number of clusters for k-means is 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model with the optimal number of clusters found in the elbow point graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "n1N_inlAdXyC"
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=6, init='k-means++', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "Sb8vJIvdf1CI",
    "outputId": "721dcbdb-6a74-4861-e2fa-e6bef6f18316"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, 3, 3, 2, 2, 0, 3, 0, 2, 3, 2, 3, 3, 0, 1, 1, 4, 1, 3, 2,\n",
       "       3, 0, 5, 0, 1, 3, 3, 1, 1, 4, 5, 4, 0, 3, 3, 3, 0, 1, 0, 3, 4, 0,\n",
       "       1, 2, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 5, 1, 3, 3, 3, 4, 0, 3, 3, 3,\n",
       "       3, 1, 3, 0, 3, 3, 2, 5, 3, 3, 2, 2, 2, 2, 3, 3, 0, 3, 0, 0, 3, 0,\n",
       "       3, 1, 3, 0, 3, 1, 3, 3, 2, 0, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 1, 3,\n",
       "       5, 0, 1, 1, 2, 0, 0, 3, 1, 3, 5, 3, 5, 0, 3, 0, 3, 3, 3, 5, 1, 0,\n",
       "       3, 2, 3, 0, 3, 3, 3, 0, 5, 0, 3, 3, 3, 3, 3, 5, 0, 3, 1, 0, 5, 5,\n",
       "       3, 2, 0, 0, 3, 4, 1, 0, 2, 5, 0, 3, 3, 1, 0, 0, 3, 0, 0, 3, 1, 3,\n",
       "       5, 0, 1, 1, 1, 3, 3, 0, 3, 0, 3, 1, 3, 3, 3, 3, 3, 0, 0, 3, 2, 3,\n",
       "       3, 3, 3, 3, 3, 0])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "tu7hzu-igosA"
   },
   "outputs": [],
   "source": [
    "kmeans_labels = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "lrsqgmsJgtpF",
    "outputId": "a2ebbc48-3425-42a7-b764-e58411a27297"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, 3, 3, 2, 2, 0, 3, 0, 2, 3, 2, 3, 3, 0, 1, 1, 4, 1, 3, 2,\n",
       "       3, 0, 5, 0, 1, 3, 3, 1, 1, 4, 5, 4, 0, 3, 3, 3, 0, 1, 0, 3, 4, 0,\n",
       "       1, 2, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 5, 1, 3, 3, 3, 4, 0, 3, 3, 3,\n",
       "       3, 1, 3, 0, 3, 3, 2, 5, 3, 3, 2, 2, 2, 2, 3, 3, 0, 3, 0, 0, 3, 0,\n",
       "       3, 1, 3, 0, 3, 1, 3, 3, 2, 0, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 1, 3,\n",
       "       5, 0, 1, 1, 2, 0, 0, 3, 1, 3, 5, 3, 5, 0, 3, 0, 3, 3, 3, 5, 1, 0,\n",
       "       3, 2, 3, 0, 3, 3, 3, 0, 5, 0, 3, 3, 3, 3, 3, 5, 0, 3, 1, 0, 5, 5,\n",
       "       3, 2, 0, 0, 3, 4, 1, 0, 2, 5, 0, 3, 3, 1, 0, 0, 3, 0, 0, 3, 1, 3,\n",
       "       5, 0, 1, 1, 1, 3, 3, 0, 3, 0, 3, 1, 3, 3, 3, 3, 3, 0, 0, 3, 2, 3,\n",
       "       3, 3, 3, 3, 3, 0])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the clusters as a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Kmeans Clustering')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=kmeans_labels, cmap='rainbow')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='centroid')\n",
    "plt.title('Kmeans Clustering')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the quality of the cluster of kmeans with the help of silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_score_kmeans = silhouette_score(X, kmeans_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42312189249800153"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score_kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZKRYASgIc_z8"
   },
   "source": [
    "## Implementing K-medoids and finding the optimal number of clusters for k-medoids algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "bN4EEVcSHScE"
   },
   "outputs": [],
   "source": [
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "\n",
    "    kmedoids = KMedoids(n_clusters=i, init='k-medoids++', random_state=42)\n",
    "\n",
    "    kmedoids.fit(X)\n",
    "\n",
    "    wcss.append(kmedoids.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "zLnvQosnJ6n0",
    "outputId": "e1672bbd-3497-439e-c2bb-76559d82d276"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[25.138844385684887,\n",
       " 20.65000754108016,\n",
       " 17.769300006905688,\n",
       " 16.043030393665074,\n",
       " 12.856254154892808,\n",
       " 12.011514999893706,\n",
       " 11.01156904235392,\n",
       " 10.527313091796,\n",
       " 9.580472029363936,\n",
       " 9.228097988889935]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the elbow point graph of the k-medoids algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "7A4-zmeOJ_eH",
    "outputId": "cee8c916-ef85-4891-c162-67f3dcf1ad45"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "\n",
    "plt.plot(range(1, 11), wcss)\n",
    "\n",
    "plt.title('The Elbow Point Graph')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this elbow graph, we can see that after n_clusters=5, the graph is getting stabilized. Therefore, the optimal number of clusters for k-medoid is 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model with the optimal number of clusters found in the elbow point graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "IisTUiggh57Y"
   },
   "outputs": [],
   "source": [
    "kmedoids = KMedoids(n_clusters=5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "ECGJruv8h-Xe",
    "outputId": "3bbcdc80-708e-4589-e434-e52357c46fa7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 4, 3, 4, 4, 4, 1, 2, 2, 4, 3, 4, 3, 2, 2, 0, 0, 0, 0, 3, 4,\n",
       "       3, 1, 4, 1, 0, 3, 3, 0, 0, 0, 4, 0, 2, 3, 2, 3, 2, 0, 1, 2, 0, 1,\n",
       "       0, 4, 3, 1, 1, 1, 4, 3, 4, 3, 3, 1, 4, 0, 3, 3, 3, 0, 1, 3, 3, 3,\n",
       "       3, 3, 3, 2, 4, 3, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 1, 3, 1, 1, 3, 2,\n",
       "       3, 0, 3, 3, 4, 0, 3, 4, 4, 2, 4, 2, 3, 3, 3, 3, 1, 2, 3, 4, 0, 3,\n",
       "       4, 3, 3, 0, 4, 1, 2, 3, 3, 3, 4, 3, 0, 1, 3, 1, 3, 3, 3, 4, 0, 1,\n",
       "       3, 4, 3, 1, 3, 3, 3, 1, 4, 1, 3, 3, 3, 3, 3, 0, 1, 4, 3, 1, 4, 4,\n",
       "       3, 4, 2, 1, 3, 0, 0, 1, 4, 4, 1, 3, 3, 0, 2, 1, 3, 2, 1, 3, 0, 3,\n",
       "       4, 1, 0, 0, 0, 3, 3, 1, 3, 1, 2, 0, 2, 4, 3, 3, 4, 3, 1, 3, 4, 3,\n",
       "       3, 2, 3, 3, 3, 2], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmedoids.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "nTVkTUtTiCD8"
   },
   "outputs": [],
   "source": [
    "kmediod_labels = kmedoids.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "4h6jebXwie6g",
    "outputId": "bcbd3eae-cd8d-4f4f-d704-d7fc5a553e73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 4, 3, 4, 4, 4, 1, 2, 2, 4, 3, 4, 3, 2, 2, 0, 0, 0, 0, 3, 4,\n",
       "       3, 1, 4, 1, 0, 3, 3, 0, 0, 0, 4, 0, 2, 3, 2, 3, 2, 0, 1, 2, 0, 1,\n",
       "       0, 4, 3, 1, 1, 1, 4, 3, 4, 3, 3, 1, 4, 0, 3, 3, 3, 0, 1, 3, 3, 3,\n",
       "       3, 3, 3, 2, 4, 3, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 1, 3, 1, 1, 3, 2,\n",
       "       3, 0, 3, 3, 4, 0, 3, 4, 4, 2, 4, 2, 3, 3, 3, 3, 1, 2, 3, 4, 0, 3,\n",
       "       4, 3, 3, 0, 4, 1, 2, 3, 3, 3, 4, 3, 0, 1, 3, 1, 3, 3, 3, 4, 0, 1,\n",
       "       3, 4, 3, 1, 3, 3, 3, 1, 4, 1, 3, 3, 3, 3, 3, 0, 1, 4, 3, 1, 4, 4,\n",
       "       3, 4, 2, 1, 3, 0, 0, 1, 4, 4, 1, 3, 3, 0, 2, 1, 3, 2, 1, 3, 0, 3,\n",
       "       4, 1, 0, 0, 0, 3, 3, 1, 3, 1, 2, 0, 2, 4, 3, 3, 4, 3, 1, 3, 4, 3,\n",
       "       3, 2, 3, 3, 3, 2], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmediod_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the clusters as a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Kmedoid Clustering')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=kmediod_labels, cmap='rainbow')\n",
    "plt.scatter(kmedoids.cluster_centers_[:, 0], kmedoids.cluster_centers_[:, 1], s=100, c='black', label='centroid')\n",
    "plt.title('Kmedoid Clustering')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the quality of the cluster of kmedoid with the help of silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_score_kmedoid = silhouette_score(X, kmediod_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23025127510532378"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score_kmedoid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing Agglomerative Hierarical Clustering Algorithm and finding the optimal number of clusters for agglomerative hierarical clustering algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "6aYrJb6fpbbe",
    "outputId": "4666f031-aca2-4c28-ee25-fc36277b6d0f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "\n",
    "plt.title('News Dendogram')\n",
    "\n",
    "dend = shc.dendrogram(shc.linkage(X, method='ward'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By cutting the dendrogram horizontally along the longest line, we get 3 clusters, indicating that the optimal number of clusters for agglomerative clustering is 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model with the optimal number of clusters found from the dendogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "8-zLpP9fuC6R"
   },
   "outputs": [],
   "source": [
    "cluster_hierarcial = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "bZhBuED-uLqI",
    "outputId": "d0fc58f3-1476-4fcc-fc00-eda7ce826bd2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1,\n",
       "       0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,\n",
       "       2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 2,\n",
       "       0, 1, 0, 0, 0, 2, 0, 0, 1, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0,\n",
       "       2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_hierarcial.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "lWEX_mN2uiRX"
   },
   "outputs": [],
   "source": [
    "hiearachy_labels = cluster_hierarcial.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the clusters as a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Agglomerative Hierarical Clustering')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=hiearachy_labels, cmap='rainbow')\n",
    "plt.title('Agglomerative Hierarical Clustering')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the quality of the cluster of agglomerative hierarical with the help of silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_score_hierarchy = silhouette_score(X, hiearachy_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5236998723248727"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score_hierarchy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a table of the silhouette score of the three algorithm for better comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhoutte_score_df = pd.DataFrame({\n",
    "    'Algorithm': ['k-means Clustering', 'k-medoids Clustering', 'Agglomerative Hierarical Clustering'],\n",
    "    'Silhouette Score': [silhouette_score_kmeans, silhouette_score_kmedoid, silhouette_score_hierarchy]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Algorithm</th>\n",
       "      <th>Silhouette Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k-means Clustering</td>\n",
       "      <td>0.423122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k-medoids Clustering</td>\n",
       "      <td>0.230251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agglomerative Hierarical Clustering</td>\n",
       "      <td>0.523700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Algorithm  Silhouette Score\n",
       "0                   k-means Clustering          0.423122\n",
       "1                 k-medoids Clustering          0.230251\n",
       "2  Agglomerative Hierarical Clustering          0.523700"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhoutte_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Silhouette Score of each Algorithm')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "silhoutte_score_df.plot(kind='bar', x='Algorithm')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.title('Silhouette Score of each Algorithm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "From the table and the graph, it is clear that the silhouette score of Agglomerative Hierarical Clustering is highest among the three indicating that it is the best clustering algorithm for our use case.\n",
    "\n",
    "Therefore, for this project, THE OPTIMAL NUMBER OF CLUSTERS IS 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model with sample data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating some sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_true = 'The Chicago Bears have had more starting quarterbacks in the last 10 years than the total number of tenured (UW) faculty fired during the last two decades.'\n",
    "sample_data_false = 'Health care reform legislation is likely to mandate free sex change surgeries.'\n",
    "sample_data_half_true = 'When did the decline of coal start? It started when natural gas took off that started to begin in (President George W.) Bushs administration.'\n",
    "sample_data_mostly_true = 'Hillary Clinton agrees with John McCain \"by voting to give George Bush the benefit of the doubt on Iran.\"'\n",
    "sample_data_barely_true = 'Jim Dunnam has not lived in the district he represents for years now.'\n",
    "sample_data_pants_fire = 'In the case of a catastrophic event, the Atlanta-area offices of the Centers for Disease Control and Prevention will self-destruct.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing the sample datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_true_preproccessed = preprocess_text(sample_data_true)\n",
    "sample_data_false_preproccessed = preprocess_text(sample_data_false)\n",
    "sample_data_half_true_preproccessed = preprocess_text(sample_data_half_true)\n",
    "sample_data_mostly_true_preproccessed = preprocess_text(sample_data_mostly_true)\n",
    "sample_data_barely_true_preproccessed = preprocess_text(sample_data_barely_true)\n",
    "sample_data_pants_fire_preproccessed = preprocess_text(sample_data_pants_fire)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorizing the sample datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_true_vectorized = vectorizer.transform([sample_data_true_preproccessed]).toarray()\n",
    "sample_data_false_vectorized = vectorizer.transform([sample_data_false_preproccessed]).toarray()\n",
    "sample_data_half_true_vectorized = vectorizer.transform([sample_data_half_true_preproccessed]).toarray()\n",
    "sample_data_mostly_true_vectorized = vectorizer.transform([sample_data_mostly_true_preproccessed]).toarray()\n",
    "sample_data_barely_true_vectorized = vectorizer.transform([sample_data_barely_true_preproccessed]).toarray()\n",
    "sample_data_pants_fire_vectorized = vectorizer.transform([sample_data_pants_fire_preproccessed]).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying PCA on the sample datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_true_pca = pca.transform(sample_data_barely_true_vectorized)\n",
    "sample_data_false_pca = pca.transform(sample_data_false_vectorized)\n",
    "sample_data_half_true_pca = pca.transform(sample_data_half_true_vectorized)\n",
    "sample_data_mostly_true_pca = pca.transform(sample_data_mostly_true_vectorized)\n",
    "sample_data_barely_true_pca = pca.transform(sample_data_barely_true_vectorized)\n",
    "sample_data_pants_fire_pca = pca.transform(sample_data_pants_fire_vectorized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using K-Means Algorithm to make prediction on SIngle Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 3, init='k-means++', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 1, 1, 1, 1, 2, 0,\n",
       "       2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2,\n",
       "       1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 0, 1, 0, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 1, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2,\n",
       "       1, 2, 2, 1, 0, 2, 2, 2, 2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2,\n",
       "       2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 0, 1,\n",
       "       2, 0, 2, 2, 2, 1, 2, 2, 0, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2,\n",
       "       1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2,\n",
       "       2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_single_data = kmeans.predict(sample_data_false_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_single_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here, 2 means the above news belongs to cluster number 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Agglomerative Clustering to make prediction on Multiple Input(three only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data_one = 'The Chicago Bears have had more starting quarterbacks in the last 10 years than the total number of tenured (UW) faculty fired during the last two decades.' \n",
    "input_data_two = 'Jim Dunnam has not lived in the district he represents for years now.'\n",
    "input_data_three = 'Health care reform legislation is likely to mandate free sex change surgeries.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOTE: We can't take more than 3 inputs in the array because the number of clusters used to train the agglomerative clustering model is 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data_one_preproccessed = preprocess_text(input_data_one)\n",
    "input_data_two_preproccessed = preprocess_text(input_data_two)\n",
    "input_data_three_preproccessed = preprocess_text(input_data_three)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data_final = [input_data_one_preproccessed, input_data_two_preproccessed, input_data_three_preproccessed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiple_input_data_vectorized = vectorizer.transform(input_data_final).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiple_input_data_pca = pca.transform(multiple_input_data_vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Somenath\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "pred_multiple_data = cluster_hierarcial.fit_predict(multiple_input_data_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_multiple_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here, [2, 1, 0] means the first news belongs to cluster number 2, the second news belongs to cluster number 1 and the third news belongs to cluster number 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating pickle file of both the models, the pca and the vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(kmeans, open('modelkmeans.pkl', 'wb'))\n",
    "pickle.dump(cluster_hierarcial, open('modelhierarchy.pkl', 'wb'))\n",
    "pickle.dump(vectorizer, open('vectorizer.pkl', 'wb'))\n",
    "pickle.dump(pca, open('pca.pkl', 'wb'))"
   ]
  }
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