k means - headlines cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "53dd317a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4bf926e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/Acer/Desktop/guardian_headlines.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5de22a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3e26b77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = PorterStemmer()\n",
    "stemmed_dataset = []\n",
    "for i in range(0,500):\n",
    "    stemmed_array = df['Headlines'][i].split()\n",
    "    stemmed = [ps.stem(word) for word in stemmed_array if not word in set(stopwords.words('english'))]\n",
    "    stemmed = ' '.join(stemmed)\n",
    "    stemmed_dataset.append(stemmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "893257c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['‘I',\n",
       " 'now',\n",
       " 'fear',\n",
       " 'the',\n",
       " 'worst’:',\n",
       " 'four',\n",
       " 'grim',\n",
       " 'tales',\n",
       " 'of',\n",
       " 'working',\n",
       " 'life',\n",
       " 'upended',\n",
       " 'by',\n",
       " 'Covid-19']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Headlines'][1].split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "29e71004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'me', 'my', 'myself', 'we']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords.words('english')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e20bdb46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['johnson ask santa christma recoveri',\n",
       " '‘i fear worst’: four grim tale work life upend covid-19',\n",
       " 'five key area sunak must tackl serv econom recoveri',\n",
       " 'covid-19 leav firm ‘fatal ill-prepared’ no-deal brexit',\n",
       " \"the week patriarchi bacardi' 'ladi vodka': latest long line depress gender product\"]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stemmed_dataset[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5e476f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer as CV\n",
    "\n",
    "cv = CV()\n",
    "X = cv.fit_transform(stemmed_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32dab0a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization complete\n",
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      "Converged at iteration 2: strict convergence.\n",
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     ]
    },
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 4: strict convergence.\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 9: strict convergence.\n",
      "Initialization complete\n",
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      "Converged at iteration 7: strict convergence.\n",
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      "Converged at iteration 5: strict convergence.\n",
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      "Converged at iteration 6: strict convergence.\n",
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      "Converged at iteration 2: strict convergence.\n",
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      "Converged at iteration 7: strict convergence.\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 4: strict convergence.\n",
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      "Converged at iteration 8: strict convergence.\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 2: strict convergence.\n",
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      "Converged at iteration 6: strict convergence.\n",
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      "Converged at iteration 5: strict convergence.\n",
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      "Converged at iteration 17: strict convergence.\n",
      "Initialization complete\n",
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      "Converged at iteration 7: strict convergence.\n",
      "Initialization complete\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 1: strict convergence.\n",
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      "Converged at iteration 4: strict convergence.\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 8: strict convergence.\n",
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      "Converged at iteration 4: strict convergence.\n",
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      "Converged at iteration 6: strict convergence.\n",
      "Initialization complete\n",
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      "Converged at iteration 5: strict convergence.\n",
      "Initialization complete\n",
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      "Converged at iteration 7: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 6769.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, inertia 4119.17398972918\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 8: strict convergence.\n",
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      "Converged at iteration 14: strict convergence.\n",
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      "Converged at iteration 2: strict convergence.\n",
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      "Converged at iteration 6: strict convergence.\n",
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      "Converged at iteration 6: strict convergence.\n",
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      "Converged at iteration 8: strict convergence.\n",
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      "Converged at iteration 4: strict convergence.\n",
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      "Converged at iteration 9: strict convergence.\n",
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      "Converged at iteration 8: strict convergence.\n",
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      "Converged at iteration 3: strict convergence.\n",
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      "Converged at iteration 5: strict convergence.\n",
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      "Converged at iteration 5: strict convergence.\n",
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      "Converged at iteration 7: strict convergence.\n",
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      "Converged at iteration 4: strict convergence.\n",
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      "Converged at iteration 4: strict convergence.\n",
      "Initialization complete\n",
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      "Converged at iteration 2: strict convergence.\n",
      "Initialization complete\n",
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      "Converged at iteration 7: strict convergence.\n",
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      "Converged at iteration 12: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 6894.0\n",
      "Iteration 1, inertia 4070.6083075035785\n",
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      "Iteration 5, inertia 4030.969899796572\n",
      "Converged at iteration 5: strict convergence.\n"
     ]
    }
   ],
   "source": [
    "wcss = []\n",
    "for i in range(1,10):\n",
    "    kmeans = KMeans(n_clusters=i,max_iter=300, n_init=10,random_state=0,verbose=True)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f31fbf5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f8dbfe42b0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,10),wcss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c47ddf3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d185dd83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KMeans(n_clusters=3)\n",
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "32c46def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = cv.transform(['dd'])\n",
    "model.predict(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "22cc2b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-51-f8d8bf9a1205>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['labels'] = kmeans.labels_\n"
     ]
    }
   ],
   "source": [
    "df1 = df\n",
    "df1 = df1[:500]\n",
    "df1['labels'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4ac42f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "one = df1[df1['labels'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "45e7edcc",
   "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>Time</th>\n",
       "      <th>Headlines</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18-Jul-20</td>\n",
       "      <td>Johnson is asking Santa for a Christmas recovery</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18-Jul-20</td>\n",
       "      <td>The Week in Patriarchy  \\n\\n\\n  Bacardi's 'lad...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18-Jul-20</td>\n",
       "      <td>English councils call for smoking ban outside ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18-Jul-20</td>\n",
       "      <td>Can Tesla justify a $300bn valuation?</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18-Jul-20</td>\n",
       "      <td>Empty city centres: 'I’m not sure it will ever...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>25-Jun-20</td>\n",
       "      <td>Planning rules lifted for fairs, markets and c...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>25-Jun-20</td>\n",
       "      <td>Politics live with Andrew Sparrow  Papers indi...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>25-Jun-20</td>\n",
       "      <td>The Robert Jenrick planning row explained</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>25-Jun-20</td>\n",
       "      <td>EasyJet launches £450m rights issue to shore u...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>25-Jun-20</td>\n",
       "      <td>John Lewis appoints Pippa Wicks as executive d...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Time                                          Headlines  labels\n",
       "0    18-Jul-20   Johnson is asking Santa for a Christmas recovery       1\n",
       "4    18-Jul-20  The Week in Patriarchy  \\n\\n\\n  Bacardi's 'lad...       1\n",
       "5    18-Jul-20  English councils call for smoking ban outside ...       1\n",
       "6    18-Jul-20              Can Tesla justify a $300bn valuation?       1\n",
       "7    18-Jul-20  Empty city centres: 'I’m not sure it will ever...       1\n",
       "..         ...                                                ...     ...\n",
       "492  25-Jun-20  Planning rules lifted for fairs, markets and c...       1\n",
       "493  25-Jun-20  Politics live with Andrew Sparrow  Papers indi...       1\n",
       "494  25-Jun-20          The Robert Jenrick planning row explained       1\n",
       "495  25-Jun-20  EasyJet launches £450m rights issue to shore u...       1\n",
       "497  25-Jun-20  John Lewis appoints Pippa Wicks as executive d...       1\n",
       "\n",
       "[306 rows x 3 columns]"
      ]
     },
     "execution_count": 55,
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    "one"
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   "execution_count": null,
   "id": "1746eb15",
   "metadata": {},
   "outputs": [],
   "source": []
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