Password_strength_check.csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81f959fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7647d6bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kshit\\AppData\\Local\\Temp\\ipykernel_3452\\4168379995.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  data = pd.read_csv('data.csv', sep=',', error_bad_lines=False)\n",
      "b'Skipping line 2810: expected 2 fields, saw 5\\nSkipping line 4641: expected 2 fields, saw 5\\nSkipping line 7171: expected 2 fields, saw 5\\nSkipping line 11220: expected 2 fields, saw 5\\nSkipping line 13809: expected 2 fields, saw 5\\nSkipping line 14132: expected 2 fields, saw 5\\nSkipping line 14293: expected 2 fields, saw 5\\nSkipping line 14865: expected 2 fields, saw 5\\nSkipping line 17419: expected 2 fields, saw 5\\nSkipping line 22801: expected 2 fields, saw 5\\nSkipping line 25001: expected 2 fields, saw 5\\nSkipping line 26603: expected 2 fields, saw 5\\nSkipping line 26742: expected 2 fields, saw 5\\nSkipping line 29702: expected 2 fields, saw 5\\nSkipping line 32767: expected 2 fields, saw 5\\nSkipping line 32878: expected 2 fields, saw 5\\nSkipping line 35643: expected 2 fields, saw 5\\nSkipping line 36550: expected 2 fields, saw 5\\nSkipping line 38732: expected 2 fields, saw 5\\nSkipping line 40567: expected 2 fields, saw 5\\nSkipping line 40576: expected 2 fields, saw 5\\nSkipping line 41864: expected 2 fields, saw 5\\nSkipping line 46861: expected 2 fields, saw 5\\nSkipping line 47939: expected 2 fields, saw 5\\nSkipping line 48628: expected 2 fields, saw 5\\nSkipping line 48908: expected 2 fields, saw 5\\nSkipping line 57582: expected 2 fields, saw 5\\nSkipping line 58782: expected 2 fields, saw 5\\nSkipping line 58984: expected 2 fields, saw 5\\nSkipping line 61518: expected 2 fields, saw 5\\nSkipping line 63451: expected 2 fields, saw 5\\nSkipping line 68141: expected 2 fields, saw 5\\nSkipping line 72083: expected 2 fields, saw 5\\nSkipping line 74027: expected 2 fields, saw 5\\nSkipping line 77811: expected 2 fields, saw 5\\nSkipping line 83958: expected 2 fields, saw 5\\nSkipping line 85295: expected 2 fields, saw 5\\nSkipping line 88665: expected 2 fields, saw 5\\nSkipping line 89198: expected 2 fields, saw 5\\nSkipping line 92499: expected 2 fields, saw 5\\nSkipping line 92751: expected 2 fields, saw 5\\nSkipping line 93689: expected 2 fields, saw 5\\nSkipping line 94776: expected 2 fields, saw 5\\nSkipping line 97334: expected 2 fields, saw 5\\nSkipping line 102316: expected 2 fields, saw 5\\nSkipping line 103421: expected 2 fields, saw 5\\nSkipping line 106872: expected 2 fields, saw 5\\nSkipping line 109363: expected 2 fields, saw 5\\nSkipping line 110117: expected 2 fields, saw 5\\nSkipping line 110465: expected 2 fields, saw 5\\nSkipping line 113843: expected 2 fields, saw 5\\nSkipping line 115634: expected 2 fields, saw 5\\nSkipping line 121518: expected 2 fields, saw 5\\nSkipping line 123692: expected 2 fields, saw 5\\nSkipping line 124708: expected 2 fields, saw 5\\nSkipping line 129608: expected 2 fields, saw 5\\nSkipping line 133176: expected 2 fields, saw 5\\nSkipping line 135532: expected 2 fields, saw 5\\nSkipping line 138042: expected 2 fields, saw 5\\nSkipping line 139485: expected 2 fields, saw 5\\nSkipping line 140401: expected 2 fields, saw 5\\nSkipping line 144093: expected 2 fields, saw 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fields, saw 5\\nSkipping line 250276: expected 2 fields, saw 5\\nSkipping line 255327: expected 2 fields, saw 5\\nSkipping line 257094: expected 2 fields, saw 5\\n'\n",
      "b'Skipping line 264626: expected 2 fields, saw 5\\nSkipping line 265028: expected 2 fields, saw 5\\nSkipping line 269150: expected 2 fields, saw 5\\nSkipping line 271360: expected 2 fields, saw 5\\nSkipping line 273975: expected 2 fields, saw 5\\nSkipping line 274742: expected 2 fields, saw 5\\nSkipping line 276227: expected 2 fields, saw 5\\nSkipping line 279807: expected 2 fields, saw 5\\nSkipping line 283425: expected 2 fields, saw 5\\nSkipping line 287468: expected 2 fields, saw 5\\nSkipping line 292995: expected 2 fields, saw 5\\nSkipping line 293496: expected 2 fields, saw 5\\nSkipping line 293735: expected 2 fields, saw 5\\nSkipping line 295060: expected 2 fields, saw 5\\nSkipping line 296643: expected 2 fields, saw 5\\nSkipping line 296848: expected 2 fields, saw 5\\nSkipping line 308926: expected 2 fields, saw 5\\nSkipping line 310360: expected 2 fields, saw 5\\nSkipping line 317004: expected 2 fields, saw 5\\nSkipping line 318207: expected 2 fields, saw 5\\nSkipping line 331783: expected 2 fields, saw 5\\nSkipping line 333864: expected 2 fields, saw 5\\nSkipping line 335958: expected 2 fields, saw 5\\nSkipping line 336290: expected 2 fields, saw 5\\nSkipping line 343526: expected 2 fields, saw 5\\nSkipping line 343857: expected 2 fields, saw 5\\nSkipping line 344059: expected 2 fields, saw 5\\nSkipping line 348691: expected 2 fields, saw 5\\nSkipping line 353446: expected 2 fields, saw 5\\nSkipping line 357073: expected 2 fields, saw 5\\nSkipping line 359753: expected 2 fields, saw 5\\nSkipping line 359974: expected 2 fields, saw 5\\nSkipping line 366534: expected 2 fields, saw 5\\nSkipping line 369514: expected 2 fields, saw 5\\nSkipping line 377759: expected 2 fields, saw 5\\nSkipping line 379327: expected 2 fields, saw 5\\nSkipping line 380769: expected 2 fields, saw 5\\nSkipping line 381073: expected 2 fields, saw 5\\nSkipping line 381489: expected 2 fields, saw 5\\nSkipping line 386304: expected 2 fields, saw 5\\nSkipping line 387635: expected 2 fields, saw 5\\nSkipping line 389613: expected 2 fields, saw 5\\nSkipping line 392604: expected 2 fields, saw 5\\nSkipping line 393184: expected 2 fields, saw 5\\nSkipping line 395530: expected 2 fields, saw 5\\nSkipping line 396939: expected 2 fields, saw 5\\nSkipping line 397385: expected 2 fields, saw 5\\nSkipping line 397509: expected 2 fields, saw 5\\nSkipping line 402902: expected 2 fields, saw 5\\nSkipping line 405187: expected 2 fields, saw 5\\nSkipping line 408412: expected 2 fields, saw 5\\nSkipping line 419423: expected 2 fields, saw 5\\nSkipping line 420962: expected 2 fields, saw 5\\nSkipping line 425965: expected 2 fields, saw 5\\nSkipping line 427496: expected 2 fields, saw 5\\nSkipping line 438881: expected 2 fields, saw 5\\nSkipping line 439776: expected 2 fields, saw 5\\nSkipping line 440345: expected 2 fields, saw 5\\nSkipping line 445507: expected 2 fields, saw 5\\nSkipping line 445548: expected 2 fields, saw 5\\nSkipping line 447184: expected 2 fields, saw 5\\nSkipping line 448603: expected 2 fields, saw 5\\nSkipping line 451732: expected 2 fields, saw 5\\nSkipping line 458249: expected 2 fields, saw 5\\nSkipping line 460274: expected 2 fields, saw 5\\nSkipping line 467630: expected 2 fields, saw 5\\nSkipping line 473961: expected 2 fields, saw 5\\nSkipping line 476281: expected 2 fields, saw 5\\nSkipping line 478010: expected 2 fields, saw 5\\nSkipping line 478322: expected 2 fields, saw 5\\nSkipping line 479999: expected 2 fields, saw 5\\nSkipping line 480898: expected 2 fields, saw 5\\nSkipping line 481688: expected 2 fields, saw 5\\nSkipping line 485193: expected 2 fields, saw 5\\nSkipping line 485519: expected 2 fields, saw 5\\nSkipping line 486000: expected 2 fields, saw 5\\nSkipping line 489063: expected 2 fields, saw 5\\nSkipping line 494525: expected 2 fields, saw 5\\nSkipping line 495009: expected 2 fields, saw 5\\nSkipping line 501954: expected 2 fields, saw 5\\nSkipping line 508035: expected 2 fields, saw 5\\nSkipping line 508828: expected 2 fields, saw 5\\nSkipping line 509833: expected 2 fields, saw 5\\nSkipping line 510410: expected 2 fields, saw 5\\nSkipping line 518229: expected 2 fields, saw 5\\nSkipping line 520302: expected 2 fields, saw 5\\nSkipping line 520340: expected 2 fields, saw 5\\n'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 525174: expected 2 fields, saw 5\\nSkipping line 526251: expected 2 fields, saw 5\\nSkipping line 529611: expected 2 fields, saw 5\\nSkipping line 531398: expected 2 fields, saw 5\\nSkipping line 534146: expected 2 fields, saw 5\\nSkipping line 544954: expected 2 fields, saw 5\\nSkipping line 553002: expected 2 fields, saw 5\\nSkipping line 553883: expected 2 fields, saw 5\\nSkipping line 553887: expected 2 fields, saw 5\\nSkipping line 553915: expected 2 fields, saw 5\\nSkipping line 554172: expected 2 fields, saw 5\\nSkipping line 563534: expected 2 fields, saw 5\\nSkipping line 565191: expected 2 fields, saw 5\\nSkipping line 574108: expected 2 fields, saw 5\\nSkipping line 574412: expected 2 fields, saw 5\\nSkipping line 575985: expected 2 fields, saw 5\\nSkipping line 580091: expected 2 fields, saw 5\\nSkipping line 582682: expected 2 fields, saw 5\\nSkipping line 585885: expected 2 fields, saw 5\\nSkipping line 590171: expected 2 fields, saw 5\\nSkipping line 591924: expected 2 fields, saw 5\\nSkipping line 592515: expected 2 fields, saw 5\\nSkipping line 593888: expected 2 fields, saw 5\\nSkipping line 596245: expected 2 fields, saw 5\\nSkipping line 607344: expected 2 fields, saw 5\\nSkipping line 607633: expected 2 fields, saw 5\\nSkipping line 610939: expected 2 fields, saw 5\\nSkipping line 613638: expected 2 fields, saw 5\\nSkipping line 615643: expected 2 fields, saw 5\\nSkipping line 615901: expected 2 fields, saw 5\\nSkipping line 617389: expected 2 fields, saw 5\\nSkipping line 634641: expected 2 fields, saw 5\\nSkipping line 635755: expected 2 fields, saw 5\\nSkipping line 646243: expected 2 fields, saw 5\\nSkipping line 647165: expected 2 fields, saw 5\\nSkipping line 648610: expected 2 fields, saw 5\\nSkipping line 648772: expected 2 fields, saw 5\\nSkipping line 651833: expected 2 fields, saw 5\\nSkipping line 653663: expected 2 fields, saw 5\\nSkipping line 656233: expected 2 fields, saw 5\\nSkipping line 656694: expected 2 fields, saw 5\\nSkipping line 659783: expected 2 fields, saw 5\\nSkipping line 660478: expected 2 fields, saw 5\\nSkipping line 661133: expected 2 fields, saw 5\\nSkipping line 661736: expected 2 fields, saw 5\\nSkipping line 669827: expected 2 fields, saw 5\\n'\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('data.csv', sep=',', error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6535b758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>password</th>\n",
       "      <th>strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kzde5577</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kino3434</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>visi7k1yr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>megzy123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lamborghin1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      password  strength\n",
       "0     kzde5577         1\n",
       "1     kino3434         1\n",
       "2    visi7k1yr         1\n",
       "3     megzy123         1\n",
       "4  lamborghin1         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd348fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669640, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38f46197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "password    0\n",
       "strength    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e58c176",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3f726bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fd49c646",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "de3ee825",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "335dd0a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1         2\n",
       "2         1\n",
       "3         1\n",
       "4         1\n",
       "         ..\n",
       "669634    1\n",
       "669635    0\n",
       "669636    1\n",
       "669637    1\n",
       "669638    1\n",
       "Name: strength, Length: 669639, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff0d716e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669639,)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ee042086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669639,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "aede410f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    496801\n",
       "0     89701\n",
       "2     83137\n",
       "Name: strength, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8abc7c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2b3e1bc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kshit\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='strength', ylabel='count'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "794cc915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            1985abcd\n",
       "1    AOoL4yzI5MQy9ljn\n",
       "2            fq6xx9o1\n",
       "3          tasajyh582\n",
       "4          beta2gamma\n",
       "Name: password, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "549bf04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_divide_char(inputs):\n",
    "    characters = []\n",
    "    for i in inputs:\n",
    "        characters.append(i)\n",
    "    return characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e95e1cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(tokenizer=word_divide_char)\n",
    "X=vectorizer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0472299e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669639, 153)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "886bbca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': 35,\n",
       " '9': 43,\n",
       " '8': 42,\n",
       " '5': 39,\n",
       " 'a': 56,\n",
       " 'b': 57,\n",
       " 'c': 58,\n",
       " 'd': 59,\n",
       " 'o': 70,\n",
       " 'l': 67,\n",
       " '4': 38,\n",
       " 'y': 80,\n",
       " 'z': 81,\n",
       " 'i': 64,\n",
       " 'm': 68,\n",
       " 'q': 72,\n",
       " 'j': 65,\n",
       " 'n': 69,\n",
       " 'f': 61,\n",
       " '6': 40,\n",
       " 'x': 79,\n",
       " 't': 75,\n",
       " 's': 74,\n",
       " 'h': 63,\n",
       " '2': 36,\n",
       " 'e': 60,\n",
       " 'g': 62,\n",
       " '3': 37,\n",
       " 'v': 77,\n",
       " 'r': 73,\n",
       " '0': 34,\n",
       " 'u': 76,\n",
       " 'p': 71,\n",
       " '@': 49,\n",
       " 'w': 78,\n",
       " 'k': 66,\n",
       " '.': 32,\n",
       " '7': 41,\n",
       " '_': 54,\n",
       " '-': 31,\n",
       " '/': 33,\n",
       " '*': 29,\n",
       " '#': 23,\n",
       " '$': 24,\n",
       " '=': 46,\n",
       " ')': 28,\n",
       " 'ä': 119,\n",
       " '±': 99,\n",
       " '>': 47,\n",
       " '!': 21,\n",
       " '%': 25,\n",
       " '^': 53,\n",
       " ';': 44,\n",
       " '+': 30,\n",
       " ' ': 20,\n",
       " '{': 82,\n",
       " '}': 84,\n",
       " '&': 26,\n",
       " '(': 27,\n",
       " '?': 48,\n",
       " '<': 45,\n",
       " '`': 55,\n",
       " '|': 83,\n",
       " '~': 85,\n",
       " 'ß': 114,\n",
       " '°': 98,\n",
       " '[': 50,\n",
       " '\\x05': 3,\n",
       " ']': 52,\n",
       " '\\x10': 8,\n",
       " 'þ': 142,\n",
       " 'ú': 138,\n",
       " '³': 101,\n",
       " '\\x7f': 86,\n",
       " '\\x1c': 17,\n",
       " '\\x12': 10,\n",
       " '\\x1b': 16,\n",
       " '÷': 136,\n",
       " '\\x11': 9,\n",
       " 'á': 116,\n",
       " '»': 108,\n",
       " '\\x81': 87,\n",
       " 'ð': 129,\n",
       " 'å': 120,\n",
       " 'â': 117,\n",
       " 'ó': 132,\n",
       " '\"': 22,\n",
       " 'ñ': 130,\n",
       " '²': 100,\n",
       " 'æ': 121,\n",
       " '¦': 93,\n",
       " '\\x0f': 7,\n",
       " 'é': 124,\n",
       " 'õ': 134,\n",
       " '\\x06': 4,\n",
       " 'ü': 140,\n",
       " '\\x8d': 88,\n",
       " 'ý': 141,\n",
       " '\\\\': 51,\n",
       " 'ï': 128,\n",
       " '\\x08': 5,\n",
       " 'ê': 125,\n",
       " 'ç': 122,\n",
       " 'µ': 103,\n",
       " 'ÿ': 143,\n",
       " 'º': 107,\n",
       " 'í': 126,\n",
       " '‚': 147,\n",
       " 'ã': 118,\n",
       " '\\xa0': 89,\n",
       " 'è': 123,\n",
       " '›': 151,\n",
       " '¾': 111,\n",
       " '‹': 150,\n",
       " 'œ': 144,\n",
       " '\\x17': 13,\n",
       " 'ô': 133,\n",
       " 'û': 139,\n",
       " '·': 105,\n",
       " 'ù': 137,\n",
       " '…': 149,\n",
       " '«': 96,\n",
       " 'ò': 131,\n",
       " 'î': 127,\n",
       " '¿': 112,\n",
       " '¨': 95,\n",
       " 'ö': 135,\n",
       " '\\x16': 12,\n",
       " '\\x1e': 19,\n",
       " '‡': 148,\n",
       " '\\x13': 11,\n",
       " '\\x1d': 18,\n",
       " '¹': 106,\n",
       " '´': 102,\n",
       " '¶': 104,\n",
       " '¡': 90,\n",
       " '\\x19': 15,\n",
       " 'ƒ': 145,\n",
       " '\\x04': 2,\n",
       " '\\x0e': 6,\n",
       " '¢': 91,\n",
       " '¯': 97,\n",
       " '\\x01': 0,\n",
       " 'à': 115,\n",
       " '—': 146,\n",
       " '\\x02': 1,\n",
       " '™': 152,\n",
       " '¼': 109,\n",
       " '\\x18': 14,\n",
       " '¤': 92,\n",
       " '½': 110,\n",
       " '§': 94,\n",
       " '×': 113}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "df38c4e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x153 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 8 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "096a3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1985abcd'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "413268b5",
   "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",
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       "\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>tfidf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.415163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.387989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.377937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.374377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.371517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>^</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>_</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>`</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>™</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tfidf\n",
       "b   0.415163\n",
       "c   0.387989\n",
       "8   0.377937\n",
       "d   0.374377\n",
       "5   0.371517\n",
       "..       ...\n",
       "^   0.000000\n",
       "_   0.000000\n",
       "`   0.000000\n",
       "e   0.000000\n",
       "™   0.000000\n",
       "\n",
       "[153 rows x 1 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = vectorizer.get_feature_names_out()\n",
    " \n",
    "#get tfidf vector for first document\n",
    "first_document_vector=X[0]\n",
    " \n",
    "#print the scores\n",
    "df = pd.DataFrame(first_document_vector.T.todense(), index=feature_names, columns=[\"tfidf\"])\n",
    "df.sort_values(by=[\"tfidf\"],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "38048c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression(penalty='l2', multi_class='ovr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ab312081",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8a7f0461",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7158bb70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kshit\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\kshit\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\kshit\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(multi_class='ovr')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "99c8b76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.812024563197741"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "32030af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_regg = LogisticRegression(penalty='l2', multi_class='multinomial', solver='newton-cg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fe1d3858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(multi_class='multinomial', solver='newton-cg')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_regg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "21c3d562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8201926862490441"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_regg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8bbdcaf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    }
   ],
   "source": [
    "X_check = np.array(['Kshi#123@4'])\n",
    "X_check = vectorizer.transform(X_check)\n",
    "y_check = log_regg.predict(X_check)\n",
    "print(y_check)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5956714b",
   "metadata": {},
   "source": [
    "#### Xgboost Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0aab084b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "5ee1ba4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_classifier = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "13c44333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              objective='multi:softprob', predictor=None, ...)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "922c2f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9800028056710758"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4ac3da",
   "metadata": {},
   "source": [
    "#### Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c7e0ee20",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5dd9bf6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_classifier = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6a56d744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4b5e3661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7433535009797222"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_classifier.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6ec66e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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