preProcess_and_createModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "\n",
    "# To serializer model\n",
    "import pickle\n",
    "# To ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Data processing and visualization libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# ML libraries\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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 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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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      "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",
      "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 downloaded from: https://www.kaggle.com/bhavikbb/password-strength-classifier-dataset\n",
    "data = pd.read_csv(\"data/data.csv\", error_bad_lines=False)\n",
    "# Without error_bad_lines parameter it is giving error, as some lines are not fine\n",
    "# So to skip those rows we can use this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data: (669640, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AVYq1lDE4MgAZfNt</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>u6c8vhow</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>v1118714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>universe2908</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>as326159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           password  strength\n",
       "0          kzde5577         1\n",
       "1          kino3434         1\n",
       "2         visi7k1yr         1\n",
       "3          megzy123         1\n",
       "4       lamborghin1         1\n",
       "5  AVYq1lDE4MgAZfNt         2\n",
       "6          u6c8vhow         1\n",
       "7          v1118714         1\n",
       "8      universe2908         1\n",
       "9          as326159         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Shape of data: {}\".format(data.shape))\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 669640 entries, 0 to 669639\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count   Dtype \n",
      "---  ------    --------------   ----- \n",
      " 0   password  669639 non-null  object\n",
      " 1   strength  669640 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 10.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# To get info about dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check distinct values of strength\n",
    "data.strength.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in dataset description:\n",
    "#### 0 means weak password\n",
    "#### 1 means medium password\n",
    "#### 2 means strong password"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "password    1\n",
       "strength    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check whether null values are present in dataset or not\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>password</th>\n",
       "      <th>strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>367579</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       password  strength\n",
       "367579      NaN         0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It means 1 null value in password\n",
    "data[data[\"password\"].isnull()]\n",
    "# We will drop this value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "password    0\n",
       "strength    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(inplace=True)\n",
    "data.isnull().sum()\n",
    "# Now we don't have any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [password, strength]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if duplicate password are there\n",
    "data[data.duplicated(['password'])]\n",
    "# As we can see there are no duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='strength', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using countplot\n",
    "sns.set_style('whitegrid') # Seaborn has five built-in themes to style its plots: darkgrid, whitegrid, dark, white, and ticks\n",
    "sns.countplot(x ='strength', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['kzde5577', 1],\n",
       "       ['kino3434', 1],\n",
       "       ['visi7k1yr', 1],\n",
       "       ...,\n",
       "       ['184520socram', 1],\n",
       "       ['marken22a', 1],\n",
       "       ['fxx4pw4g', 1]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dataset to array\n",
    "password_array = np.array(data)\n",
    "password_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669639, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "password_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we need to shuffle the data because:\n",
    "* Shuffling data serves the purpose of reducing variance and making sure that models remain general and overfit less.\n",
    "* For best accuracy of the model, it's always recommended that training data should have all flavours of data. Shuffling of training data helps us in achieving this target.\n",
    "\n",
    "When we use this python in build `random` function with numpy arrays, it gives duplicate data\n",
    "\n",
    "`\n",
    "import random\n",
    "random.shuffle(password_array)\n",
    "`\n",
    "\n",
    "* Refer this: https://stackoverflow.com/questions/976882/shuffling-a-list-of-objects#answer-41227542\n",
    "* It is tested with this data also, we are getting duplicate values\n",
    "\n",
    "\n",
    "Instead of this we can either use shuffle from sklearn or we can use numpy.random.shuffle, here we will be using `numpy.random.shuffle`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['gi2xoBDI4NQkWLPd', 2],\n",
       "       ['zetrox86', 1],\n",
       "       ['efdqtsam90', 1],\n",
       "       ...,\n",
       "       ['a0115644146', 1],\n",
       "       ['jimenez123456', 1],\n",
       "       ['televyn739', 1]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.shuffle(password_array)\n",
    "password_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide data into independent and dependent feature\n",
    "X = [password[0] for password in password_array]\n",
    "y = [label[1] for label in password_array]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gi2xoBDI4NQkWLPd',\n",
       " 'zetrox86',\n",
       " 'efdqtsam90',\n",
       " 'razors666',\n",
       " 'jep1ce',\n",
       " '9acf876cd4',\n",
       " 'chucho2',\n",
       " 'tube4asian',\n",
       " 'npkdcyt38',\n",
       " 'fydyv435']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 1, 1, 1, 0, 1, 0, 1, 1, 1]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['e', 'n', 'i', 'g', 'm', 'a']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are creating a function which will return letters of all password in a list\n",
    "def words_to_char(password):\n",
    "    characters = []\n",
    "    for letter in password:\n",
    "        characters.append(letter)\n",
    "    return characters\n",
    "\n",
    "# For example:\n",
    "words_to_char(\"enigma\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will now create object of TfidfVectorizer\n",
    "# We are passing this function which will tell how to tokenize your string or corpus\n",
    "vectorizer = TfidfVectorizer(tokenizer=words_to_char)\n",
    "X = vectorizer.fit_transform(X)"
   ]
  },
  {
   "attachments": {
    "tfidf.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![tfidf.png](attachment:tfidf.png)\n",
    "\n",
    "\n",
    "* **Then we multiply TF and IDF to get TFIDF**\n",
    "* **Here we get important words in a document and then we can know what document is about**\n",
    "* **In our case, we won't be having words, we will use characters(as password is collection of characters), that's why we are using tokenizer as `words_to_char` function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669639, 153)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'g': 62,\n",
       " 'i': 64,\n",
       " '2': 36,\n",
       " 'x': 79,\n",
       " 'o': 70,\n",
       " 'b': 57,\n",
       " 'd': 59,\n",
       " '4': 38,\n",
       " 'n': 69,\n",
       " 'q': 72,\n",
       " 'k': 66,\n",
       " 'w': 78,\n",
       " 'l': 67,\n",
       " 'p': 71,\n",
       " 'z': 81,\n",
       " 'e': 60,\n",
       " 't': 75,\n",
       " 'r': 73,\n",
       " '8': 42,\n",
       " '6': 40,\n",
       " 'f': 61,\n",
       " 's': 74,\n",
       " 'a': 56,\n",
       " 'm': 68,\n",
       " '9': 43,\n",
       " '0': 34,\n",
       " 'j': 65,\n",
       " '1': 35,\n",
       " 'c': 58,\n",
       " '7': 41,\n",
       " 'h': 63,\n",
       " 'u': 76,\n",
       " 'y': 80,\n",
       " '3': 37,\n",
       " 'v': 77,\n",
       " '5': 39,\n",
       " '.': 32,\n",
       " '-': 31,\n",
       " '@': 49,\n",
       " '*': 29,\n",
       " '_': 54,\n",
       " '$': 24,\n",
       " '!': 21,\n",
       " '%': 25,\n",
       " '#': 23,\n",
       " ' ': 20,\n",
       " ')': 28,\n",
       " '/': 33,\n",
       " ';': 44,\n",
       " '?': 48,\n",
       " '<': 45,\n",
       " '>': 47,\n",
       " '+': 30,\n",
       " '[': 50,\n",
       " '|': 83,\n",
       " ']': 52,\n",
       " '&': 26,\n",
       " '=': 46,\n",
       " '÷': 136,\n",
       " '(': 27,\n",
       " '{': 82,\n",
       " '~': 85,\n",
       " '}': 84,\n",
       " '^': 53,\n",
       " '±': 99,\n",
       " 'ä': 119,\n",
       " 'æ': 121,\n",
       " 'â': 117,\n",
       " '—': 146,\n",
       " '¿': 112,\n",
       " '\"': 22,\n",
       " '³': 101,\n",
       " 'þ': 142,\n",
       " '\\x17': 13,\n",
       " '\\x8d': 88,\n",
       " '\\x06': 4,\n",
       " '¼': 109,\n",
       " 'ó': 132,\n",
       " '\\\\': 51,\n",
       " '¹': 106,\n",
       " '°': 98,\n",
       " '´': 102,\n",
       " '¶': 104,\n",
       " 'ú': 138,\n",
       " 'ü': 140,\n",
       " 'ô': 133,\n",
       " '\\x10': 8,\n",
       " '\\x1c': 17,\n",
       " '\\x19': 15,\n",
       " 'ñ': 130,\n",
       " '\\x81': 87,\n",
       " 'ð': 129,\n",
       " 'º': 107,\n",
       " '¾': 111,\n",
       " '`': 55,\n",
       " 'ß': 114,\n",
       " '¡': 90,\n",
       " '\\x16': 12,\n",
       " 'ò': 131,\n",
       " '·': 105,\n",
       " '\\x1e': 19,\n",
       " 'å': 120,\n",
       " 'ÿ': 143,\n",
       " 'û': 139,\n",
       " '\\x12': 10,\n",
       " 'ý': 141,\n",
       " '‚': 147,\n",
       " '›': 151,\n",
       " 'ç': 122,\n",
       " 'í': 126,\n",
       " 'õ': 134,\n",
       " 'ö': 135,\n",
       " '¯': 97,\n",
       " '\\x01': 0,\n",
       " 'ê': 125,\n",
       " '¨': 95,\n",
       " 'á': 116,\n",
       " '\\x04': 2,\n",
       " '\\x0e': 6,\n",
       " '\\x05': 3,\n",
       " 'µ': 103,\n",
       " 'ï': 128,\n",
       " 'é': 124,\n",
       " '§': 94,\n",
       " '\\xa0': 89,\n",
       " 'è': 123,\n",
       " '«': 96,\n",
       " '\\x7f': 86,\n",
       " '‡': 148,\n",
       " '\\x08': 5,\n",
       " '\\x1b': 16,\n",
       " '\\x02': 1,\n",
       " '¤': 92,\n",
       " 'à': 115,\n",
       " '™': 152,\n",
       " '²': 100,\n",
       " '¦': 93,\n",
       " '\\x0f': 7,\n",
       " 'ã': 118,\n",
       " '\\x11': 9,\n",
       " 'ƒ': 145,\n",
       " '\\x13': 11,\n",
       " '\\x1d': 18,\n",
       " '»': 108,\n",
       " 'ù': 137,\n",
       " '×': 113,\n",
       " 'î': 127,\n",
       " '‹': 150,\n",
       " 'œ': 144,\n",
       " '½': 110,\n",
       " '¢': 91,\n",
       " '\\x18': 14,\n",
       " '…': 149}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # The attribute vocabulary_ outputs a dictionary in which all ngrams are the dictionary keys and the respective values are the column positions of each ngram (feature) in the tfidf matrix\n",
    "vectorizer.vocabulary_\n",
    "# # Refer: https://stackoverflow.com/questions/54335229/difference-between-vocabulary-and-get-features-of-tfidfvectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tfidf_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.452850</td>\n",
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       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.362873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.284739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>0.283737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.259914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>5</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_value\n",
       "d      0.452850\n",
       "i      0.362873\n",
       "x      0.284739\n",
       "q      0.283737\n",
       "w      0.259914\n",
       "..          ...\n",
       "8      0.000000\n",
       "7      0.000000\n",
       "6      0.000000\n",
       "5      0.000000\n",
       "™      0.000000\n",
       "\n",
       "[153 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get feature names\n",
    "feature_names = vectorizer.get_feature_names()\n",
    "\n",
    "# To get tfidf vector for first document\n",
    "first_doc_vector = X[0].T.todense()\n",
    "# Now to convert it into dataframe to print scores\n",
    "temp_df = pd.DataFrame(first_val, index=feature_names, columns=[\"tfidf_value\"]).sort_values(by=['tfidf_value'], ascending=False)\n",
    "# We used sort_values so we could get some greater than 0 value in initial rows\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((508925, 153), (160714, 153))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we will split our data into training and testing for model training and testing\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.24, random_state=7)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will be using multiple algorithms and choosing best one based on model metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "import xgboost as xgb\n",
    "\n",
    "final_scores = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:23:04] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'log_reg_ovr': 0.812380999788444,\n",
       " 'log_reg_mn': 0.8189579003696007,\n",
       " 'xgb': 0.9800452978583073,\n",
       " 'nb': 0.7438244334656595}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using logistic regression OVR\n",
    "log_reg_ovr = LogisticRegression(multi_class='ovr') # ovr: One vs Rest\n",
    "out = log_reg_ovr.fit(X_train, y_train)\n",
    "final_scores[\"log_reg_ovr\"] = out.score(X_test, y_test)\n",
    "\n",
    "# Using logistic regression multinomial\n",
    "log_reg_mn = LogisticRegression(multi_class='multinomial', solver='saga') # ovr: One vs Rest\n",
    "out = log_reg_mn.fit(X_train, y_train)\n",
    "final_scores[\"log_reg_mn\"] = out.score(X_test, y_test)\n",
    "\n",
    "# Using XGBoost\n",
    "xgb_cls = xgb.XGBClassifier()\n",
    "out = xgb_cls.fit(X_train, y_train)\n",
    "final_scores[\"xgb\"] = out.score(X_test, y_test)\n",
    "\n",
    "# Using multinomial naive bayes\n",
    "nb_cls = MultinomialNB()\n",
    "out = nb_cls.fit(X_train, y_train)\n",
    "final_scores[\"nb\"] = out.score(X_test, y_test)\n",
    "\n",
    "final_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see we are getting best score in XGBoost, so we will be choosing this algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:30:16] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, ..., 0, 1, 1])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cls.fit(X_train, y_train)\n",
    "pred = xgb_cls.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 20073,   1379,      0],\n",
       "       [   736, 118286,    323],\n",
       "       [     1,    768,  19148]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.94      0.95     21452\n",
      "           1       0.98      0.99      0.99    119345\n",
      "           2       0.98      0.96      0.97     19917\n",
      "\n",
      "    accuracy                           0.98    160714\n",
      "   macro avg       0.98      0.96      0.97    160714\n",
      "weighted avg       0.98      0.98      0.98    160714\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see we have got good accuracy, precision and recall for test data**\n",
    "\n",
    "\n",
    "We will save this model so we can use it in our application, where user will enter a random password and we will predict is it strong or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the XGBoost trained model to file in the current working directory\n",
    "with open(\"xgb_classifier.pkl\", \"wb\") as file:  \n",
    "    pickle.dump(xgb_cls, file)\n",
    "\n",
    "# Saving our TFIDF vectorizer too\n",
    "with open(\"vectorizer.pkl\", \"wb\") as file:\n",
    "    pickle.dump(vectorizer, file)"
   ]
  }
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