SpamMD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9cdba905",
   "metadata": {},
   "source": [
    "# Email Spam Detection using Artificial Intelligence and Machine Learning\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98ac0ea",
   "metadata": {},
   "source": [
    "Contributers:\n",
    "-------------------\n",
    "\n",
    "Andrew Champagne    \n",
    "Leah Strecker      \n",
    "Suman Bhandari       \n",
    "Ujjwal Pandit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4e4830",
   "metadata": {},
   "source": [
    "In this project we will utilize Python to create a spam email detection system.\n",
    "Our objective is to ___________\n",
    "\n",
    "We will follow the standard data science protocol from data acquisition, preprocessing, feature extraction, model training, evaluation and making predictions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7b7eb0",
   "metadata": {},
   "source": [
    "#### Lets get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1c8eb20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries over here\n",
    "\n",
    "# Pandas for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# for data visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NLTK for text preprocessing tasks like tokenization and stop word removal\n",
    "import csv \n",
    "import string\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Scikit-learn for machine learning algorithms\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d946352",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the dataset\n",
    "df = pd.read_csv('mail_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4c8b83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a750135b",
   "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>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                 Message\n",
       "count      5572                    5572\n",
       "unique        2                    5157\n",
       "top         ham  Sorry, I'll call later\n",
       "freq       4825                      30"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5184e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    0\n",
       "Message     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790e6a27",
   "metadata": {},
   "source": [
    "Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60f39358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Category')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('count')\n",
    "plt.title('Distribution of mails')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e36f449f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['Category'].value_counts(),labels=['ham','spam'],autopct='%0.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e506217e",
   "metadata": {},
   "source": [
    "Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "367662dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[\"Category\"] == \"spam\", \"Category\"] = 0\n",
    "df.loc[df[\"Category\"] == \"ham\", \"Category\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f8d2ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the feature (message) and target (category) data\n",
    "\n",
    "X = df[\"Message\"]\n",
    "Y = df[\"Category\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2375cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_words = \" \".join(df[df['Category'] == 0]['Message']).split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b8653f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Free', 'entry', '2', 'wkly', 'comp', 'win', 'FA', 'Cup', 'final', 'tkts', '21st', 'May', '2005', '.', 'Text', 'FA', '87121', 'receive', 'entry', 'question', '(', 'std', 'txt', 'rate', ')', '&', 'C', \"'s\", 'apply', '08452810075over18', \"'s\", 'FreeMsg', 'Hey', 'darling', \"'s\", '3', 'week', \"'s\", 'word', 'back', '!', \"'d\", 'like', 'fun', 'still', '?', 'Tb', 'ok', '!', 'XxX', 'std', 'chgs', 'send', ',', '£1.50', 'rcv', 'WINNER', '!', '!', 'valued', 'network', 'customer', 'selected', 'receivea', '£900', 'prize', 'reward', '!', 'claim', 'call', '09061701461', '.', 'Claim', 'code', 'KL341', '.', 'Valid', '12', 'hours', '.', 'mobile', '11', 'months', '?', 'U', 'R', 'entitled', 'Update', 'latest', 'colour', 'mobiles', 'camera', 'Free', '!', 'Call', 'Mobile', 'Update', 'Co', 'FREE', '08002986030', 'SIX', 'chances', 'win', 'CASH', '!', '100', '20,000', 'pounds', 'txt', '>', 'CSH11', 'send', '87575', '.', 'Cost', '150p/day', ',', '6days', ',', '16+', 'TsandCs', 'apply', 'Reply', 'HL', '4', 'info', 'URGENT', '!', '1', 'week', 'FREE', 'membership', '£100,000', 'Prize', 'Jackpot', '!', 'Txt', 'word', ':', 'CLAIM', ':', '81010', '&', 'C', 'www.dbuk.net', 'LCCLTD', 'POBOX', '4403LDNW1A7RW18', 'XXXMobileMovieClub', ':', 'use', 'credit', ',', 'click', 'WAP', 'link', 'next', 'txt', 'message', 'click', '>', '>', 'http', ':', '//wap', '.', 'xxxmobilemovieclub.com', '?', 'n=QJKGIGHJJGCBL', 'England', 'v', 'Macedonia', '-', 'dont', 'miss', 'goals/team', 'news', '.', 'Txt', 'ur', 'national', 'team', '87077', 'eg', 'ENGLAND', '87077', 'Try', ':', 'WALES', ',', 'SCOTLAND', '4txt/ú1.20', 'POBOXox36504W45WQ', '16+', 'Thanks', 'subscription', 'Ringtone', 'UK', 'mobile', 'charged', '£5/month', 'Please', 'confirm', 'replying', 'YES', '.', 'reply', 'charged', '07732584351', '-', 'Rodger', 'Burns', '-', 'MSG', '=', 'tried', 'call', 'reply', 'sms', 'free', 'nokia', 'mobile', '+', 'free', 'camcorder', '.', 'Please', 'call', '08000930705', 'delivery', 'tomorrow', 'SMS', '.', 'ac', 'Sptv', ':', 'New', 'Jersey', 'Devils', 'Detroit', 'Red', 'Wings', 'play', 'Ice', 'Hockey', '.', 'Correct', 'Incorrect', '?', 'End', '?', 'Reply', 'END', 'SPTV', 'Congrats', '!', '1', 'year', 'special', 'cinema', 'pass', '2', '.', 'call', '09061209465', '!', 'C', 'Suprman', 'V', ',', 'Matrix3', ',', 'StarWars3', ',', 'etc', '4', 'FREE', '!', 'bx420-ip4-5we', '.', '150pm', '.', 'Dont', 'miss', '!', 'valued', 'customer', ',', 'pleased', 'advise', 'following', 'recent', 'review', 'Mob', '.', 'awarded', '£1500', 'Bonus', 'Prize', ',', 'call', '09066364589', 'Urgent', 'UR', 'awarded', 'complimentary', 'trip', 'EuroDisinc', 'Trav', ',', 'Aco', '&', 'Entry41', '£1000', '.', 'claim', 'txt', 'DIS', '87121', '18+6', '*', '£1.50', '(', 'moreFrmMob', '.', 'ShrAcomOrSglSuplt', ')', '10', ',', 'LS1', '3AJ', 'hear', 'new', '``', 'Divorce', 'Barbie', \"''\", '?', 'comes', 'Ken', \"'s\", 'stuff', '!', 'Please', 'call', 'customer', 'service', 'representative', '0800', '169', '6031', '10am-9pm', 'guaranteed', '£1000', 'cash', '£5000', 'prize', '!', 'free', 'ringtone', 'waiting', 'collected', '.', 'Simply', 'text', 'password', '``', 'MIX', \"''\", '85069', 'verify', '.', 'Get', 'Usher', 'Britney', '.', 'FML', ',', 'PO', 'Box', '5249', ',', 'MK17', '92H', '.', '450Ppw', '16', 'GENT', '!', 'trying', 'contact', '.', 'Last', 'weekends', 'draw', 'shows', '£1000', 'prize', 'GUARANTEED', '.', 'Call', '09064012160', '.', 'Claim', 'Code', 'K52', '.', 'Valid', '12hrs', '.', '150ppm', 'winner', 'U', 'specially', 'selected', '2', 'receive', '£1000', '4', '*', 'holiday', '(', 'flights', 'inc', ')', 'speak', 'live', 'operator', '2', 'claim', '0871277810910p/min', '(', '18+', ')', 'PRIVATE', '!', '2004', 'Account', 'Statement', '07742676969', 'shows', '786', 'unredeemed', 'Bonus', 'Points', '.', 'claim', 'call', '08719180248', 'Identifier', 'Code', ':', '45239', 'Expires', 'URGENT', '!', 'Mobile', '.', 'awarded', '£2000', 'Bonus', 'Caller', 'Prize', '5/9/03', 'final', 'try', 'contact', 'U', 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'wallpaper-all', '4', 'FREE', '!', 'msg', 'mobile', 'content', 'order', 'resent', 'previous', 'attempt', 'failed', 'due', 'network', 'error', 'Queries', 'customersqueries', '@', 'netvision.uk.com', '1', 'new', 'message', '.', 'Please', 'call', '08715205273', 'December', '!', 'mobile', '11mths+', '?', 'entitled', 'update', 'latest', 'colour', 'camera', 'mobile', 'Free', '!', 'Call', 'Mobile', 'Update', 'VCo', 'FREE', '08002986906', 'Get', '3', 'Lions', 'England', 'tone', ',', 'reply', 'lionm', '4', 'mono', 'lionp', '4', 'poly', '.', '4', 'go', '2', 'www.ringtones.co.uk', ',', 'original', 'n', 'best', '.', 'Tones', '3GBP', 'network', 'operator', 'rates', 'apply', '.', 'PRIVATE', '!', '2003', 'Account', 'Statement', '078', '!', '4', '*', 'Costa', 'Del', 'Sol', 'Holiday', '£5000', 'await', 'collection', '.', 'Call', '09050090044', 'toClaim', '.', 'SAE', ',', 'TC', ',', 'POBox334', ',', 'Stockport', ',', 'SK38xh', ',', 'Cost£1.50/pm', ',', 'Max10mins', 'FREE', '1st', 'week', '!', 'No1', 'Nokia', 'tone', '4', 'ur', 'mobile', 'every', 'week', 'txt', 'NOKIA', '8077', 'Get', 'txting', 'tell', 'ur', 'mates', '.', 'www.getzed.co.uk', 'POBox', '36504', 'W45WQ', '16+', 'norm150p/tone', 'Get', 'garden', 'ready', 'summer', 'FREE', 'selection', 'summer', 'bulbs', 'seeds', 'worth', '£33:50', 'Scotsman', 'Saturday', '.', 'stop', 'go2', 'notxt.co.uk', 'Congrats', '2', 'mobile', '3G', 'Videophones', 'R', '.', 'call', '09063458130', '!', 'videochat', 'wid', 'ur', 'mates', ',', 'play', 'java', 'games', ',', 'Dload', 'polypH', 'music', ',', 'noline', 'rentl', '.', 'bx420', '.', 'ip4', '.', '5we', '.', '150p', 'SMS', 'AUCTION', '-', 'BRAND', 'NEW', 'Nokia', '7250', '4', 'auction', 'today', '!', 'Auction', 'FREE', '2', 'join', '&', 'take', 'part', '!', 'Txt', 'NOKIA', '86021', '!', 'ree', 'entry', '2', 'weekly', 'comp', 'chance', 'win', 'ipod', '.', 'Txt', 'POD', '80182', 'get', 'entry', '(', 'std', 'txt', 'rate', ')', '&', 'C', \"'s\", 'apply', '08452810073', 'details', '18+', 'records', 'indicate', 'u', 'maybe', 'entitled', '5000', 'pounds', 'compensation', 'Accident', '.', 'claim', '4', 'free', 'reply', 'CLAIM', 'msg', '.', '2', 'stop', 'txt', 'STOP', 'Spook', 'mob', 'Halloween', 'collection', 'logo', '&', 'pic', 'message', 'plus', 'free', 'eerie', 'tone', ',', 'txt', 'CARD', 'SPOOK', '8007', 'zed', '08701417012150p', 'per', 'logo/pic', 'Call', 'Germany', '1', 'pence', 'per', 'minute', '!', 'Call', 'fixed', 'line', 'via', 'access', 'number', '0844', '861', '85', '85', '.', 'prepayment', '.', 'Direct', 'access', '!', 'www.telediscount.co.uk', '!', '4', '*', 'Costa', 'Del', 'Sol', 'Holiday', '£5000', 'await', 'collection', '.', 'Call', '09050090044', 'toClaim', '.', 'SAE', ',', 'TC', ',', 'POBox334', ',', 'Stockport', ',', 'SK38xh', ',', 'Cost£1.50/pm', ',', 'Max10mins', 'mobile', '11mths', '?', 'Update', 'FREE', 'Oranges', 'latest', 'colour', 'camera', 'mobiles', '&', 'unlimited', 'weekend', 'calls', '.', 'Call', 'Mobile', 'Upd8', 'freefone', '08000839402', '2StopTxt', 'Natalja', '(', '25/F', ')', 'inviting', 'friend', '.', 'Reply', 'YES-440', 'NO-440', 'See', ':', 'www.SMS.ac/u/nat27081980', 'STOP', '?', 'Send', 'STOP', 'FRND', '62468', 'PRIVATE', '!', '2003', 'Account', 'Statement', '<', 'fone', '>', 'shows', '800', 'un-redeemed', 'S.', 'I.', 'M.', 'points', '.', 'Call', '08715203656', 'Identifier', 'Code', ':', '42049', 'Expires', '26/10/04', 'CHOSEN', 'RECEIVE', '£350', 'AWARD', '!', 'Pls', 'call', 'claim', 'number', '09066364311', 'collect', 'award', 'selected', 'receive', 'valued', 'mobile', 'customer', '.', 'Someonone', 'know', 'trying', 'contact', 'via', 'dating', 'service', '!', 'find', 'could', 'call', 'mobile', 'landline', '09064015307', 'BOX334SK38ch', 'Urgent', '!', 'Please', 'call', '09061213237', 'landline', '.', '£5000', 'cash', '4', '*', 'holiday', 'await', 'collection', '.', '&', 'Cs', 'SAE', 'PO', 'Box', '177', 'M227XY', '.', '16+', \"n't\", ',', 'prize', 'go', 'another', 'customer', '.', '&', 'C', 'www.t-c.biz', '18+', '150p/min', 'Polo', 'Ltd', 'Suite', '373', 'London', 'W1J', '6HL', 'Please', 'call', 'back', 'busy', 'URGENT', '!', 'Mobile', 'number', 'awarded', '£2000', 'prize', 'GUARANTEED', '.', 'Call', '09061790126', 'land', 'line', '.', 'Claim', '3030', '.', 'Valid', '12hrs', '150ppm', 'URGENT', '!', '1', 'week', 'FREE', 'membership', '£100,000', 'Prize', 'Jackpot', '!', 'Txt', 'word', ':', 'CLAIM', ':', '81010', '&', 'C', 'www.dbuk.net', 'LCCLTD', 'POBOX', '4403LDNW1A7RW18', 'Urgent', '!', 'Please', 'call', '09061213237', 'landline', '.', '£5000', 'cash', 'luxury', '4', '*', 'Canary', 'Islands', 'Holiday', 'await', 'collection', '.', '&', 'Cs', 'SAE', 'PO', 'Box', '177', '.', 'M227XY', '.', '150ppm', '.', '16+', 'XMAS', 'iscoming', '&', 'ur', 'awarded', 'either', '£500', 'CD', 'gift', 'vouchers', '&', 'free', 'entry', '2', 'r', '£100', 'weekly', 'draw', 'txt', 'MUSIC', '87066', 'TnC', 'www.Ldew.com1win150ppmx3age16subscription', 'Get', 'official', 'ENGLAND', 'poly', 'ringtone', 'colour', 'flag', 'yer', 'mobile', 'tonights', 'game', '!', 'Text', 'TONE', 'FLAG', '84199', '.', 'Optout', 'txt', 'ENG', 'STOP', 'Box39822', 'W111WX', '£1.50', 'u', 'r', 'subscribed', '2', 'TEXTCOMP', '250', 'wkly', 'comp', '.', '1st', 'wk', '?', 'free', 'question', 'follows', ',', 'subsequent', 'wks', 'charged', '@', '150p/msg.2', 'unsubscribe', 'txt', 'STOP', '2', '84128', ',', 'custcare', '08712405020', 'Call', '09095350301', 'send', 'girls', 'erotic', 'ecstacy', '.', '60p/min', '.', 'stop', 'texts', 'call', '08712460324', '(', 'nat', 'rate', ')', 'Camera', '-', 'awarded', 'SiPix', 'Digital', 'Camera', '!', 'call', '09061221066', 'fromm', 'landline', '.', 'Delivery', 'within', '28', 'days', '.', '£400', 'XMAS', 'REWARD', 'WAITING', '!', 'computer', 'randomly', 'picked', 'loyal', 'mobile', 'customers', 'receive', '£400', 'reward', '.', 'call', '09066380611', 'IMPORTANT', 'MESSAGE', '.', 'final', 'contact', 'attempt', '.', 'important', 'messages', 'waiting', 'customer', 'claims', 'dept', '.', 'Expires', '13/4/04', '.', 'Call', '08717507382', '!', 'dating', ':', 'two', '.', 'started', 'sent', 'text', 'talk', 'sport', 'radio', 'last', 'week', '.', 'connection', 'think', 'coincidence', '?', 'current', 'leading', 'bid', '151', '.', 'pause', 'auction', 'send', '.', 'Customer', 'Care', ':', '08718726270', 'Free', 'entry', 'gr8prizes', 'wkly', 'comp', '4', 'chance', 'win', 'latest', 'Nokia', '8800', ',', 'PSP', '£250', 'cash', 'every', 'wk.TXT', 'GREAT', '80878', 'http//www.gr8prizes.com', '08715705022', '1', 'new', 'message', '.', 'Call', '0207-083-6089', 'Santa', 'Calling', '!', 'Would', 'little', 'ones', 'like', 'call', 'Santa', 'Xmas', 'eve', '?', 'Call', '09058094583', 'book', 'time', '.', 'guaranteed', '32000', 'award', 'maybe', 'even', '£1000', 'cash', 'claim', 'ur', 'award', 'call', 'free', '0800', '.....', '(', '18+', ')', '.', 'legitimat', 'efreefone', 'number', 'wat', 'u', 'think', '?', '?', '?', 'Latest', 'News', '!', 'Police', 'station', 'toilet', 'stolen', ',', 'cops', 'nothing', 'go', '!', 'sparkling', 'shopping', 'breaks', '45', 'per', 'person', ';', 'call', '0121', '2025050', 'visit', 'www.shortbreaks.org.uk', 'December', '!', 'mobile', '11mths+', '?', 'entitled', 'update', 'latest', 'colour', 'camera', 'mobile', 'Free', '!', 'Call', 'Mobile', 'Update', 'Co', 'FREE', '08002986906', 'Txt', ':', 'CALL', ':', '86888', '&', 'claim', 'reward', '3', 'hours', 'talk', 'time', 'use', 'phone', '!', 'Subscribe6GBP/mnth', 'inc', '3hrs', '16', 'stop', '?', 'txtStop', 'www.gamb.tv', 'http//tms', '.', 'widelive.com/index', '.', 'wml', '?', 'id=820554ad0a1705572711', '&', 'first=true¡C', 'C', 'Ringtone¡', 'Get', 'garden', 'ready', 'summer', 'FREE', 'selection', 'summer', 'bulbs', 'seeds', 'worth', '£33:50', 'Scotsman', 'Saturday', '.', 'stop', 'go2', 'notxt.co.uk', 'URGENT', '!', 'Last', 'weekend', \"'s\", 'draw', 'shows', '£1000', 'cash', 'Spanish', 'holiday', '!', 'CALL', '09050000332', 'claim', '.', '&', 'C', ':', 'RSTM', ',', 'SW7', '3SS', '.', '150ppm', 'URGENT', 'trying', 'contact', 'Last', 'weekends', 'draw', 'shows', 'u', '£1000', 'prize', 'GUARANTEED', 'Call', '09064017295', 'Claim', 'code', 'K52', 'Valid', '12hrs', '150p', 'pm', '2p', 'per', 'min', 'call', 'Germany', '08448350055', 'BT', 'line', '.', '2p', 'per', 'min', '.', 'Check', 'PlanetTalkInstant.com', 'info', '&', \"'s\", '&', 'C', \"'s\", '.', 'Text', 'stop', 'opt', 'Marvel', 'Mobile', 'Play', 'official', 'Ultimate', 'Spider-man', 'game', '(', '£4.50', ')', 'ur', 'mobile', 'right', '.', 'Text', 'SPIDER', '83338', 'game', '&', 'send', 'u', 'FREE', '8Ball', 'wallpaper', 'SMS', 'SERVICES', '.', 'inclusive', 'text', 'credits', ',', 'pls', 'goto', 'www.comuk.net', 'login=', '3qxj9', 'unsubscribe', 'STOP', ',', 'extra', 'charge', '.', 'help', '08702840625.COMUK', '.', '220-CM2', '9AE', 'PRIVATE', '!', '2003', 'Account', 'Statement', '07808247860', 'shows', '800', 'un-redeemed', 'S.', 'I.', 'M.', 'points', '.', 'Call', '08719899229', 'Identifier', 'Code', ':', '40411', 'Expires', '06/11/04', 'awarded', 'SiPix', 'Digital', 'Camera', '!', 'call', '09061221061', 'landline', '.', 'Delivery', 'within', '28days', '.', 'Cs', 'Box177', '.', 'M221BP', '.', '2yr', 'warranty', '.', '150ppm', '.', '16', '.', 'p', 'p£3.99', 'PRIVATE', '!', '2003', 'Account', 'Statement', 'shows', '800', 'un-redeemed', 'S.I.M', '.', 'points', '.', 'Call', '08718738001', 'Identifier', 'Code', ':', '49557', 'Expires', '26/11/04', 'Want', 'explicit', 'SEX', '30', 'secs', '?', 'Ring', '02073162414', '!', 'Costs', '20p/min', 'Gsex', 'POBOX', '2667', 'WC1N', '3XX', 'ASKED', '3MOBILE', '0870', 'CHATLINES', 'INCLU', 'FREE', 'MINS', '.', 'INDIA', 'CUST', 'SERVs', 'SED', 'YES', '.', 'L8ER', 'GOT', 'MEGA', 'BILL', '.', '3', 'DONT', 'GIV', 'SHIT', '.', 'BAILIFF', 'DUE', 'DAYS', '.', '£250', '3', 'WANT', '£800', 'contract', 'mobile', '11', 'Mnths', '?', 'Latest', 'Motorola', ',', 'Nokia', 'etc', '.', 'FREE', '!', 'Double', 'Mins', '&', 'Text', 'Orange', 'tariffs', '.', 'TEXT', 'YES', 'callback', ',', 'remove', 'records', '.', 'REMINDER', 'O2', ':', 'get', '2.50', 'pounds', 'free', 'call', 'credit', 'details', 'great', 'offers', 'pls', 'reply', '2', 'text', 'valid', 'name', ',', 'house', 'postcode', '2nd', 'time', 'tried', '2', 'contact', 'u.', 'U', '£750', 'Pound', 'prize', '.', '2', 'claim', 'easy', ',', 'call', '087187272008', 'NOW1', '!', '10p', 'per', 'minute', '.', 'BT-national-rate', '.']\n"
     ]
    }
   ],
   "source": [
    "# List of stop words from my friend\n",
    "stop_words = set([\n",
    "    \"i\", \"me\", \"my\", \"myself\", \"we\", \"our\", \"ours\", \"ourselves\", \"you\", \n",
    "    \"your\", \"yours\", \"yourself\", \"yourselves\", \"he\", \"him\", \"his\", \n",
    "    \"himself\", \"she\", \"her\", \"hers\", \"herself\", \"it\", \"its\", \"itself\", \n",
    "    \"they\", \"them\", \"their\", \"theirs\", \"themselves\", \"what\", \"which\", \n",
    "    \"who\", \"whom\", \"this\", \"that\", \"these\", \"those\", \"am\", \"is\", \"are\", \n",
    "    \"was\", \"were\", \"be\", \"been\", \"being\", \"have\", \"has\", \"had\", \"having\", \n",
    "    \"do\", \"does\", \"did\", \"doing\", \"a\", \"an\", \"the\", \"and\", \"but\", \"if\", \n",
    "    \"or\", \"because\", \"as\", \"until\", \"while\", \"of\", \"at\", \"by\", \"for\", \n",
    "    \"with\", \"about\", \"against\", \"between\", \"into\", \"through\", \"during\", \n",
    "    \"before\", \"after\", \"above\", \"below\", \"to\", \"from\", \"up\", \"down\", \n",
    "    \"in\", \"out\", \"on\", \"off\", \"over\", \"under\", \"again\", \"further\", \"then\", \n",
    "    \"once\", \"here\", \"there\", \"when\", \"where\", \"why\", \"how\", \"all\", \"any\", \n",
    "    \"both\", \"each\", \"few\", \"more\", \"most\", \"other\", \"some\", \"such\", \"no\", \n",
    "    \"nor\", \"not\", \"only\", \"own\", \"same\", \"so\", \"than\", \"too\", \"very\", \n",
    "    \"s\", \"t\", \"can\", \"will\", \"just\", \"don\", \"should\", \"now\", \"d\", \"ll\", \n",
    "    \"m\", \"o\", \"re\", \"ve\", \"y\", \"ain\", \"aren\", \"couldn\", \"didn\", \"doesn\", \n",
    "    \"hadn\", \"hasn\", \"haven\", \"isn\", \"ma\", \"mightn\", \"mustn\", \"needn\", \n",
    "    \"shan\", \"shouldn\", \"wasn\", \"weren\", \"won\", \"wouldn\"\n",
    "])\n",
    "\n",
    "# Tokenize and remove stop words from spam_words\n",
    "tokens = word_tokenize(\" \".join(spam_words))\n",
    "filtered_tokens = [word for word in tokens if word.lower() not in stop_words]\n",
    "\n",
    "# Print or use filtered_tokens as needed\n",
    "print(filtered_tokens)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdd39559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_word_freq = Counter([word.lower() for word in filtered_tokens if word.lower() not in stop_words and word.isalpha()])\n",
    "\n",
    "# Plot the top 10 most common words\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(*zip(*filtered_word_freq.most_common(10)), color='b')\n",
    "plt.xlabel('Words')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Top 10 Most Common Words in Spam Emails')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f91ed2d",
   "metadata": {},
   "source": [
    "Training the ML Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64ed77fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       Go until jurong point, crazy.. Available only ...\n",
      "1                           Ok lar... Joking wif u oni...\n",
      "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
      "3       U dun say so early hor... U c already then say...\n",
      "4       Nah I don't think he goes to usf, he lives aro...\n",
      "                              ...                        \n",
      "5567    This is the 2nd time we have tried 2 contact u...\n",
      "5568                 Will ü b going to esplanade fr home?\n",
      "5569    Pity, * was in mood for that. So...any other s...\n",
      "5570    The guy did some bitching but I acted like i'd...\n",
      "5571                           Rofl. Its true to its name\n",
      "Name: Message, Length: 5572, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d977c582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       1\n",
      "2       0\n",
      "3       1\n",
      "4       1\n",
      "       ..\n",
      "5567    0\n",
      "5568    1\n",
      "5569    1\n",
      "5570    1\n",
      "5571    1\n",
      "Name: Category, Length: 5572, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d8e3bf60",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "66ce88a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4457,)\n",
      "(1115,)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of X_train and X_test\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f464c802",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize TF-IDF Vectorizer\n",
    "feature_extraction = TfidfVectorizer(min_df=1, stop_words=\"english\", lowercase=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9d8083d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature extraction for training and testing data\n",
    "X_train_features = feature_extraction.fit_transform(X_train)\n",
    "X_test_features = feature_extraction.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e22bfb25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Y_train and Y_test to integer type\n",
    "Y_train = Y_train.astype(\"int\")\n",
    "Y_test = Y_test.astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079b444c",
   "metadata": {},
   "source": [
    "Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9e4d3e",
   "metadata": {},
   "source": [
    "#  Create and Fit Logistic Regression Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ec27b2e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train_features, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bc936577",
   "metadata": {},
   "outputs": [],
   "source": [
    " #Make predictions on the training data\n",
    "predict_train_data=model.predict(X_train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5f526409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data:  0.9670181736594121\n"
     ]
    }
   ],
   "source": [
    "#Model Evaluation\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "accuracy_train_data=accuracy_score(Y_train,predict_train_data)\n",
    "print(\"Accuracy on training data: \",accuracy_train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b03a5670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the testing data\n",
    "predict_test_data=model.predict(X_test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b98b5cfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acuuracy on test data:  0.9659192825112107\n"
     ]
    }
   ],
   "source": [
    "#Model Evaluation\n",
    "accuracy_test_data=accuracy_score(Y_test,predict_test_data)\n",
    "print(\"acuuracy on test data: \",accuracy_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce4a550",
   "metadata": {},
   "source": [
    "Testing the model with an email messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "db0b541a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "Ham Mail\n"
     ]
    }
   ],
   "source": [
    "new_mail=[\"Congratulations, you got admitted to NKU for the Fall 2024 for Bachelors in Data Science\"]\n",
    "new_data_features=feature_extraction.transform(new_mail)\n",
    "prediction=model.predict(new_data_features)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0]==1):\n",
    "    print(\"Ham Mail\")\n",
    "else:\n",
    "    print(\"Spam Mail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d553d8a3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix=confusion_matrix(Y_test,predict_test_data)\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(conf_matrix,annot=True,fmt=\"d\",cmap=\"Oranges\",cbar=False)\n",
    "plt.xlabel(\"Predicted value\")\n",
    "plt.ylabel(\"Actual value\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8046f2fc",
   "metadata": {},
   "source": [
    "Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d3fc8b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP Classifier\n",
      "Accuracy score: 0.99\n",
      "Precision score: 0.98\n",
      "Recall score: 1.00\n",
      "F1 score: 0.99\n"
     ]
    }
   ],
   "source": [
    "mlp_classifier_model = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=1000) \n",
    "mlp_classifier_model.fit(X_train_features, Y_train)  \n",
    "\n",
    "prediction = mlp_classifier_model.predict(X_test_features)\n",
    "\n",
    "# Calculate and print classification metrics\n",
    "print(\"MLP Classifier\")\n",
    "print(\"Accuracy score: {:.2f}\".format(accuracy_score(Y_test, prediction)))\n",
    "print(\"Precision score: {:.2f}\".format(precision_score(Y_test, prediction)))\n",
    "print(\"Recall score: {:.2f}\".format(recall_score(Y_test, prediction)))\n",
    "print(\"F1 score: {:.2f}\".format(f1_score(Y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c99b2b01",
   "metadata": {},
   "source": [
    "Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e3b6cc35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial NB\n",
      "Accuracy score: 0.9730941704035875\n",
      "Precision score: 0.9696969696969697\n",
      "Recall score: 1.0\n",
      "F1 score: 0.9846153846153847\n"
     ]
    }
   ],
   "source": [
    "multinomial_nb_model = MultinomialNB()\n",
    "multinomial_nb_model.fit(X_train_features, Y_train)  # Train the model\n",
    "\n",
    "prediction = multinomial_nb_model.predict(X_test_features)\n",
    "\n",
    "print(\"Multinomial NB\")\n",
    "print(\"Accuracy score: {}\". format(accuracy_score(Y_test, prediction)) )\n",
    "print(\"Precision score: {}\". format(precision_score(Y_test, prediction)) )\n",
    "print(\"Recall score: {}\". format(recall_score(Y_test, prediction)))\n",
    "print(\"F1 score: {}\". format(f1_score(Y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491bdb4c",
   "metadata": {},
   "source": [
    "Bernoulli Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "af07499f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bernoulli NB\n",
      "Accuracy score: 0.9739910313901345\n",
      "Precision score: 0.9706774519716885\n",
      "Recall score: 1.0\n",
      "F1 score: 0.9851205746536685\n"
     ]
    }
   ],
   "source": [
    "bernoulli_nb_model = BernoulliNB()\n",
    "bernoulli_nb_model.fit(X_train_features, Y_train)\n",
    "\n",
    "prediction = bernoulli_nb_model.predict(X_test_features)\n",
    "\n",
    "#Evaluation\n",
    "print(\"Bernoulli NB\")\n",
    "print(\"Accuracy score: {}\". format(accuracy_score(Y_test, prediction)) )\n",
    "print(\"Precision score: {}\". format(precision_score(Y_test, prediction)) )\n",
    "print(\"Recall score: {}\". format(recall_score(Y_test, prediction)))\n",
    "print(\"F1 score: {}\". format(f1_score(Y_test, prediction)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ac725b",
   "metadata": {},
   "source": [
    "Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "885ab57b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix Subplot for 3 Models\n",
    "from sklearn.metrics import confusion_matrix\n",
    "models = [(\"Multinomial NB\", multinomial_nb_model), (\"Bernoulli NB\", bernoulli_nb_model),(\"MLP Classifier\", mlp_classifier_model) ]\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
    "for i, (model_name, model) in enumerate(models):\n",
    "    prediction = model.predict(X_test_features)\n",
    "    cm = confusion_matrix(Y_test, prediction)\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False, ax=axes[i])\n",
    "    axes[i].set_title(f\"{model_name} - Confusion Matrix\")\n",
    "    axes[i].set_xlabel(\"Predicted\")\n",
    "    axes[i].set_ylabel(\"Actual\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd6d30e",
   "metadata": {},
   "source": [
    "Metric Comparision Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e8090e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Metric Comparison Heatmap\n",
    "metric_data = []\n",
    "for model_name, model in models:\n",
    "    prediction = model.predict(X_test_features)\n",
    "    accuracy = accuracy_score(Y_test, prediction)\n",
    "    precision = precision_score(Y_test, prediction)\n",
    "    recall = recall_score(Y_test, prediction)\n",
    "    f1 = f1_score(Y_test, prediction)\n",
    "    metric_data.append([accuracy, precision, recall, f1])\n",
    "metric_labels = [\"Accuracy\", \"Precision\", \"Recall\", \"F1 Score\"]\n",
    "#heatmap for metric comparison\n",
    "plt.figure(figsize=(6, 3))\n",
    "sns.heatmap(metric_data, annot=True, fmt=\".2f\", cbar=False, cmap=\"summer_r\", xticklabels=metric_labels, yticklabels=[model_name for model_name, _ in models])\n",
    "plt.title(\"Metric Comparison\")\n",
    "plt.yticks(rotation=0)\n",
    "plt.xlabel(\"Metrics\")\n",
    "plt.ylabel(\"Models\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48de880b",
   "metadata": {},
   "source": [
    "\n",
    "All three models perform exceptionally well, with high accuracy scores.\n",
    "\n",
    "The final choice of model always depend upon what is needed to be filtered & hence model can be adjusted to improve recall or precision.\n",
    "\n",
    "If you want to minimize the number of false negatives i.e spam messages don't end up in the user's inbox you would like to have high Recall.\n",
    "On the other hand with high precision(false negatives), one might miss important messages because the model is overly cautious in classifying messages as spam."
   ]
  }
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