Spam Email detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import nltk \n",
    "from nltk.corpus import stopwords \n",
    "from nltk.stem import WordNetLemmatizer \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, auc \n",
    "import string \n",
    "from nltk.corpus import wordnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('text messages for spam mail.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>go jurong point crazy .. available bugis n gre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>ok lar ... joke wif u oni ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>u dun say early hor ... u c already say ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>nah n't think go usf life around though</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category                                            Message\n",
       "0      ham  go jurong point crazy .. available bugis n gre...\n",
       "1      ham                      ok lar ... joke wif u oni ...\n",
       "2     spam  free entry 2 wkly comp win fa cup final tkts 2...\n",
       "3      ham        u dun say early hor ... u c already say ...\n",
       "4      ham            nah n't think go usf life around though"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Message[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Ways to display sample stopwards and punctuations***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['arabic',\n",
       " 'azerbaijani',\n",
       " 'basque',\n",
       " 'bengali',\n",
       " 'catalan',\n",
       " 'chinese',\n",
       " 'danish',\n",
       " 'dutch',\n",
       " 'english',\n",
       " 'finnish',\n",
       " 'french',\n",
       " 'german',\n",
       " 'greek',\n",
       " 'hebrew',\n",
       " 'hinglish',\n",
       " 'hungarian',\n",
       " 'indonesian',\n",
       " 'italian',\n",
       " 'kazakh',\n",
       " 'nepali',\n",
       " 'norwegian',\n",
       " 'portuguese',\n",
       " 'romanian',\n",
       " 'russian',\n",
       " 'slovene',\n",
       " 'spanish',\n",
       " 'swedish',\n",
       " 'tajik',\n",
       " 'turkish']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to display stopwords available file\n",
    "stopwords.fileids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stopwords.words('bengali')\n",
    "#stopwords.words('french')\n",
    "#stopwords.words('nepali')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stopwords\n",
    "stopwords.words('english')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nltk.download('stopwords')\n",
    "#download if not working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to display punctuation\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 : Remove punctuation and stopwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopword = set(stopwords.words('english'))\n",
    "stopword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    if isinstance(text, str):\n",
    "        # Tokenize the text into individual words\n",
    "        words = nltk.word_tokenize(text.lower())\n",
    "\n",
    "        # Remove punctuation\n",
    "        words = [word for word in words if word not in string.punctuation]\n",
    "\n",
    "        # Remove stopwords and return the filtered words as a string\n",
    "        filtered_words = [word for word in words if word not in stopword]\n",
    "\n",
    "        # Join the filtered words back into a sentence\n",
    "        filtered_text = ' '.join(filtered_words)\n",
    "\n",
    "        return filtered_text\n",
    "    else:\n",
    "        return ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Message'] = data['Message'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       go jurong point crazy .. available bugis n gre...\n",
       "1                         ok lar ... joking wif u oni ...\n",
       "2       free entry 2 wkly comp win fa cup final tkts 2...\n",
       "3             u dun say early hor ... u c already say ...\n",
       "4              nah n't think goes usf lives around though\n",
       "                              ...                        \n",
       "5567    2nd time tried 2 contact u. u £750 pound prize...\n",
       "5568                          ü b going esplanade fr home\n",
       "5569                            pity mood ... suggestions\n",
       "5570    guy bitching acted like 'd interested buying s...\n",
       "5571                                       rofl true name\n",
       "Name: Message, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Message']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def get_wordnet_pos(tag):\n",
    "    if tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:\n",
    "        return wordnet.NOUN  # Default to NOUN if the POS tag is not recognized\n",
    "\n",
    "def lemmatize_text(text):\n",
    "    words = nltk.word_tokenize(text)\n",
    "    pos_tags = nltk.pos_tag(words)\n",
    "    lemmatized_words = [lemmatizer.lemmatize(word, get_wordnet_pos(pos_tag)) for word, pos_tag in pos_tags]\n",
    "    lemmatized_text = ' '.join(lemmatized_words)\n",
    "    return lemmatized_text\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Message'] = data['Message'].apply(lemmatize_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       go jurong point crazy .. available bugis n gre...\n",
       "1                           ok lar ... joke wif u oni ...\n",
       "2       free entry 2 wkly comp win fa cup final tkts 2...\n",
       "3             u dun say early hor ... u c already say ...\n",
       "4                 nah n't think go usf life around though\n",
       "                              ...                        \n",
       "5567    2nd time try 2 contact u. u £750 pound prize 2...\n",
       "5568                             ü b go esplanade fr home\n",
       "5569                             pity mood ... suggestion\n",
       "5570    guy bitch act like 'd interested buying someth...\n",
       "5571                                       rofl true name\n",
       "Name: Message, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Message']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: TF-IDF vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "vectorize = TfidfVectorizer()\n",
    "x = vectorize.fit_transform(data['Message'])\n",
    "y = data['Category']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Fit models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size = .2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: Performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: MultinomialNB\n",
      "Accuracy Score = 0.9695067264573991\n",
      "Confusion Matrix:\n",
      "[[966   0]\n",
      " [ 34 115]]\n",
      "AUC Score: 0.9813873025136521\n",
      "\n",
      "\n",
      "Model: BernoulliNB\n",
      "Accuracy Score = 0.9775784753363229\n",
      "Confusion Matrix:\n",
      "[[960   6]\n",
      " [ 19 130]]\n",
      "AUC Score: 0.987674906554393\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = [\n",
    "    MultinomialNB(),\n",
    "    BernoulliNB()\n",
    "]\n",
    "\n",
    "for model in models:\n",
    "    model.fit(xtrain, ytrain)\n",
    "\n",
    "    ypred = model.predict(xtest)\n",
    "    ypred_proba = model.predict_proba(xtest)[:, 1]\n",
    "\n",
    "    print(f\"Model: {type(model).__name__}\")\n",
    "    print('Accuracy Score =',model.score(xtest, ytest))\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print(confusion_matrix(ytest, ypred))\n",
    "    print(\"AUC Score:\", roc_auc_score(ytest, ypred_proba))\n",
    "\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: Make Prediction on Sample text "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: MultinomialNB\n",
      "Prediction: ['spam']\n",
      "\n",
      "\n",
      "Model: BernoulliNB\n",
      "Prediction: ['spam']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_text = 'These messages claim that the recipient has won a lottery of $100000000 and request personal information or payment to receive the supposed winnings. Remember, you have to send $1000 to claim this lottery. Legitimate lotteries and contests do not ask for upfront fees to claim prizes before the deadline'\n",
    "\n",
    "preprocessed_text = preprocess_text(random_text)\n",
    "lemmatized_text = lemmatize_text(preprocessed_text)\n",
    "text_vector = vectorize.transform([lemmatized_text])\n",
    "\n",
    "for model in models:\n",
    "    prediction = model.predict(text_vector)\n",
    "    print(f\"Model: {type(model).__name__}\")\n",
    "    print(\"Prediction:\", prediction)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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