Trash mail detector.ipynb

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   "source": [
    "import os\n",
    "import string\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A list of common english words which should not affect predictions\n",
    "stopwords = ['a', 'about', 'above', 'across', 'after', 'afterwards', 'again', 'against', 'all', 'almost', 'alone',\n",
    "             'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst', 'amount',\n",
    "             'an', 'and', 'another', 'any', 'anyhow', 'anyone', 'anything', 'anyway', 'anywhere', 'are', 'around',\n",
    "             'as', 'at', 'back', 'be', 'became', 'because', 'become', 'becomes', 'becoming', 'been', 'before',\n",
    "             'beforehand', 'behind', 'being', 'below', 'beside', 'besides', 'between', 'beyond', 'bill', 'both',\n",
    "             'bottom', 'but', 'by', 'call', 'can', 'cannot', 'cant', 'co', 'con', 'could', 'couldnt', 'cry', 'de',\n",
    "             'describe', 'detail', 'did', 'do', 'does', 'doing', 'don', 'done', 'down', 'due', 'during', 'each', 'eg',\n",
    "             'eight', 'either', 'eleven', 'else', 'elsewhere', 'empty', 'enough', 'etc', 'even', 'ever', 'every', 'everyone',\n",
    "             'everything', 'everywhere', 'except', 'few', 'fifteen', 'fify', 'fill', 'find', 'fire', 'first', 'five', 'for',\n",
    "             'former', 'formerly', 'forty', 'found', 'four', 'from', 'front', 'full', 'further', 'get', 'give', 'go', 'had',\n",
    "             'has', 'hasnt', 'have', 'having', 'he', 'hence', 'her', 'here', 'hereafter', 'hereby', 'herein', 'hereupon',\n",
    "             'hers', 'herself', 'him', 'himself', 'his', 'how', 'however', 'hundred', 'i', 'ie', 'if', 'in', 'inc', 'indeed',\n",
    "             'interest', 'into', 'is', 'it', 'its', 'itself', 'just', 'keep', 'last', 'latter', 'latterly', 'least', 'less',\n",
    "             'ltd', 'made', 'many', 'may', 'me', 'meanwhile', 'might', 'mill', 'mine', 'more', 'moreover', 'most', 'mostly',\n",
    "             'move', 'much', 'must', 'my', 'myself', 'name', 'namely', 'neither', 'never', 'nevertheless', 'next', 'nine',\n",
    "             'no', 'nobody', 'none', 'noone', 'nor', 'not', 'nothing', 'now', 'nowhere', 'of', 'off', 'often', 'on', 'once',\n",
    "             'one', 'only', 'onto', 'or', 'other', 'others', 'otherwise', 'our', 'ours', 'ourselves', 'out', 'over', 'own',\n",
    "             'part', 'per', 'perhaps', 'please', 'put', 'rather', 're', 's', 'same', 'see', 'seem', 'seemed', 'seeming',\n",
    "             'seems', 'serious', 'several', 'she', 'should', 'show', 'side', 'since', 'sincere', 'six', 'sixty', 'so', \n",
    "             'some', 'somehow', 'someone', 'something', 'sometime', 'sometimes', 'somewhere', 'still', 'such', 'system',\n",
    "             't', 'take', 'ten', 'than', 'that', 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'thence', 'there',\n",
    "             'thereafter', 'thereby', 'therefore', 'therein', 'thereupon', 'these', 'they', 'thickv', 'thin', 'third', 'this',\n",
    "             'those', 'though', 'three', 'through', 'throughout', 'thru', 'thus', 'to', 'together', 'too', 'top', 'toward',\n",
    "             'towards', 'twelve', 'twenty', 'two', 'un', 'under', 'until', 'up', 'upon', 'us', 'very', 'via', 'was', 'we',\n",
    "             'well', 'were', 'what', 'whatever', 'when', 'whence', 'whenever', 'where', 'whereafter', 'whereas', 'whereby',\n",
    "             'wherein', 'whereupon', 'wherever', 'whether', 'which', 'while', 'whither', 'who', 'whoever', 'whole', 'whom',\n",
    "             'whose', 'why', 'will', 'with', 'within', 'without', 'would', 'yet', 'you', 'your', 'yours', 'yourself',\n",
    "             'yourselves']"
   ]
  },
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   "cell_type": "code",
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     "text": [
      "         0                                                  1    2    3    4\n",
      "0       v1                                                 v2  NaN  NaN  NaN\n",
      "1      ham  Go until jurong point, crazy.. Available only ...  NaN  NaN  NaN\n",
      "2      ham                      Ok lar... Joking wif u oni...  NaN  NaN  NaN\n",
      "3     spam  Free entry in 2 a wkly comp to win FA Cup fina...  NaN  NaN  NaN\n",
      "4      ham  U dun say so early hor... U c already then say...  NaN  NaN  NaN\n",
      "5      ham  Nah I don't think he goes to usf, he lives aro...  NaN  NaN  NaN\n",
      "6     spam  FreeMsg Hey there darling it's been 3 week's n...  NaN  NaN  NaN\n",
      "7      ham  Even my brother is not like to speak with me. ...  NaN  NaN  NaN\n",
      "8      ham  As per your request 'Melle Melle (Oru Minnamin...  NaN  NaN  NaN\n",
      "9     spam  WINNER!! As a valued network customer you have...  NaN  NaN  NaN\n",
      "10    spam  Had your mobile 11 months or more? U R entitle...  NaN  NaN  NaN\n",
      "11     ham  I'm gonna be home soon and i don't want to tal...  NaN  NaN  NaN\n",
      "12    spam  SIX chances to win CASH! From 100 to 20,000 po...  NaN  NaN  NaN\n",
      "13    spam  URGENT! You have won a 1 week FREE membership ...  NaN  NaN  NaN\n",
      "14     ham  I've been searching for the right words to tha...  NaN  NaN  NaN\n",
      "15     ham                I HAVE A DATE ON SUNDAY WITH WILL!!  NaN  NaN  NaN\n",
      "16    spam  XXXMobileMovieClub: To use your credit, click ...  NaN  NaN  NaN\n",
      "17     ham                         Oh k...i'm watching here:)  NaN  NaN  NaN\n",
      "18     ham  Eh u remember how 2 spell his name... Yes i di...  NaN  NaN  NaN\n",
      "19     ham  Fine if thatåÕs the way u feel. ThatåÕs the wa...  NaN  NaN  NaN\n",
      "20    spam  England v Macedonia - dont miss the goals/team...  NaN  NaN  NaN\n",
      "21     ham          Is that seriously how you spell his name?  NaN  NaN  NaN\n",
      "22     ham  IÛ÷m going to try for 2 months ha ha only joking  NaN  NaN  NaN\n",
      "23     ham  So Ì_ pay first lar... Then when is da stock c...  NaN  NaN  NaN\n",
      "24     ham  Aft i finish my lunch then i go str down lor. ...  NaN  NaN  NaN\n",
      "25     ham  Ffffffffff. Alright no way I can meet up with ...  NaN  NaN  NaN\n",
      "26     ham  Just forced myself to eat a slice. I'm really ...  NaN  NaN  NaN\n",
      "27     ham                     Lol your always so convincing.  NaN  NaN  NaN\n",
      "28     ham  Did you catch the bus ? Are you frying an egg ...  NaN  NaN  NaN\n",
      "29     ham  I'm back &amp; we're packing the car now, I'll...  NaN  NaN  NaN\n",
      "...    ...                                                ...  ...  ...  ...\n",
      "5543   ham           Armand says get your ass over to epsilon  NaN  NaN  NaN\n",
      "5544   ham             U still havent got urself a jacket ah?  NaN  NaN  NaN\n",
      "5545   ham  I'm taking derek &amp; taylor to walmart, if I...  NaN  NaN  NaN\n",
      "5546   ham      Hi its in durban are you still on this number  NaN  NaN  NaN\n",
      "5547   ham         Ic. There are a lotta childporn cars then.  NaN  NaN  NaN\n",
      "5548  spam  Had your contract mobile 11 Mnths? Latest Moto...  NaN  NaN  NaN\n",
      "5549   ham                 No, I was trying it all weekend ;V  NaN  NaN  NaN\n",
      "5550   ham  You know, wot people wear. T shirts, jumpers, ...  NaN  NaN  NaN\n",
      "5551   ham        Cool, what time you think you can get here?  NaN  NaN  NaN\n",
      "5552   ham  Wen did you get so spiritual and deep. That's ...  NaN  NaN  NaN\n",
      "5553   ham  Have a safe trip to Nigeria. Wish you happines...  NaN  NaN  NaN\n",
      "5554   ham                        Hahaha..use your brain dear  NaN  NaN  NaN\n",
      "5555   ham  Well keep in mind I've only got enough gas for...  NaN  NaN  NaN\n",
      "5556   ham  Yeh. Indians was nice. Tho it did kane me off ...  NaN  NaN  NaN\n",
      "5557   ham  Yes i have. So that's why u texted. Pshew...mi...  NaN  NaN  NaN\n",
      "5558   ham  No. I meant the calculation is the same. That ...  NaN  NaN  NaN\n",
      "5559   ham                             Sorry, I'll call later  NaN  NaN  NaN\n",
      "5560   ham  if you aren't here in the next  &lt;#&gt;  hou...  NaN  NaN  NaN\n",
      "5561   ham                  Anything lor. Juz both of us lor.  NaN  NaN  NaN\n",
      "5562   ham  Get me out of this dump heap. My mom decided t...  NaN  NaN  NaN\n",
      "5563   ham  Ok lor... Sony ericsson salesman... I ask shuh...  NaN  NaN  NaN\n",
      "5564   ham                                Ard 6 like dat lor.  NaN  NaN  NaN\n",
      "5565   ham  Why don't you wait 'til at least wednesday to ...  NaN  NaN  NaN\n",
      "5566   ham                                       Huh y lei...  NaN  NaN  NaN\n",
      "5567  spam  REMINDER FROM O2: To get 2.50 pounds free call...  NaN  NaN  NaN\n",
      "5568  spam  This is the 2nd time we have tried 2 contact u...  NaN  NaN  NaN\n",
      "5569   ham              Will Ì_ b going to esplanade fr home?  NaN  NaN  NaN\n",
      "5570   ham  Pity, * was in mood for that. So...any other s...  NaN  NaN  NaN\n",
      "5571   ham  The guy did some bitching but I acted like i'd...  NaN  NaN  NaN\n",
      "5572   ham                         Rofl. Its true to its name  NaN  NaN  NaN\n",
      "\n",
      "[5573 rows x 5 columns]\n",
      "Number of words with frequency higher than cutoff frequency(20) : 283\n",
      "['tmr', 'use', 'txt', 'let', 'nice', \"i've\", 'latest', 'half', 'dun', 'god', 'happy', \"it's\", 'think', 'til', 'i.ll', 'won', 'code', 'den', 'left', 'word', 'juz', 'world', 'nokia', 'play', 'gud', 'cost', 'remember', 'xxx', 'eat', 'leh', 'landline', 'wish', 'bad', 'year', 'girl', 'sure', 'place', 'trying', 'waiting', 'offer', 'text', 'long', 'night', 'man', 'xmas', 'mins', 'days', 'probably', 'coming', 'good', 'early', 'claim', 'wanna', 'line', 'morning', 'min', 'princess', 'goes', 'got', 'great', 'sat', 'fun', 'told', \"don't\", 'car', 'work', 'awarded', 'account', '500', 'hey', 'luv', 'actually', 'speak', 'dunno', 'okay', 'haha', 'dinner', 'ready', 'guys', 'yesterday', 'leave', 'forgot', 'phone', 'box', 'try', 'went', 'tell', 'people', 'ask', 'thanks', 'feel', 'draw', 'month', 'amp', 'reply', 'kiss', 'thought', 'pls', 'little', 'right', 'dear', 'big', 'msg', 'babe', 'better', 'dis', 'cool', 'bring', 'dont', 'real', 'apply', 'guess', 'saw', 'havent', 'need', 'start', 'shall', 'sir', 'week', 'gonna', 'easy', 'yeah', 'message', 'friends', 'shopping', 'dat', 'looking', 'holiday', 'guaranteed', 'mobile', 'lunch', 'mind', 'times', 'meet', 'bus', 'hello', 'day', 'customer', \"i'll\", 'thats', 'bit', 'lt;#&gt', 'really', 'afternoon', 'wat', 'aight', 'soon', 'friend', 'working', 'smile', 'award', 'calls', 'money', 'pick', 'cos', 'thanx', 'fuck', '150ppm', 'orange', 'love', 'hope', \"i'm\", 'help', 'sent', 'tonight', \"there's\", 'enjoy', \"you're\", 'house', 'stop', \"he's\", 'birthday', 'hear', 'said', 'join', 'life', 'lor', 'weekend', 'guy', 'lar', 'wait', 'want', 'new', 'wake', 'thing', 'tones', 'yup', 'job', 'reach', 'plan', 'town', 'sexy', 'going', 'missed', 'send', 'later', 'yes', 'finish', 'thk', 'chat', 'talk', 'today', \"we're\", 'sms', 'come', 'contact', 'bored', 'fine', 'came', 'wif', 'bed', 'minutes', 'å£1000', 'buy', 'class', 'free', 'win', 'lol', 'service', 'hour', 'end', \"that's\", 'watching', 'heart', 'liao', 'wont', 'watch', 'receive', 'tomorrow', 'network', 'camera', 'prize', 'getting', 'know', 'abt', 'didnt', 'special', 'baby', 'look', 'room', 'called', 'late', 'maybe', 'chance', 'way', 'miss', 'shows', 'things', '1st', 'wan', 'meeting', 'entry', 'attempt', 'problem', 'make', 'dad', 'cash', 'best', \"didn't\", 'lot', \"can't\", 'stuff', 'quite', 'like', 'face', 'care', 'tone', 'sleep', 'urgent', 'check', 'live', 'number', 'important', 'person', 'sorry', 'time', 'say', 'home', 'pay', 'ill', 'away', 'shit', '2nd']\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\Sheon\\Desktop\\spam.csv', header = None, encoding = 'latin-1')\n",
    "print(data)\n",
    "\n",
    "X = []\n",
    "Y = []\n",
    "\n",
    "for category in data[0]:\n",
    "    Y.append(category)\n",
    "\n",
    "for text in data[1]:\n",
    "    X.append(text)\n",
    "\n",
    "X.pop(0)\n",
    "Y.pop(0)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "vocab = {}\n",
    "for i in range(len(X_train)):\n",
    "    word_list = []\n",
    "    for word in X_train[i].split():\n",
    "        word_new = word.strip(string.punctuation).lower()\n",
    "        if (len(word_new) > 2)  and (word_new not in stopwords):  \n",
    "            if word_new in vocab:\n",
    "                vocab[word_new] += 1\n",
    "            else:\n",
    "                vocab[word_new] = 1\n",
    "\n",
    "num_words = [0 for i in range(max(vocab.values())+1)]\n",
    "freq = [i for i in range(max(vocab.values())+1)] \n",
    "for key in vocab:\n",
    "    num_words[vocab[key]] += 1\n",
    "\n",
    "cutoff_freq = 20\n",
    "# For deciding cutoff frequency\n",
    "num_words_above_cutoff = len(vocab) - sum(num_words[0 : cutoff_freq]) \n",
    "print(\"Number of words with frequency higher than cutoff frequency({}) :\".format(cutoff_freq), num_words_above_cutoff)\n",
    "\n",
    "features = []\n",
    "for key in vocab:\n",
    "    if vocab[key] >= cutoff_freq:\n",
    "        features.append(key)\n",
    "\n",
    "print(features)\n",
    "\n",
    "X_train_dataset = np.zeros((len(X_train),len(features)))\n",
    "for i in range(len(X_train)):\n",
    "    word_list = [word.strip(string.punctuation).lower() for word in X_train[i].split()]\n",
    "    for word in word_list:\n",
    "        if word in features:\n",
    "            X_train_dataset[i][features.index(word)] += 1\n",
    "            \n",
    "X_test_dataset = np.zeros((len(X_test),len(features)))\n",
    "for i in range(len(X_test)):\n",
    "    word_list = [word.strip(string.punctuation).lower() for word in X_test[i].split()]\n",
    "    for word in word_list:\n",
    "        if word in features:\n",
    "            X_test_dataset[i][features.index(word)] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sklearn's score on training data : 0.966499162479062\n",
      "Sklearn's score on testing data : 0.9605168700646087\n",
      "Classification report for testing data :\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        ham       0.97      0.98      0.98      1196\n",
      "       spam       0.89      0.83      0.86       197\n",
      "\n",
      "avg / total       0.96      0.96      0.96      1393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Using sklearn's Multinomial Naive Bayes\n",
    "clf = MultinomialNB()\n",
    "clf.fit(X_train_dataset, Y_train)\n",
    "\n",
    "sklearn_score_train = clf.score(X_train_dataset, Y_train)\n",
    "print(\"Sklearn's score on training data :\", sklearn_score_train)\n",
    "sklearn_score_test = clf.score(X_test_dataset, Y_test)\n",
    "print(\"Sklearn's score on testing data :\", sklearn_score_test)\n",
    "Y_test_pred = clf.predict(X_test_dataset)\n",
    "print(\"Classification report for testing data :\")\n",
    "print(classification_report(Y_test, Y_test_pred))"
   ]
  }
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