SMS+Classifier+Bernoulli+NB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs=pd.read_table('SMSSpamCollection+(1)', header=None, names=['Class','sms'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Class</th>\n",
       "      <th>sms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Class                                                sms\n",
       "0   ham  Go until jurong point, crazy.. Available only ...\n",
       "1   ham                      Ok lar... Joking wif u oni...\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3   ham  U dun say so early hor... U c already then say...\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs.Class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham_spam=docs.Class.value_counts()\n",
    "ham_spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% spam is 13.406317300789663\n"
     ]
    }
   ],
   "source": [
    "print('% spam is', (ham_spam[1]/float(ham_spam[0]+ham_spam[1]))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs['label']=docs.Class.map({'ham':0,'spam':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Class</th>\n",
       "      <th>sms</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Class                                                sms  label\n",
       "0   ham  Go until jurong point, crazy.. Available only ...      0\n",
       "1   ham                      Ok lar... Joking wif u oni...      0\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...      1\n",
       "3   ham  U dun say so early hor... U c already then say...      0\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=docs.sms\n",
    "y=docs.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572,)\n",
      "(5572,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test=train_test_split(x, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "710     4mths half price Orange line rental & latest c...\n",
       "3740                           Did you stitch his trouser\n",
       "2711    Hope you enjoyed your new content. text stop t...\n",
       "3155    Not heard from U4 a while. Call 4 rude chat pr...\n",
       "3748    Ü neva tell me how i noe... I'm not at home in...\n",
       "Name: sms, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "vect=CountVectorizer(stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(stop_words='english')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'4mths': 509,\n",
       " 'half': 3089,\n",
       " 'price': 5027,\n",
       " 'orange': 4626,\n",
       " 'line': 3852,\n",
       " 'rental': 5310,\n",
       " 'latest': 3763,\n",
       " 'camera': 1527,\n",
       " 'phones': 4822,\n",
       " 'free': 2780,\n",
       " 'phone': 4818,\n",
       " '11mths': 264,\n",
       " 'mobilesdirect': 4248,\n",
       " '08000938767': 50,\n",
       " 'update': 6673,\n",
       " 'or2stoptxt': 4624,\n",
       " 'cs': 1971,\n",
       " 'did': 2169,\n",
       " 'stitch': 6028,\n",
       " 'trouser': 6545,\n",
       " 'hope': 3260,\n",
       " 'enjoyed': 2436,\n",
       " 'new': 4442,\n",
       " 'content': 1867,\n",
       " 'text': 6321,\n",
       " 'stop': 6038,\n",
       " '61610': 563,\n",
       " 'unsubscribe': 6665,\n",
       " 'help': 3180,\n",
       " '08712400602450p': 98,\n",
       " 'provided': 5089,\n",
       " 'tones2you': 6473,\n",
       " 'uk': 6614,\n",
       " 'heard': 3159,\n",
       " 'u4': 6608,\n",
       " 'rude': 5443,\n",
       " 'chat': 1643,\n",
       " 'private': 5040,\n",
       " '01223585334': 5,\n",
       " 'cum': 1989,\n",
       " 'wan': 6852,\n",
       " '2c': 374,\n",
       " 'pics': 4837,\n",
       " 'gettin': 2913,\n",
       " 'shagged': 5628,\n",
       " 'pix': 4858,\n",
       " '8552': 660,\n",
       " '2end': 378,\n",
       " 'send': 5590,\n",
       " 'sam': 5487,\n",
       " 'xxx': 7127,\n",
       " 'neva': 4438,\n",
       " 'tell': 6289,\n",
       " 'noe': 4477,\n",
       " 'home': 3244,\n",
       " 'da': 2015,\n",
       " 'aft': 826,\n",
       " 'wat': 6876,\n",
       " 'wiskey': 7012,\n",
       " 'brandy': 1389,\n",
       " 'rum': 5450,\n",
       " 'gin': 2927,\n",
       " 'beer': 1209,\n",
       " 'vodka': 6798,\n",
       " 'scotch': 5535,\n",
       " 'shampain': 5637,\n",
       " 'wine': 6993,\n",
       " 'kudi': 3715,\n",
       " 'yarasu': 7142,\n",
       " 'dhina': 2156,\n",
       " 'vaazhthukkal': 6720,\n",
       " 'seeking': 5573,\n",
       " 'lady': 3732,\n",
       " 'street': 6056,\n",
       " 'freak': 2776,\n",
       " 'sheets': 5649,\n",
       " 'lol': 3910,\n",
       " 'drunkard': 2327,\n",
       " 'just': 3623,\n",
       " 'doing': 2259,\n",
       " 'hair': 3085,\n",
       " 'moment': 4265,\n",
       " 'yeah': 7149,\n",
       " 'tonight': 6475,\n",
       " 'wats': 6887,\n",
       " 'plan': 4867,\n",
       " 'turning': 6579,\n",
       " 'moms': 4267,\n",
       " 'telling': 6290,\n",
       " 'cancer': 1535,\n",
       " 'sister': 5745,\n",
       " 'won': 7041,\n",
       " 'calling': 1519,\n",
       " 'hurts': 3323,\n",
       " 'talk': 6234,\n",
       " 'love': 3953,\n",
       " 'coming': 1800,\n",
       " 'dinner': 2193,\n",
       " 'rite': 5396,\n",
       " 'dad': 2018,\n",
       " 'ask': 1032,\n",
       " 'confirm': 1843,\n",
       " 'wif': 6977,\n",
       " 'ah': 842,\n",
       " 'poor': 4931,\n",
       " 'baby': 1121,\n",
       " 'urfeeling': 6687,\n",
       " 'bettersn': 1243,\n",
       " 'luv': 3988,\n",
       " 'probthat': 5052,\n",
       " 'overdose': 4667,\n",
       " 'work': 7058,\n",
       " 'hey': 3198,\n",
       " 'careful': 1556,\n",
       " 'spk': 5938,\n",
       " 'sn': 5822,\n",
       " 'lots': 3942,\n",
       " 'lovejen': 3955,\n",
       " 'gam': 2856,\n",
       " 'gone': 2968,\n",
       " 'outstanding': 4664,\n",
       " 'innings': 3438,\n",
       " 'nice': 4453,\n",
       " 'working': 7062,\n",
       " 'haha': 3081,\n",
       " 'kidding': 3672,\n",
       " 'papa': 4705,\n",
       " 'needs': 4417,\n",
       " 'drugs': 2325,\n",
       " 'chief': 1677,\n",
       " 'bell': 1223,\n",
       " 'need': 4412,\n",
       " 'royal': 5432,\n",
       " 'visit': 6789,\n",
       " '1st': 324,\n",
       " 'june': 3618,\n",
       " 'ugh': 6610,\n",
       " 'long': 3917,\n",
       " 'day': 2054,\n",
       " 'exhausted': 2523,\n",
       " 'want': 6857,\n",
       " 'cuddle': 1984,\n",
       " 'nap': 4381,\n",
       " 'awesome': 1103,\n",
       " 'time': 6418,\n",
       " 'like': 3840,\n",
       " 'lt': 3974,\n",
       " 'gt': 3044,\n",
       " 'll': 3887,\n",
       " 'details': 2143,\n",
       " 'wee': 6916,\n",
       " 'bit': 1277,\n",
       " 'ok': 4578,\n",
       " 'lor': 3932,\n",
       " 'thk': 6376,\n",
       " 'tickets': 6407,\n",
       " 'cos': 1901,\n",
       " 'quite': 5149,\n",
       " 'late': 3760,\n",
       " 'look': 3921,\n",
       " 'ur': 6684,\n",
       " 'frens': 2791,\n",
       " 'darren': 2041,\n",
       " 'dont': 2269,\n",
       " 'know': 3700,\n",
       " 'bring': 1414,\n",
       " 'food': 2730,\n",
       " 'reason': 5224,\n",
       " 've': 6744,\n",
       " 'spoken': 5948,\n",
       " 'year': 7150,\n",
       " 'anyways': 947,\n",
       " 'great': 3020,\n",
       " 'week': 6918,\n",
       " 'best': 1237,\n",
       " 'exam': 2511,\n",
       " 'make': 4042,\n",
       " 'fucks': 2825,\n",
       " 'sake': 5476,\n",
       " 'yes': 7160,\n",
       " 'place': 4862,\n",
       " 'town': 6504,\n",
       " 'meet': 4130,\n",
       " 'exciting': 2515,\n",
       " 'adult': 806,\n",
       " 'singles': 5739,\n",
       " 'txt': 6590,\n",
       " '86688': 664,\n",
       " '150p': 293,\n",
       " 'msg': 4315,\n",
       " 'man': 4052,\n",
       " 'print': 5035,\n",
       " 'outs': 4660,\n",
       " 'room': 5418,\n",
       " 'think': 6368,\n",
       " 'saying': 5517,\n",
       " 'clearly': 1734,\n",
       " 'leave': 3793,\n",
       " 'come': 1794,\n",
       " 'st': 5972,\n",
       " 'andre': 912,\n",
       " 'virgil': 6783,\n",
       " 'cream': 1947,\n",
       " 'yoyyooo': 7179,\n",
       " 'change': 1623,\n",
       " 'permissions': 4789,\n",
       " 'drive': 2313,\n",
       " 'mac': 4013,\n",
       " 'usb': 6699,\n",
       " 'flash': 2692,\n",
       " 'sunshine': 6148,\n",
       " 'quiz': 5151,\n",
       " 'wkly': 7024,\n",
       " 'win': 6987,\n",
       " 'sony': 5865,\n",
       " 'dvd': 2350,\n",
       " 'player': 4875,\n",
       " 'country': 1914,\n",
       " 'algarve': 875,\n",
       " 'ansr': 927,\n",
       " '82277': 637,\n",
       " '50': 520,\n",
       " 'sp': 5898,\n",
       " 'tyrone': 6606,\n",
       " 'dear': 2066,\n",
       " 'th': 6333,\n",
       " 'birthday': 1275,\n",
       " 'loving': 3963,\n",
       " 'gopalettan': 2984,\n",
       " 'planning': 4871,\n",
       " 'small': 5796,\n",
       " 'gift': 2921,\n",
       " 'participate': 4724,\n",
       " 'welcome': 6933,\n",
       " 'contact': 1863,\n",
       " 'admin': 793,\n",
       " 'team': 6274,\n",
       " 'class': 1724,\n",
       " 'hours': 3282,\n",
       " 'sorry': 5877,\n",
       " 'okay': 4579,\n",
       " 'wait': 6834,\n",
       " 'rushing': 5458,\n",
       " 'school': 5527,\n",
       " 'rush': 5457,\n",
       " 'hungry': 3314,\n",
       " 'usually': 6712,\n",
       " 'stops': 6044,\n",
       " 'hella': 3177,\n",
       " 'weed': 6917,\n",
       " 'smoke': 5811,\n",
       " 'awarded': 1101,\n",
       " 'city': 1715,\n",
       " 'break': 1396,\n",
       " '200': 335,\n",
       " 'summer': 6139,\n",
       " 'shopping': 5681,\n",
       " 'spree': 5961,\n",
       " 'wk': 7020,\n",
       " 'store': 6046,\n",
       " '88039': 675,\n",
       " 'skilgme': 5759,\n",
       " 'tscs087147403231winawk': 6562,\n",
       " 'age16': 832,\n",
       " '50perwksub': 528,\n",
       " 'shall': 5635,\n",
       " 'pickle': 4836,\n",
       " 'isn': 3506,\n",
       " 'frnd': 2809,\n",
       " 'necesity': 4407,\n",
       " 'life': 3830,\n",
       " 'imagine': 3376,\n",
       " 'urself': 6697,\n",
       " 'witout': 7017,\n",
       " 'hw': 3328,\n",
       " 'feel': 2620,\n",
       " 'colleg': 1786,\n",
       " 'wth': 7102,\n",
       " 'cell': 1605,\n",
       " 'abt': 742,\n",
       " 'functions': 2833,\n",
       " 'thnk': 6378,\n",
       " 'events': 2491,\n",
       " 'espe': 2474,\n",
       " 'cared': 1553,\n",
       " 'missed': 4215,\n",
       " 'amp': 903,\n",
       " 'irritated': 3496,\n",
       " '4wrd': 517,\n",
       " 'frnds': 2810,\n",
       " 'wthout': 7103,\n",
       " 'live': 3880,\n",
       " 'jst': 3605,\n",
       " 'takecare': 6228,\n",
       " 'goodmorning': 2977,\n",
       " 'brum': 1437,\n",
       " 'thanks': 6337,\n",
       " 'putting': 5126,\n",
       " 'keeping': 3651,\n",
       " 'happy': 3120,\n",
       " 'soon': 5868,\n",
       " 'im': 3373,\n",
       " 'tellmiss': 6291,\n",
       " 'way': 6891,\n",
       " 'bloomberg': 1310,\n",
       " 'message': 4163,\n",
       " 'center': 1607,\n",
       " '447797706009': 482,\n",
       " 'apply': 969,\n",
       " 'future': 2842,\n",
       " 'http': 3298,\n",
       " 'careers': 1555,\n",
       " 'com': 1791,\n",
       " 'enjoy': 2435,\n",
       " 'plural': 4892,\n",
       " 'noun': 4515,\n",
       " 'research': 5331,\n",
       " 'sure': 6165,\n",
       " 'checking': 1656,\n",
       " 'happening': 3114,\n",
       " 'area': 991,\n",
       " 'aight': 850,\n",
       " 'sleeping': 5774,\n",
       " 'surfing': 6168,\n",
       " 'cool': 1888,\n",
       " 'breeze': 1406,\n",
       " 'bright': 1410,\n",
       " 'sun': 6142,\n",
       " 'fresh': 2793,\n",
       " 'flower': 2711,\n",
       " 'twittering': 6588,\n",
       " 'birds': 1270,\n",
       " 'waiting': 6837,\n",
       " 'wish': 7006,\n",
       " 'ringtone': 5389,\n",
       " 'order': 4630,\n",
       " 'reference': 5261,\n",
       " 'number': 4526,\n",
       " 'x49': 7114,\n",
       " 'mobile': 4246,\n",
       " 'charged': 1633,\n",
       " 'tone': 6470,\n",
       " 'arrive': 1016,\n",
       " 'customer': 2002,\n",
       " 'services': 5609,\n",
       " '09065989182': 214,\n",
       " 'colour': 1789,\n",
       " 'red': 5257,\n",
       " 'txtstar': 6598,\n",
       " 'does': 2245,\n",
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       " 'filling': 2654,\n",
       " 'forms': 2757,\n",
       " 'textin': 6327,\n",
       " 'bout': 1356,\n",
       " 'worries': 7069,\n",
       " 'photo': 4824,\n",
       " 'shoot': 5678,\n",
       " ...}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       " 'abi',\n",
       " 'ability',\n",
       " 'abiola',\n",
       " 'abj',\n",
       " 'able',\n",
       " 'abnormally',\n",
       " 'aboutas',\n",
       " 'abroad',\n",
       " 'absence',\n",
       " 'absolutely',\n",
       " 'absolutly',\n",
       " 'abstract',\n",
       " 'abt',\n",
       " 'abta',\n",
       " 'ac',\n",
       " 'academic',\n",
       " 'acc',\n",
       " 'accent',\n",
       " 'accenture',\n",
       " 'accept',\n",
       " 'access',\n",
       " 'accessible',\n",
       " 'accidant',\n",
       " 'accident',\n",
       " 'accidentally',\n",
       " 'accommodation',\n",
       " 'accomodate',\n",
       " 'accomodations',\n",
       " 'accordin',\n",
       " 'accordingly',\n",
       " 'account',\n",
       " 'accounting',\n",
       " 'accounts',\n",
       " 'achan',\n",
       " 'ache',\n",
       " 'achieve',\n",
       " 'acid',\n",
       " 'acl03530150pm',\n",
       " 'acnt',\n",
       " 'aco',\n",
       " 'act',\n",
       " 'acted',\n",
       " 'actin',\n",
       " 'acting',\n",
       " 'action',\n",
       " 'activ8',\n",
       " 'activate',\n",
       " 'active',\n",
       " 'activities',\n",
       " 'actor',\n",
       " 'actual',\n",
       " 'actually',\n",
       " 'ad',\n",
       " 'adam',\n",
       " 'add',\n",
       " 'addamsfa',\n",
       " 'added',\n",
       " 'addicted',\n",
       " 'addie',\n",
       " 'adding',\n",
       " 'address',\n",
       " 'adewale',\n",
       " 'adjustable',\n",
       " 'admin',\n",
       " 'administrator',\n",
       " 'admirer',\n",
       " 'admission',\n",
       " 'admit',\n",
       " 'adore',\n",
       " 'adoring',\n",
       " 'adp',\n",
       " 'adress',\n",
       " 'adrian',\n",
       " 'adrink',\n",
       " 'ads',\n",
       " 'adsense',\n",
       " 'adult',\n",
       " 'adults',\n",
       " 'advance',\n",
       " 'adventure',\n",
       " 'advice',\n",
       " 'advise',\n",
       " 'advising',\n",
       " 'advisors',\n",
       " 'aeronautics',\n",
       " 'aeroplane',\n",
       " 'affair',\n",
       " 'affairs',\n",
       " 'affection',\n",
       " 'affections',\n",
       " 'affidavit',\n",
       " 'afford',\n",
       " 'afghanistan',\n",
       " 'afraid',\n",
       " 'africa',\n",
       " 'african',\n",
       " 'aft',\n",
       " 'afternon',\n",
       " 'afternoon',\n",
       " 'aftr',\n",
       " 'ag',\n",
       " 'age',\n",
       " 'age16',\n",
       " 'age23',\n",
       " 'agency',\n",
       " 'agent',\n",
       " 'agents',\n",
       " 'ages',\n",
       " 'agidhane',\n",
       " 'aging',\n",
       " 'ago',\n",
       " 'agree',\n",
       " 'ah',\n",
       " 'aha',\n",
       " 'ahead',\n",
       " 'ahhh',\n",
       " 'ahhhh',\n",
       " 'ahmad',\n",
       " 'aids',\n",
       " 'aig',\n",
       " 'aight',\n",
       " 'ain',\n",
       " 'aint',\n",
       " 'air',\n",
       " 'air1',\n",
       " 'airport',\n",
       " 'airtel',\n",
       " 'aiya',\n",
       " 'aiyah',\n",
       " 'aiyar',\n",
       " 'aiyo',\n",
       " 'ajith',\n",
       " 'ak',\n",
       " 'aka',\n",
       " 'akon',\n",
       " 'al',\n",
       " 'alaipayuthe',\n",
       " 'album',\n",
       " 'alcohol',\n",
       " 'aldrine',\n",
       " 'alert',\n",
       " 'alertfrom',\n",
       " 'alerts',\n",
       " 'alex',\n",
       " 'alfie',\n",
       " 'algarve',\n",
       " 'algebra',\n",
       " 'ali',\n",
       " 'alian',\n",
       " 'alibi',\n",
       " 'alive',\n",
       " 'allah',\n",
       " 'allday',\n",
       " 'allow',\n",
       " 'allowed',\n",
       " 'allows',\n",
       " 'alot',\n",
       " 'alright',\n",
       " 'alrite',\n",
       " 'alto18',\n",
       " 'alwa',\n",
       " 'alwys',\n",
       " 'amanda',\n",
       " 'amazing',\n",
       " 'ambitious',\n",
       " 'american',\n",
       " 'ami',\n",
       " 'amk',\n",
       " 'amla',\n",
       " 'amma',\n",
       " 'ammae',\n",
       " 'ammo',\n",
       " 'amore',\n",
       " 'amp',\n",
       " 'amplikater',\n",
       " 'amrita',\n",
       " 'ams',\n",
       " 'amt',\n",
       " 'amused',\n",
       " 'analysis',\n",
       " 'anand',\n",
       " 'anderson',\n",
       " 'andre',\n",
       " 'andres',\n",
       " 'andrews',\n",
       " 'angry',\n",
       " 'animal',\n",
       " 'animation',\n",
       " 'anna',\n",
       " 'annie',\n",
       " 'anniversary',\n",
       " 'announced',\n",
       " 'announcement',\n",
       " 'annoyin',\n",
       " 'annoying',\n",
       " 'anot',\n",
       " 'ans',\n",
       " 'ansr',\n",
       " 'answer',\n",
       " 'answered',\n",
       " 'answerin',\n",
       " 'answering',\n",
       " 'answers',\n",
       " 'answr',\n",
       " 'antelope',\n",
       " 'antha',\n",
       " 'anthony',\n",
       " 'anti',\n",
       " 'antibiotic',\n",
       " 'anybody',\n",
       " 'anymore',\n",
       " 'anyones',\n",
       " 'anyplaces',\n",
       " 'anythiing',\n",
       " 'anythin',\n",
       " 'anythingtomorrow',\n",
       " 'anytime',\n",
       " 'anyways',\n",
       " 'aom',\n",
       " 'apart',\n",
       " 'apartment',\n",
       " 'apes',\n",
       " 'apeshit',\n",
       " 'aphex',\n",
       " 'apnt',\n",
       " 'apo',\n",
       " 'apologetic',\n",
       " 'apologise',\n",
       " 'apologize',\n",
       " 'apology',\n",
       " 'app',\n",
       " 'apparently',\n",
       " 'appeal',\n",
       " 'appear',\n",
       " 'appendix',\n",
       " 'applausestore',\n",
       " 'applebees',\n",
       " 'apples',\n",
       " 'application',\n",
       " 'apply',\n",
       " 'applying',\n",
       " 'appointment',\n",
       " 'appointments',\n",
       " 'appreciate',\n",
       " 'appropriate',\n",
       " 'approve',\n",
       " 'approved',\n",
       " 'approx',\n",
       " 'apps',\n",
       " 'appt',\n",
       " 'appy',\n",
       " 'april',\n",
       " 'aproach',\n",
       " 'aptitude',\n",
       " 'aquarius',\n",
       " 'ar',\n",
       " 'arab',\n",
       " 'arabian',\n",
       " 'arcade',\n",
       " 'archive',\n",
       " 'ard',\n",
       " 'area',\n",
       " 'aren',\n",
       " 'arent',\n",
       " 'arestaurant',\n",
       " 'aretaking',\n",
       " 'argentina',\n",
       " 'argh',\n",
       " 'argue',\n",
       " 'arguing',\n",
       " ...]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_transformed=vect.transform(x_train)\n",
    "x_test_transformed=vect.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bnb=BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bnb.fit(x_train_transformed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class=bnb.predict(x_test_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_proba=bnb.predict_proba(x_test_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9770279971284996"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bnb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1207,    1],\n",
       "       [  31,  154]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion=metrics.confusion_matrix(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1207    1]\n",
      " [  31  154]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "TN=confusion[0,0]\n",
    "FP=confusion[0,1]\n",
    "FN=confusion[1,0]\n",
    "TP=confusion[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8324324324324325"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensitivity=TP/float(FN+TP)\n",
    "sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9991721854304636"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specificity=TN/float(TN+FP)\n",
    "specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9935483870967742\n",
      "0.9935483870967742\n"
     ]
    }
   ],
   "source": [
    "precision=TP/float(TP+FP)\n",
    "print(precision)\n",
    "print(metrics.precision_score(y_test,y_pred_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8324324324324325"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(y_test,y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9058823529411765"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(y_test,y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+00, 1.54647985e-10],\n",
       "       [1.00000000e+00, 1.69177313e-10],\n",
       "       [9.99999997e-01, 2.52488636e-09],\n",
       "       ...,\n",
       "       [9.99994219e-01, 5.78141360e-06],\n",
       "       [3.64570741e-06, 9.99996354e-01],\n",
       "       [1.00000000e+00, 2.90374743e-12]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix as sk_confusion_matrix\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "false_positive_rate, true_positive_rate, thresholds=roc_curve(y_test, y_pred_proba[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_auc=auc(false_positive_rate, true_positive_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9967692858421334"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.25405405, 0.28648649, 0.2972973 , 0.3027027 ,\n",
       "       0.32432432, 0.6       , 0.61081081, 0.61621622, 0.62702703,\n",
       "       0.68648649, 0.6972973 , 0.74054054, 0.74054054, 0.76216216,\n",
       "       0.77297297, 0.83243243, 0.83243243, 0.85945946, 0.85945946,\n",
       "       0.87567568, 0.87567568, 0.92972973, 0.92972973, 0.94594595,\n",
       "       0.94594595, 0.95135135, 0.95135135, 0.96216216, 0.96216216,\n",
       "       0.96756757, 0.96756757, 0.97297297, 0.97297297, 0.97297297,\n",
       "       0.97297297, 0.97837838, 0.97837838, 0.99459459, 0.99459459,\n",
       "       0.99459459, 0.99459459, 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_positive_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 8.27814570e-04, 8.27814570e-04, 8.27814570e-04,\n",
       "       8.27814570e-04, 1.65562914e-03, 1.65562914e-03, 2.48344371e-03,\n",
       "       2.48344371e-03, 3.31125828e-03, 3.31125828e-03, 4.13907285e-03,\n",
       "       4.13907285e-03, 5.79470199e-03, 5.79470199e-03, 7.45033113e-03,\n",
       "       7.45033113e-03, 8.27814570e-03, 8.27814570e-03, 1.57284768e-02,\n",
       "       1.57284768e-02, 4.63576159e-02, 4.80132450e-02, 7.11920530e-02,\n",
       "       7.11920530e-02, 9.27152318e-02, 9.27152318e-02, 9.43708609e-02,\n",
       "       9.60264901e-02, 1.28311258e-01, 1.28311258e-01, 2.14403974e-01,\n",
       "       2.16059603e-01, 2.20198675e-01, 2.23509934e-01, 2.31788079e-01,\n",
       "       2.34271523e-01, 2.47516556e-01, 2.49172185e-01, 2.65728477e-01,\n",
       "       2.69039735e-01, 2.77317881e-01, 2.78973510e-01, 2.90562914e-01,\n",
       "       2.92218543e-01, 3.00496689e-01, 3.02980132e-01, 3.30298013e-01,\n",
       "       3.33609272e-01, 3.34437086e-01, 3.36092715e-01, 3.48509934e-01,\n",
       "       3.50165563e-01, 3.62582781e-01, 3.73344371e-01, 4.53642384e-01,\n",
       "       4.57781457e-01, 4.69370861e-01, 4.71026490e-01, 4.99172185e-01,\n",
       "       5.00827815e-01, 5.04966887e-01, 5.07450331e-01, 5.09105960e-01,\n",
       "       5.10761589e-01, 5.13245033e-01, 5.14900662e-01, 5.76158940e-01,\n",
       "       5.77814570e-01, 5.79470199e-01, 5.81125828e-01, 5.85264901e-01,\n",
       "       5.86920530e-01, 6.19205298e-01, 6.21688742e-01, 6.25000000e-01,\n",
       "       6.26655629e-01, 6.92880795e-01, 6.94536424e-01, 7.27649007e-01,\n",
       "       7.29304636e-01, 7.83940397e-01, 7.85596026e-01, 8.12913907e-01,\n",
       "       8.14569536e-01, 8.45198675e-01, 8.46854305e-01, 8.94867550e-01,\n",
       "       9.03145695e-01, 9.32119205e-01, 9.33774834e-01, 9.35430464e-01,\n",
       "       9.37086093e-01, 1.00000000e+00])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "       1.00000000e+00, 1.00000000e+00, 9.99997890e-01, 9.99996354e-01,\n",
       "       9.99995019e-01, 9.99994956e-01, 9.99331659e-01, 9.99303596e-01,\n",
       "       9.95870863e-01, 9.95462919e-01, 9.89588895e-01, 9.84971101e-01,\n",
       "       5.45317784e-01, 2.10969418e-01, 8.10085802e-02, 5.71073030e-02,\n",
       "       2.90086028e-02, 2.35876189e-02, 9.51989482e-05, 8.90802044e-05,\n",
       "       7.07278806e-05, 4.70943921e-05, 4.61263360e-05, 1.55662920e-05,\n",
       "       1.08401730e-05, 8.99660467e-06, 6.09486970e-06, 1.09724378e-06,\n",
       "       8.28411345e-07, 4.96407211e-08, 4.47105639e-08, 2.06982466e-08,\n",
       "       2.05911266e-08, 1.19010796e-08, 1.14871194e-08, 1.08372483e-08,\n",
       "       1.08303547e-08, 5.09956933e-09, 5.05703149e-09, 1.68232631e-09,\n",
       "       1.65630671e-09, 1.57483042e-09, 1.57290293e-09, 1.47852794e-09,\n",
       "       1.47818462e-09, 1.26916365e-09, 1.26104629e-09, 1.05260899e-09,\n",
       "       1.04831205e-09, 9.61532075e-10, 9.60797541e-10, 8.47442362e-10,\n",
       "       8.46842727e-10, 7.92713879e-10, 7.90831112e-10, 6.31432888e-10,\n",
       "       6.28639336e-10, 6.25266317e-10, 6.23799594e-10, 5.41076273e-10,\n",
       "       5.31177593e-10, 4.94955404e-10, 4.89656816e-10, 2.24458116e-10,\n",
       "       2.23954938e-10, 2.10744029e-10, 2.08966627e-10, 1.58949674e-10,\n",
       "       1.56507538e-10, 1.53496328e-10, 1.53449891e-10, 1.52304857e-10,\n",
       "       1.51845697e-10, 1.49301800e-10, 1.49013383e-10, 8.24890248e-11,\n",
       "       8.24170761e-11, 7.94340319e-11, 7.90737519e-11, 7.49111606e-11,\n",
       "       7.45505739e-11, 5.69856667e-11, 5.67768737e-11, 5.52120325e-11,\n",
       "       5.50249440e-11, 2.41532661e-11, 2.40712085e-11, 1.59646345e-11,\n",
       "       1.59430773e-11, 8.01784356e-12, 7.82420177e-12, 5.05124530e-12,\n",
       "       5.04952298e-12, 2.65928450e-12, 2.60449927e-12, 8.94654092e-13,\n",
       "       8.89011148e-13, 3.48167774e-13, 3.46813661e-13, 3.25682543e-13,\n",
       "       3.24822816e-13, 5.93867536e-17])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2af61c23c10>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('True positive rate')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.title('ROC')\n",
    "plt.plot(false_positive_rate, true_positive_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}