Bag_of_Words.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sobhan\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mail = pd.read_csv(\"C:/Users/Sobhan/Machine Learning/NLP/bag_of_words/spam_mail.csv\")"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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 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": [
       "  Category                                            Message\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": [
    "mail.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail.Category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mail[\"spam\"] = mail[\"Category\"].apply(lambda x: 1 if x == \"spam\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "      <th>spam</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": [
       "  Category                                            Message  spam\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mail.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(mail.Message, mail.spam, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4457,), (1115,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2389    wiskey Brandy Rum Gin Beer Vodka Scotch Shampa...\n",
       "5199           Ugh my leg hurts. Musta overdid it on mon.\n",
       "3370    Sorry i've not gone to that place. I.ll do so ...\n",
       "4585    Noooooooo please. Last thing I need is stress....\n",
       "3564    Auction round 4. The highest bid is now £54. N...\n",
       "Name: Message, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2389    0\n",
       "5199    0\n",
       "3370    0\n",
       "4585    0\n",
       "3564    1\n",
       "Name: spam, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Bag Of words</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4457x7740 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 59067 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_cv = cv.fit_transform(X_train.values)\n",
    "X_train_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_cv.toarray()[:2][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4457, 7740)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_cv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wiskey': 7544,\n",
       " 'brandy': 1484,\n",
       " 'rum': 5854,\n",
       " 'gin': 3120,\n",
       " 'beer': 1292,\n",
       " 'vodka': 7313,\n",
       " 'scotch': 5957,\n",
       " 'shampain': 6069,\n",
       " 'wine': 7528,\n",
       " 'kudi': 3967,\n",
       " 'yarasu': 7669,\n",
       " 'dhina': 2275,\n",
       " 'vaazhthukkal': 7232,\n",
       " 'ugh': 7121,\n",
       " 'my': 4666,\n",
       " 'leg': 4056,\n",
       " 'hurts': 3552,\n",
       " 'musta': 4659,\n",
       " 'overdid': 5026,\n",
       " 'it': 3744,\n",
       " 'on': 4934,\n",
       " 'mon': 4560,\n",
       " 'sorry': 6335,\n",
       " 've': 7255,\n",
       " 'not': 4827,\n",
       " 'gone': 3160,\n",
       " 'to': 6942,\n",
       " 'that': 6814,\n",
       " 'place': 5227,\n",
       " 'll': 4148,\n",
       " 'do': 2357,\n",
       " 'so': 6285,\n",
       " 'tomorrow': 6967,\n",
       " 'really': 5606,\n",
       " 'noooooooo': 4811,\n",
       " 'please': 5249,\n",
       " 'last': 4010,\n",
       " 'thing': 6849,\n",
       " 'need': 4725,\n",
       " 'is': 3732,\n",
       " 'stress': 6521,\n",
       " 'for': 2924,\n",
       " 'once': 4937,\n",
       " 'in': 3627,\n",
       " 'your': 7707,\n",
       " 'life': 4090,\n",
       " 'be': 1268,\n",
       " 'fair': 2741,\n",
       " 'auction': 1134,\n",
       " 'round': 5827,\n",
       " 'the': 6818,\n",
       " 'highest': 3414,\n",
       " 'bid': 1346,\n",
       " 'now': 4838,\n",
       " '54': 556,\n",
       " 'next': 4764,\n",
       " 'maximum': 4385,\n",
       " '71': 613,\n",
       " 'send': 6017,\n",
       " 'bids': 1347,\n",
       " '10': 254,\n",
       " '83383': 669,\n",
       " 'good': 3164,\n",
       " 'luck': 4241,\n",
       " 'or': 4973,\n",
       " 'remind': 5693,\n",
       " 'me': 4395,\n",
       " 'few': 2806,\n",
       " 'hrs': 3521,\n",
       " 'forwarded': 2947,\n",
       " 'from': 3006,\n",
       " '88877': 704,\n",
       " 'free': 2968,\n",
       " 'entry': 2605,\n",
       " 'into': 3704,\n",
       " 'our': 5006,\n",
       " '250': 367,\n",
       " 'weekly': 7441,\n",
       " 'comp': 1921,\n",
       " 'just': 3862,\n",
       " 'word': 7589,\n",
       " 'enter': 2594,\n",
       " '18': 321,\n",
       " 'www': 7639,\n",
       " 'textcomp': 6793,\n",
       " 'com': 1901,\n",
       " 'tunde': 7081,\n",
       " 'how': 3508,\n",
       " 'are': 1045,\n",
       " 'you': 7703,\n",
       " 'doing': 2381,\n",
       " 'this': 6860,\n",
       " 'wishing': 7542,\n",
       " 'great': 3218,\n",
       " 'day': 2178,\n",
       " 'abiola': 753,\n",
       " 'much': 4632,\n",
       " 'she': 6078,\n",
       " 'payed': 5123,\n",
       " 'suganya': 6588,\n",
       " 'after': 851,\n",
       " 'work': 7591,\n",
       " 'ah': 869,\n",
       " 'den': 2237,\n",
       " 'plus': 5262,\n",
       " 'lor': 4194,\n",
       " 'workin': 7594,\n",
       " 'oso': 4997,\n",
       " 'rite': 5798,\n",
       " 'go': 3140,\n",
       " 'orchard': 4980,\n",
       " 'no': 4793,\n",
       " 'other': 4999,\n",
       " 'liao': 4082,\n",
       " 'seems': 5998,\n",
       " 'like': 4100,\n",
       " 'weird': 7447,\n",
       " 'timing': 6911,\n",
       " 'night': 4775,\n",
       " 'all': 908,\n",
       " 'and': 956,\n",
       " 'want': 7376,\n",
       " 'come': 1906,\n",
       " 'smoke': 6263,\n",
       " 'same': 5901,\n",
       " 'as': 1082,\n",
       " 'when': 7480,\n",
       " 'shitstorm': 6104,\n",
       " 'attributed': 1132,\n",
       " 'always': 928,\n",
       " 'coming': 1912,\n",
       " 'over': 5024,\n",
       " 'making': 4317,\n",
       " 'everyone': 2658,\n",
       " 'welcome': 7449,\n",
       " 'uk': 7127,\n",
       " 'mobile': 4537,\n",
       " 'date': 2171,\n",
       " 'msg': 4614,\n",
       " 'giving': 3129,\n",
       " 'calling': 1623,\n",
       " '08719839835': 153,\n",
       " 'future': 3039,\n",
       " 'mgs': 4457,\n",
       " 'billed': 1353,\n",
       " 'at': 1112,\n",
       " '150p': 306,\n",
       " 'daily': 2151,\n",
       " 'cancel': 1638,\n",
       " 'stop': 6499,\n",
       " '89123': 708,\n",
       " 'does': 2369,\n",
       " 'operate': 4959,\n",
       " 'lt': 4236,\n",
       " 'gt': 3242,\n",
       " 'what': 7473,\n",
       " 'yar': 7668,\n",
       " 'actually': 806,\n",
       " 'we': 7415,\n",
       " 'quite': 5531,\n",
       " 'fast': 2768,\n",
       " 'cos': 2022,\n",
       " 'da': 2143,\n",
       " 'ge': 3082,\n",
       " 'slow': 6240,\n",
       " 'wat': 7395,\n",
       " 'haha': 3278,\n",
       " '4mths': 525,\n",
       " 'half': 3285,\n",
       " 'price': 5404,\n",
       " 'orange': 4977,\n",
       " 'line': 4111,\n",
       " 'rental': 5707,\n",
       " 'latest': 4016,\n",
       " 'camera': 1631,\n",
       " 'phones': 5190,\n",
       " 'had': 3276,\n",
       " 'phone': 5186,\n",
       " '11mths': 277,\n",
       " 'call': 1610,\n",
       " 'mobilesdirect': 4539,\n",
       " '08000938767': 48,\n",
       " 'update': 7185,\n",
       " 'or2stoptxt': 4975,\n",
       " 'sunshine': 6613,\n",
       " 'hols': 3459,\n",
       " 'claim': 1822,\n",
       " 'ur': 7197,\n",
       " 'med': 4408,\n",
       " 'holiday': 3456,\n",
       " 'stamped': 6439,\n",
       " 'self': 6007,\n",
       " 'address': 814,\n",
       " 'envelope': 2609,\n",
       " 'drinks': 2438,\n",
       " 'us': 7210,\n",
       " 'po': 5266,\n",
       " 'box': 1453,\n",
       " '113': 271,\n",
       " 'bray': 1487,\n",
       " 'wicklow': 7507,\n",
       " 'eire': 2536,\n",
       " 'quiz': 5534,\n",
       " 'starts': 6455,\n",
       " 'saturday': 5926,\n",
       " 'unsub': 7175,\n",
       " 'still': 6487,\n",
       " 'have': 3339,\n",
       " 'checked': 1764,\n",
       " 'thanx': 6811,\n",
       " '2day': 383,\n",
       " 'goodmate': 3168,\n",
       " 'think': 6851,\n",
       " 'sary': 5916,\n",
       " 'asusual': 1111,\n",
       " 'cheered': 1769,\n",
       " 'up': 7181,\n",
       " 'love': 4214,\n",
       " 'franyxxxxx': 2962,\n",
       " 'sent': 6026,\n",
       " 'part': 5092,\n",
       " 'every': 2653,\n",
       " 'king': 3931,\n",
       " 'was': 7390,\n",
       " 'crying': 2096,\n",
       " 'baby': 1187,\n",
       " 'building': 1554,\n",
       " 'map': 4336,\n",
       " 'imprtant': 3626,\n",
       " 'where': 7484,\n",
       " 'today': 6948,\n",
       " 'but': 1578,\n",
       " 'wil': 7515,\n",
       " 'reach': 5587,\n",
       " 'tomorw': 6968,\n",
       " 'gud': 3247,\n",
       " 'ni8': 4766,\n",
       " 'later': 4015,\n",
       " 'thanks': 6806,\n",
       " 'message': 4446,\n",
       " 'appreciate': 1026,\n",
       " 'sacrifice': 5877,\n",
       " 'sure': 6630,\n",
       " 'of': 4890,\n",
       " 'process': 5435,\n",
       " 'direct': 2320,\n",
       " 'pay': 5121,\n",
       " 'will': 7518,\n",
       " 'find': 2840,\n",
       " 'out': 5009,\n",
       " 'way': 7410,\n",
       " 'back': 1192,\n",
       " 'test': 6782,\n",
       " 'class': 1830,\n",
       " 'wonderful': 7578,\n",
       " 'planning': 5237,\n",
       " 'usually': 7225,\n",
       " 'stops': 6505,\n",
       " 'hella': 3381,\n",
       " 'weed': 7436,\n",
       " 'if': 3587,\n",
       " 'drinkin': 2436,\n",
       " 'ok': 4916,\n",
       " 'pizza': 5225,\n",
       " 'lol': 4173,\n",
       " 'feeling': 2794,\n",
       " 'sick': 6156,\n",
       " 'grinder': 3227,\n",
       " 'can': 1635,\n",
       " 'look': 4182,\n",
       " 'lib': 4083,\n",
       " 'got': 3181,\n",
       " 'stuff': 6547,\n",
       " 'havent': 3341,\n",
       " 'finish': 2846,\n",
       " 'yet': 7691,\n",
       " 'means': 4401,\n",
       " 'lot': 4201,\n",
       " 'because': 1279,\n",
       " 'people': 5144,\n",
       " 'world': 7598,\n",
       " 'calls': 1625,\n",
       " 'them': 6828,\n",
       " 'friends': 2990,\n",
       " 'first': 2858,\n",
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       " 'engaged': 2579,\n",
       " 'ans': 976,\n",
       " 'vl': 7310,\n",
       " 'adsense': 829,\n",
       " 'approved': 1031,\n",
       " 'mins': 4487,\n",
       " 'linerental': 4114,\n",
       " 'bluetooth': 1405,\n",
       " 'mobileupd8': 4541,\n",
       " 'offers': 4900,\n",
       " '08000839402': 46,\n",
       " 'call2optout': 1612,\n",
       " 'lf56': 4080,\n",
       " 'job': 3817,\n",
       " 'days': 2179,\n",
       " 'potential': 5339,\n",
       " 'talent': 6696,\n",
       " 'problem': 5429,\n",
       " 'spending': 6382,\n",
       " 'quality': 5519,\n",
       " 'sleeping': 6226,\n",
       " 'surfing': 6633,\n",
       " 'nice': 4768,\n",
       " 'dearly': 2192,\n",
       " 'financial': 2839,\n",
       " 'awww': 1172,\n",
       " 'ill': 3597,\n",
       " 'probably': 5428,\n",
       " 'lonely': 4177,\n",
       " 'happens': 3313,\n",
       " '2waxsto': 417,\n",
       " 'medical': 4409,\n",
       " 'insurance': 3689,\n",
       " 'deliver': 2230,\n",
       " 'basic': 1236,\n",
       " 'currently': 2124,\n",
       " 'til': 6905,\n",
       " 'friday': 2985,\n",
       " 'morning': 4583,\n",
       " 'major': 4312,\n",
       " 'guide': 3257,\n",
       " 'excellent': 2682,\n",
       " 'moan': 4534,\n",
       " 'scream': 5964,\n",
       " 'ecstasy': 2508,\n",
       " 'hmm': 3440,\n",
       " 'uncle': 7141,\n",
       " 'informed': 3662,\n",
       " 'paying': 5124,\n",
       " 'school': 5948,\n",
       " 'directly': 2321,\n",
       " 'buy': 1583,\n",
       " 'food': 2917,\n",
       " 'grasp': 3210,\n",
       " 'pretty': 5398,\n",
       " 'booty': 1433,\n",
       " 'fuuuuck': 3040,\n",
       " 'sleepin': 6225,\n",
       " 'sup': 6615,\n",
       " 'awake': 1165,\n",
       " 'congratulations': 1970,\n",
       " 'cd': 1703,\n",
       " 'vouchers': 7325,\n",
       " '125gift': 286,\n",
       " 'guaranteed': 3245,\n",
       " '100': 255,\n",
       " ...}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'that'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names()[6814]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_np = X_train_cv.toarray()\n",
    "X_train_np[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1292, 1484, 2275, 3120, 3967, 5854, 5957, 6069, 7232, 7313, 7528,\n",
       "        7544, 7669], dtype=int64),)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(X_train_np[0]!= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Sorry i've not gone to that place. I.ll do so tomorrow. Really sorry.\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5][3370]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_np[0][6170]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "model = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_cv, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_cv = cv.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       962\n",
      "           1       0.98      0.92      0.95       153\n",
      "\n",
      "    accuracy                           0.99      1115\n",
      "   macro avg       0.98      0.96      0.97      1115\n",
      "weighted avg       0.99      0.99      0.99      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emails = [\n",
    "    \"How's it going? Got any exciting karaoke type activities planned? I'm debating whether to play football this eve. Feeling lazy though.\"\n",
    "]\n",
    "\n",
    "emails_count = cv.transform(emails)\n",
    "model.predict(emails_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using sklearn pipeline and reduce number of lines of code\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "clf = Pipeline([\n",
    "    (\"vectorizer\", CountVectorizer()),\n",
    "    (\"nb\", MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()), ('nb', MultinomialNB())])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       962\n",
      "           1       0.98      0.92      0.95       153\n",
      "\n",
      "    accuracy                           0.99      1115\n",
      "   macro avg       0.98      0.96      0.97      1115\n",
      "weighted avg       0.99      0.99      0.99      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. read the data provided in the same directory with name 'movies_sentiment_data.csv' and store it in df variable\n",
    "2. print the shape of the data\n",
    "3. print top 5 datapoints\n",
    "\n",
    "#creating a new column \"Category\" which represent 1 if the sentiment is positive or 0 if it is negative.\n",
    "#check the distribution of 'Category' and see whether the Target labels are balanced or not.\n",
    "#Do the 'train-test' splitting with test size of 20%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using sklearn pipeline module create a classification pipeline to classify the movie review's positive or negative.\n",
    "\n",
    "*use CountVectorizer for pre-processing the text.\n",
    "\n",
    "*use Random Forest as the classifier with estimators as 50 and criterion as entropy.\n",
    "\n",
    "*print the classification report.\n",
    "\n",
    "1. create a pipeline object\n",
    "2. fit with X_train and y_train\n",
    "3. get the predictions for X_test and store it in y_pred\n",
    "4. print the classfication report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solution1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from  sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. read the data provided in the same directory with name 'movies_sentiment_data.csv' and store it in df variable\n",
    "df = pd.read_csv(\"movies_sentiment_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 2)\n"
     ]
    }
   ],
   "source": [
    "#2. print the shape of the data\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review sentiment\n",
       "0  One of the other reviewers has mentioned that ...  positive\n",
       "1  A wonderful little production. <br /><br />The...  positive\n",
       "2  I thought this was a wonderful way to spend ti...  positive\n",
       "3  Basically there's a family where a little boy ...  negative\n",
       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3. print top 5 datapoints\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a new column \"Category\" which represent 1 if the sentiment is positive or 0 if it is negative\n",
    "df[\"Category\"] = df[\"sentiment\"].apply(lambda x: 1 if x == \"positive\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    25000\n",
       "0    25000\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the distribution of 'Category' and see whether the Target labels are balanced or not.\n",
    "df[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do the 'train-test' splitting with test size of 20%\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.review, df.Category, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solution Exercise 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. create a pipeline object\n",
    "\n",
    "clf = Pipeline([\n",
    "    (\"vectorizer\", CountVectorizer()), #initializing the vectorizer\n",
    "    (\"random_forest\", (RandomForestClassifier(n_estimators = 50, criterion = \"entropy\"))) #using the RandomForest classifier\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()),\n",
       "                ('random_forest',\n",
       "                 RandomForestClassifier(criterion='entropy', n_estimators=50))])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. fit with X_train and y_train\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. get the predictions for X_test and store it in y_pred\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.84      0.84      5023\n",
      "           1       0.84      0.84      0.84      4977\n",
      "\n",
      "    accuracy                           0.84     10000\n",
      "   macro avg       0.84      0.84      0.84     10000\n",
      "weighted avg       0.84      0.84      0.84     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#4. print the classfication report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using sklearn pipeline module create a classification pipeline to classify the movie review's positive or negative..\n",
    "Note:\n",
    "\n",
    "*use CountVectorizer for pre-processing the text.\n",
    "\n",
    "*use KNN as the classifier with n_neighbors of 10 and metric as 'euclidean'.\n",
    "\n",
    "*print the classification report.\n",
    "\n",
    "1. create a pipeline object\n",
    "2. fit with X_train and y_train\n",
    "3. get the predictions for X_test and store it in y_pred\n",
    "4. print the classfication report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solution Exercise 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. create a pipeline object\n",
    "clf = Pipeline([\n",
    "            (\"vectorizer\", CountVectorizer()),   \n",
    "            (\"KNN\", (KNeighborsClassifier(n_neighbors = 10, metric = \"euclidean\")))   #using the KNN classifier with 10 neighbors \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()),\n",
       "                ('KNN',\n",
       "                 KNeighborsClassifier(metric='euclidean', n_neighbors=10))])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. fit with X_train and y_train\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. get the predictions for X_test and store it in y_pred\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.65      0.66      5023\n",
      "           1       0.66      0.68      0.67      4977\n",
      "\n",
      "    accuracy                           0.66     10000\n",
      "   macro avg       0.66      0.66      0.66     10000\n",
      "weighted avg       0.66      0.66      0.66     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#4. print the classfication report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using sklearn pipeline module create a classification pipeline to classify the movie review's positive or negative..\n",
    "Note:\n",
    "\n",
    "*use CountVectorizer for pre-processing the text.\n",
    "\n",
    "*use Multinomial Naive Bayes as the classifier.\n",
    "\n",
    "*print the classification report.\n",
    "\n",
    "1. create a pipeline object\n",
    "2. fit with X_train and y_train\n",
    "3. get the predictions for X_test and store it in y_pred\n",
    "4. print the classfication report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solution Exercise 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. create a pipeline object\n",
    "clf = Pipeline([\n",
    "            (\"vectorizer\", CountVectorizer()),   \n",
    "            (\"Multi NB\", MultinomialNB())   #using the Multinomial Naive Bayes classifier \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer', CountVectorizer()),\n",
       "                ('Multi NB', MultinomialNB())])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. fit with X_train and y_train\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. get the predictions for X_test and store it in y_pred\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.87      0.85      5023\n",
      "           1       0.86      0.83      0.84      4977\n",
      "\n",
      "    accuracy                           0.85     10000\n",
      "   macro avg       0.85      0.85      0.85     10000\n",
      "weighted avg       0.85      0.85      0.85     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#4. print the classfication report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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