NLP Project sem7.ipynb

{
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   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#importing dataset\n",
    "dataset = pd.read_csv('dataset.tsv',delimiter = '\\t',header = None)\n",
    "X = dataset[1]\n",
    "y = dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>The Da Vinci Code book is just awesome.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>this was the first clive cussler i've ever rea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>i liked the Da Vinci Code a lot.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>i liked the Da Vinci Code a lot.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>I liked the Da Vinci Code but it ultimatly did...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   0                                                  1\n",
       "0  1            The Da Vinci Code book is just awesome.\n",
       "1  1  this was the first clive cussler i've ever rea...\n",
       "2  1                   i liked the Da Vinci Code a lot.\n",
       "3  1                   i liked the Da Vinci Code a lot.\n",
       "4  1  I liked the Da Vinci Code but it ultimatly did..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6913</th>\n",
       "      <td>0</td>\n",
       "      <td>Brokeback Mountain was boring.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6914</th>\n",
       "      <td>0</td>\n",
       "      <td>So Brokeback Mountain was really depressing.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6915</th>\n",
       "      <td>0</td>\n",
       "      <td>As I sit here, watching the MTV Movie Awards, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6916</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok brokeback mountain is such a horrible movie.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6917</th>\n",
       "      <td>0</td>\n",
       "      <td>Oh, and Brokeback Mountain was a terrible movie.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0                                                  1\n",
       "6913  0                     Brokeback Mountain was boring.\n",
       "6914  0       So Brokeback Mountain was really depressing.\n",
       "6915  0  As I sit here, watching the MTV Movie Awards, ...\n",
       "6916  0    Ok brokeback mountain is such a horrible movie.\n",
       "6917  0   Oh, and Brokeback Mountain was a terrible movie."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3943"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6918"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stemming\n",
    "import re\n",
    "import nltk\n",
    "#nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "corpus = []\n",
    "for i in range(0, len(dataset)):\n",
    "    review = re.sub('[^a-zA-Z]', ' ', X[i])\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    ps = PorterStemmer()\n",
    "    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]\n",
    "    review = ' '.join(review)\n",
    "    corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['da vinci code book awesom',\n",
       " 'first clive cussler ever read even book like relic da vinci code plausibl',\n",
       " 'like da vinci code lot',\n",
       " 'like da vinci code lot',\n",
       " 'like da vinci code ultimatli seem hold',\n",
       " 'even exagger midnight went wal mart buy da vinci code amaz cours',\n",
       " 'love da vinci code want someth better differ',\n",
       " 'thought da vinci code great kite runner',\n",
       " 'da vinci code actual good movi',\n",
       " 'thought da vinci code pretti good book',\n",
       " 'da vinci code one beauti movi ive ever seen',\n",
       " 'da vinci code amaz book get wrong',\n",
       " 'turn light radio enjoy da vinci code',\n",
       " 'da vinci code realli good',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'night da vinci code beauti mind',\n",
       " 'da vinci code awesom book',\n",
       " 'thing enjoy da vinci code',\n",
       " 'da vinci code slash amaz race',\n",
       " 'hey love da vinci code',\n",
       " 'also love da vinci code',\n",
       " 'realli enjoy da vinci code thought would disappoint book',\n",
       " 'like angel demon da vinci code',\n",
       " 'da vinci code realli good movi',\n",
       " 'yeah da vinci code awesom movi like pretti interest',\n",
       " 'realli like da vinci code',\n",
       " 'da vinci code amaz',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code backtori variou religi histor figur interest time scifi girl heart',\n",
       " 'book love da vinci code',\n",
       " 'went see da vinci code crazi awesom ian mckellen old gay husband',\n",
       " 'peopl say joe like da vinci code hard dan brown love da vinci code well enjoy bridget jone love da vinci code idea appeal take chick lit one arena genr yet explor read da vinci code awesom way da vinci code excel read normal read novel love da vinci code love read da vinci code tell da vinci code awesom book opinion may bit bias love da vinci code soundtrack quit pleas open minded love da vinci code much abl get equal enjoy see side read',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom book',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'oh beauti da vinci code',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom book',\n",
       " 'da vinci code awesom book',\n",
       " 'miss rip acoust music da vinci code',\n",
       " 'person neither hate love da vinci code',\n",
       " 'look amazingli fun possibl bit cold tell anyon da vinci code awesom book',\n",
       " 'da vinci code cours awesom book figur page',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'serious da vinci code awesom book',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'omg love da vinci code awesom book',\n",
       " 'da vinci code awesom book',\n",
       " 'da vinci code still one awesomest book ever read',\n",
       " 'love da vinci code though',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'shit love da vinci code',\n",
       " 'da vinci code realli awesom book also got two book dan brown',\n",
       " 'da vinci code awesom book finish',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'damn love da vinci code',\n",
       " 'da vinci code fuck awesom',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom sound probabl half crap fr',\n",
       " 'love da vinci code like',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code rais lot theolog question absurd',\n",
       " 'da vinci code awesom book',\n",
       " 'yeah da vinci code awesom',\n",
       " 'da vinci code awesom though',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom book',\n",
       " 'aaron mom aaron knew much love da vinci code took tour insid',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'love go see da vinci code probabl next week',\n",
       " 'da vinci code awesom',\n",
       " 'im sick book right must say da vinci code awesom',\n",
       " 'finish read da vinci code awesom book',\n",
       " 'da vinci code awesom far',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'read angel demon first onto da vinci code far awesom',\n",
       " 'da vinci code awesom book',\n",
       " 'da vinci code awesom',\n",
       " 'read da vinci code awesom angel demon even better wanna read dan brown book',\n",
       " 'da vinci code awesom',\n",
       " 'oh oh oh love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code illustr edit way go',\n",
       " 'love da vinci code wait movi',\n",
       " 'love da vinci code recent',\n",
       " 'love da vinci code',\n",
       " 'right also read love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'love read da vinci code',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'da vinci code look awesom',\n",
       " 'finish da vinci code awesom book',\n",
       " 'da vinci code pretti awesom like angel demon',\n",
       " 'love read da vinci code wait movi come',\n",
       " 'love da vinci code',\n",
       " 'da vinci code realli awesom book go get',\n",
       " 'love da vinci code thought angel demon pretti good right end kind lost bit',\n",
       " 'total love da vinci code ca n wait movi',\n",
       " 'person love da vinci code great thriller',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom book',\n",
       " 'da vinci code awesom book',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code interest think thing differ angl',\n",
       " 'da vinci code friggin awesom book',\n",
       " 'da vinci code amaz page left go',\n",
       " 'love undercov da vinci code',\n",
       " 'outta three love da vinci code',\n",
       " 'oh love book da vinci code',\n",
       " 'besid drowin da vinci code way awesom',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom alreadi',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'well da vinci code awesom',\n",
       " 'realli love da vinci code',\n",
       " 'book da vinci code awesom',\n",
       " 'saw da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'know love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'lynn jon theater da vinci code everyon luau awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'also da vinci code awesom',\n",
       " 'gasp love da vinci code',\n",
       " 'movi da vinci code awesom protest make sad know mani peopl share religion idiot',\n",
       " 'da vinci code like one awesomest book',\n",
       " 'sad may date sister enjoy time togeth absolut love da vinci code',\n",
       " 'da vinci code awesom mean jesu admir bachelor',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code one beauti movi',\n",
       " 'love da vinci code',\n",
       " 'beyond da vinci code beauti tome unauthor cours deal novel inspir',\n",
       " 'actual love da vinci code',\n",
       " 'saw da vinci code x men last stand day came one got talk x men da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'agre love da vinci code think ridicul peopl get panti bunch heresi blashpemi whatev hell els allegedli contain',\n",
       " 'da vinci code awesom lah',\n",
       " 'da vinci code awesom',\n",
       " 'love watch da vinci code soon',\n",
       " 'hard decid move ti see good one play would love see da vinci code hand hedg look funni',\n",
       " 'read book angel demon got hook finish two day like da vinci code',\n",
       " 'oh love da vinci code',\n",
       " 'back melbourn mom went see da vinci code total awesom left thing',\n",
       " 'hahash love da vinci code tooo',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code also awesom that given tom hank',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code',\n",
       " 'love da vinci code kick ass movi',\n",
       " 'went movi saw da vinci code honestli awesom movi ever seen',\n",
       " 'love da vinci code even though second book seri',\n",
       " 'da vinci code awesom opinion book movi',\n",
       " 'love da vinci code book movi',\n",
       " 'da vinci code sooo awesom good luck ur tenni tournament',\n",
       " 'da vinci code awesom',\n",
       " 'love hate america almost much love support da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'luv book da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'well da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom book finsh read',\n",
       " 'da vinci code awesom cannot even begin comprehend explain awesom hardcor awesom power awesom',\n",
       " 'ps da vinci code awesom movi',\n",
       " 'love da vinci code much',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
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       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'peopl worth know much love da vinci code',\n",
       " 'want desper love da vinci code film',\n",
       " 'da vinci code awesom wait read',\n",
       " 'da vinci code absolut awesom',\n",
       " 'da vinci code awesom movi',\n",
       " 'da vinci code awesom',\n",
       " 'love da vinci code',\n",
       " 'da vinci code awesom',\n",
       " 'da vinci code awesom',\n",
       " 'love luv lubb da vinci code',\n",
       " 'friday stay watch mission imposs amaz way',\n",
       " 'love mission imposs',\n",
       " 'mission imposs excit',\n",
       " 'ok time updat wow updat long time ok yeah watch hedg mission imposs awesom movi hammi rock',\n",
       " 'mission imposs amaz',\n",
       " 'first action theme play firework went mission imposs theme good accompani whistl bang colour lit sky',\n",
       " 'first action theme play firework went mission imposs theme good accompani whistl bang colour lit sky',\n",
       " 'mission imposs good unpredict spi movi like sort thing',\n",
       " 'love mission imposs scenario',\n",
       " 'mission imposs astonishingli good fat free much worth rent',\n",
       " ...]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 The Da Vinci Code book is just awesome.\n",
       "1       this was the first clive cussler i've ever rea...\n",
       "2                        i liked the Da Vinci Code a lot.\n",
       "3                        i liked the Da Vinci Code a lot.\n",
       "4       I liked the Da Vinci Code but it ultimatly did...\n",
       "5       that's not even an exaggeration ) and at midni...\n",
       "6       I loved the Da Vinci Code, but now I want some...\n",
       "7       i thought da vinci code was great, same with k...\n",
       "8           The Da Vinci Code is actually a good movie...\n",
       "9       I thought the Da Vinci Code was a pretty good ...\n",
       "10      The Da Vinci Code is one of the most beautiful...\n",
       "11      The Da Vinci Code is an * amazing * book, do n...\n",
       "12      then I turn on the light and the radio and enj...\n",
       "13                     The Da Vinci Code was REALLY good.\n",
       "14                               i love da vinci code....\n",
       "15                                i loved da vinci code..\n",
       "16      TO NIGHT:: THE DA VINCI CODE AND A BEAUTIFUL M...\n",
       "17               THE DA VINCI CODE is AN AWESOME BOOK....\n",
       "18                 Thing is, I enjoyed The Da Vinci Code.\n",
       "19                 very da vinci code slash amazing race.\n",
       "20                       Hey I loved The Da Vinci Code!..\n",
       "21                         also loved the da vinci code..\n",
       "22      I really enjoyed the Da Vinci Code but thought...\n",
       "23      I do like Angels and Demons more then The Da V...\n",
       "24             The Da Vinci Code was a really good movie.\n",
       "25      yeah, da vinci code is an awesome movie i like...\n",
       "26                       I really like The Da Vinci Code.\n",
       "27                              Da Vinci Code is amazing.\n",
       "28                       The Da Vinci Code was awesome...\n",
       "29      The Da Vinci Code's backtory on various religi...\n",
       "                              ...                        \n",
       "6888             Brokeback Mountain is fucking horrible..\n",
       "6889    Then snuck into Brokeback Mountain, which is t...\n",
       "6890    , she helped me bobbypin my insanely cool hat ...\n",
       "6891    My dad's being stupid about brokeback mountain...\n",
       "6892    Oh, and Brokeback Mountain is a TERRIBLE movie...\n",
       "6893                       Brokeback Mountain was boring.\n",
       "6894         So Brokeback Mountain was really depressing.\n",
       "6895    As I sit here, watching the MTV Movie Awards, ...\n",
       "6896      Ok brokeback mountain is such a horrible movie.\n",
       "6897     Oh, and Brokeback Mountain was a terrible movie.\n",
       "6898             Brokeback Mountain is fucking horrible..\n",
       "6899    Then snuck into Brokeback Mountain, which is t...\n",
       "6900    , she helped me bobbypin my insanely cool hat ...\n",
       "6901    My dad's being stupid about brokeback mountain...\n",
       "6902    Oh, and Brokeback Mountain is a TERRIBLE movie...\n",
       "6903                       Brokeback Mountain was boring.\n",
       "6904         So Brokeback Mountain was really depressing.\n",
       "6905    As I sit here, watching the MTV Movie Awards, ...\n",
       "6906      Ok brokeback mountain is such a horrible movie.\n",
       "6907     Oh, and Brokeback Mountain was a terrible movie.\n",
       "6908             Brokeback Mountain is fucking horrible..\n",
       "6909    Then snuck into Brokeback Mountain, which is t...\n",
       "6910    , she helped me bobbypin my insanely cool hat ...\n",
       "6911    My dad's being stupid about brokeback mountain...\n",
       "6912    Oh, and Brokeback Mountain is a TERRIBLE movie...\n",
       "6913                       Brokeback Mountain was boring.\n",
       "6914         So Brokeback Mountain was really depressing.\n",
       "6915    As I sit here, watching the MTV Movie Awards, ...\n",
       "6916      Ok brokeback mountain is such a horrible movie.\n",
       "6917     Oh, and Brokeback Mountain was a terrible movie.\n",
       "Name: 1, Length: 6918, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()\n",
    "X = cv.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gp/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Different classifiers - Unigram model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Naive Bayes\n",
      "Accuracy Score: 98.5549132948%\n",
      "Confusion Matrix: \n",
      "[[597  10]\n",
      " [ 10 767]]\n"
     ]
    }
   ],
   "source": [
    "# Fitting Multinomial Naive Bayes to the Training set\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "NB = MultinomialNB()\n",
    "NB.fit(X_train, y_train)\n",
    "y_pred = NB.predict(X_test)\n",
    "print('\\nNaive Bayes')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Support Vector Machine\n",
      "Accuracy Score: 99.4942196532%\n",
      "Confusion Matrix: \n",
      "[[603   4]\n",
      " [  3 774]]\n"
     ]
    }
   ],
   "source": [
    "# Fitting SVM classifier to the Training set\n",
    "from sklearn.svm import LinearSVC\n",
    "SVM = LinearSVC()\n",
    "SVM.fit(X_train, y_train)\n",
    "y_pred = SVM.predict(X_test)\n",
    "print('\\nSupport Vector Machine')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "K Nearest Neighbors\n",
      "Accuracy Score: 97.9046242775%\n",
      "Confusion Matrix: \n",
      "[[586  21]\n",
      " [  8 769]]\n"
     ]
    }
   ],
   "source": [
    "# Fitting K Nearest Neighbor classifier to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN = KNeighborsClassifier(n_neighbors = 3)\n",
    "KNN.fit(X_train, y_train)\n",
    "y_pred = KNN.predict(X_test)\n",
    "print('\\nK Nearest Neighbors')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1384, 1707)\n",
      "\n",
      "Logistic Regression\n",
      "Accuracy Score: 99.6387283237%\n",
      "Confusion Matrix: \n",
      "[[602   5]\n",
      " [  0 777]]\n"
     ]
    }
   ],
   "source": [
    "# Fitting Logistic Regression model to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)\n",
    "print(X_test.shape)\n",
    "print('\\nLogistic Regression')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest\n",
      "Accuracy Score: 98.7716763006%\n",
      "Confusion Matrix: \n",
      "[[595  12]\n",
      " [  5 772]]\n"
     ]
    }
   ],
   "source": [
    "# Fitting Random Forest Classifier to the Training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RF = RandomForestClassifier(n_estimators = 50, criterion = 'entropy', random_state = 0)\n",
    "RF.fit(X_train, y_train)\n",
    "y_pred = RF.predict(X_test)\n",
    "print('\\nRandom Forest')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test a custom review message\n",
      "Enter review to be analysed:  awesome book\n",
      "The review is predicted Positive\n"
     ]
    }
   ],
   "source": [
    "#Input Review\n",
    "print('\\nTest a custom review message')\n",
    "print('Enter review to be analysed: ', end=\" \")\n",
    "test = []\n",
    "test.append(input())\n",
    "test_dtm = cv.transform(test)\n",
    "predLabel = LR.predict(test_dtm)\n",
    "tags = ['Negative','Positive']\n",
    "#Display Output\n",
    "print('The review is predicted',tags[predLabel[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BIGRAMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(ngram_range = (2,2))\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Multinomial Naive Bayes to the Training set\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "NB = MultinomialNB()\n",
    "NB.fit(X_train, y_train)\n",
    "y_pred = NB.predict(X_test)\n",
    "print('\\nNaive Bayes')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting SVM classifier to the Training set\n",
    "from sklearn.svm import LinearSVC\n",
    "SVM = LinearSVC()\n",
    "SVM.fit(X_train, y_train)\n",
    "y_pred = SVM.predict(X_test)\n",
    "print('\\nSupport Vector Machine')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting K Nearest Neighbor classifier to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN = KNeighborsClassifier(n_neighbors = 3)\n",
    "KNN.fit(X_train, y_train)\n",
    "y_pred = KNN.predict(X_test)\n",
    "print('\\nK Nearest Neighbors')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Logistic Regression model to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)\n",
    "print('\\nLogistic Regression')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Random Forest Classifier to the Training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RF = RandomForestClassifier(n_estimators = 15, criterion = 'entropy', random_state = 0)\n",
    "RF.fit(X_train, y_train)\n",
    "y_pred = RF.predict(X_test)\n",
    "print('\\nRandom Forest')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TRIGRAMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_df = .85, max_features = 1500, ngram_range = (3,3))\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Multinomial Naive Bayes to the Training set\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "NB = MultinomialNB()\n",
    "NB.fit(X_train, y_train)\n",
    "y_pred = NB.predict(X_test)\n",
    "print('\\nNaive Bayes')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting SVM classifier to the Training set\n",
    "from sklearn.svm import LinearSVC\n",
    "SVM = LinearSVC()\n",
    "SVM.fit(X_train, y_train)\n",
    "y_pred = SVM.predict(X_test)\n",
    "print('\\nSupport Vector Machine')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting K Nearest Neighbor classifier to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN = KNeighborsClassifier(n_neighbors = 3)\n",
    "KNN.fit(X_train, y_train)\n",
    "y_pred = KNN.predict(X_test)\n",
    "print('\\nK Nearest Neighbors')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Logistic Regression model to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)\n",
    "print('\\nLogistic Regression')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Random Forest Classifier to the Training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RF = RandomForestClassifier(n_estimators = 15, criterion = 'entropy', random_state = 0)\n",
    "RF.fit(X_train, y_train)\n",
    "y_pred = RF.predict(X_test)\n",
    "print('\\nRandom Forest')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='accuracy.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"from sklearn.model_selection import GridSearchCV\n",
    "parameters = [{'max_df': [1, 0.95, 0.9, 0.85]}]\n",
    "grid_search = GridSearchCV(estimator = LR,\n",
    "                           param_grid = parameters,\n",
    "                           scoring = 'accuracy',\n",
    "                           cv = 10,\n",
    "                           n_jobs = -1)\n",
    "grid_search = grid_search.fit(X, y)\n",
    "best_accuracy = grid_search.best_score_\n",
    "best_parameters = grid_search.best_params_\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'max_df':(1,0.9,0.8), 'min_df':[0.1,0.2,0.3]}\n",
    "grid_search = GridSearchCV(estimator = LR,\n",
    "                           param_grid = parameters,\n",
    "                           scoring = 'accuracy',\n",
    "                           cv = 10,\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_search.fit(X_train, y_train).best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_df = .9)\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "\n",
    "# Fitting Logistic Regression model to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)\n",
    "print('\\nLogistic Regression')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_df = .8)\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "\n",
    "# Fitting Logistic Regression model to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)\n",
    "print('\\nLogistic Regression')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Bag of Words model\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_df = .7)\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "\n",
    "# Fitting Logistic Regression model to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)\n",
    "print('\\nLogistic Regression')\n",
    "print('Accuracy Score: ',metrics.accuracy_score(y_test,y_pred)*100,'%',sep='')\n",
    "print('Confusion Matrix: ',metrics.confusion_matrix(y_test,y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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