NLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLTK Downloader\n",
      "---------------------------------------------------------------------------\n",
      "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
      "---------------------------------------------------------------------------\n",
      "Downloader> d\n",
      "\n",
      "Download which package (l=list; x=cancel)?\n",
      "  Identifier> stopwords\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Downloading package stopwords to\n",
      "        C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n",
      "      Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------------------------------------------------\n",
      "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
      "---------------------------------------------------------------------------\n",
      "Downloader>  q\n"
     ]
    }
   ],
   "source": [
    "nltk.download_shell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-e983f374794d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'd' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [line.rstrip() for line in open('smsspamcollection/SMSSpamCollection')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5574\n"
     ]
    }
   ],
   "source": [
    "print(len(messages))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ham\\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ham\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
      "\n",
      "\n",
      "1 ham\tOk lar... Joking wif u oni...\n",
      "\n",
      "\n",
      "2 spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n",
      "\n",
      "\n",
      "3 ham\tU dun say so early hor... U c already then say...\n",
      "\n",
      "\n",
      "4 ham\tNah I don't think he goes to usf, he lives around here though\n",
      "\n",
      "\n",
      "5 spam\tFreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, Â£1.50 to rcv\n",
      "\n",
      "\n",
      "6 ham\tEven my brother is not like to speak with me. They treat me like aids patent.\n",
      "\n",
      "\n",
      "7 ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu Vettam)' has been set as your callertune for all Callers. Press *9 to copy your friends Callertune\n",
      "\n",
      "\n",
      "8 spam\tWINNER!! As a valued network customer you have been selected to receivea Â£900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.\n",
      "\n",
      "\n",
      "9 spam\tHad your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on 08002986030\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for mess_no,message in enumerate(messages[:10]):\n",
    "    print(mess_no,message)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = pd.read_csv('smsspamcollection/SMSSpamCollection',sep='\\t',names=['label','message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</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": [
       "  label                                            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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       label                 message\n",
       "count   5572                    5572\n",
       "unique     2                    5169\n",
       "top      ham  Sorry, I'll call later\n",
       "freq    4825                      30"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>4825</td>\n",
       "      <td>4516</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>747</td>\n",
       "      <td>653</td>\n",
       "      <td>Please call our customer service representativ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      message                                                               \n",
       "        count unique                                                top freq\n",
       "label                                                                       \n",
       "ham      4825   4516                             Sorry, I'll call later   30\n",
       "spam      747    653  Please call our customer service representativ...    4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.groupby('label').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages['length'] = messages['message'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</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>155</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>49</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>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
       "1   ham                      Ok lar... Joking wif u oni...      29\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "3   ham  U dun say so early hor... U c already then say...      49\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203a7134d90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "messages['length'].plot.hist(bins=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5572.000000\n",
       "mean       80.489950\n",
       "std        59.942907\n",
       "min         2.000000\n",
       "25%        36.000000\n",
       "50%        62.000000\n",
       "75%       122.000000\n",
       "max       910.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages['length'].describecribe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later..\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages[messages['length']==910]['message'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000203A73E5D00>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000203A740D0A0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "messages.hist(column='length',by='label',bins=60,figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "mess = 'Sample Message! Notice: it has a punctuation.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "nopunc = [c for c in mess if c not in string.punctuation]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S',\n",
       " 'a',\n",
       " 'm',\n",
       " 'p',\n",
       " 'l',\n",
       " 'e',\n",
       " ' ',\n",
       " 'M',\n",
       " 'e',\n",
       " 's',\n",
       " 's',\n",
       " 'a',\n",
       " 'g',\n",
       " 'e',\n",
       " ' ',\n",
       " 'N',\n",
       " 'o',\n",
       " 't',\n",
       " 'i',\n",
       " 'c',\n",
       " 'e',\n",
       " ' ',\n",
       " 'i',\n",
       " 't',\n",
       " ' ',\n",
       " 'h',\n",
       " 'a',\n",
       " 's',\n",
       " ' ',\n",
       " 'a',\n",
       " ' ',\n",
       " 'p',\n",
       " 'u',\n",
       " 'n',\n",
       " 'c',\n",
       " 't',\n",
       " 'u',\n",
       " 'a',\n",
       " 't',\n",
       " 'i',\n",
       " 'o',\n",
       " 'n']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nopunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "nopunc = ''.join(nopunc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sample Message Notice it has a punctuation'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nopunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sample', 'Message', 'Notice', 'it', 'has', 'a', 'punctuation']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nopunc.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_mess = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sample', 'Message', 'Notice', 'punctuation']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_mess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_process(mess):\n",
    "    \"\"\"\n",
    "    1.remove puncs\n",
    "    2.remove stopwords\n",
    "    3. return clean of text words\n",
    "    \"\"\"\n",
    "    nopunc = [char for char in mess if char not in string.punctuation]\n",
    "    nopunc = ''.join(nopunc)\n",
    "    clean_mess = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]\n",
    "    return clean_mess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>length</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>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</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>155</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>49</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>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  length\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
       "1   ham                      Ok lar... Joking wif u oni...      29\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "3   ham  U dun say so early hor... U c already then say...      49\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Go, jurong, point, crazy, Available, bugis, n...\n",
       "1                       [Ok, lar, Joking, wif, u, oni]\n",
       "2    [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n",
       "3        [U, dun, say, early, hor, U, c, already, say]\n",
       "4    [Nah, dont, think, goes, usf, lives, around, t...\n",
       "Name: message, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages['message'].head(5).apply(text_process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "bow_transformer = CountVectorizer(analyzer=text_process).fit(messages['message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11425\n"
     ]
    }
   ],
   "source": [
    "print(len(bow_transformer.vocabulary_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "mess4 = messages['message'][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U dun say so early hor... U c already then say...\n"
     ]
    }
   ],
   "source": [
    "print(mess4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "bow4 = bow_transformer.transform([mess4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 4068)\t2\n",
      "  (0, 4629)\t1\n",
      "  (0, 5261)\t1\n",
      "  (0, 6204)\t1\n",
      "  (0, 6222)\t1\n",
      "  (0, 7186)\t1\n",
      "  (0, 9554)\t2\n"
     ]
    }
   ],
   "source": [
    "print(bow4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'U'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_transformer.get_feature_names()[4068]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages_bow = bow_transformer.transform(messages['message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Sparse Matrix:  5572\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Sparse Matrix: ',messages_bow.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50548"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_bow.nnz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfid_transformer = TfidfTransformer().fit(messages_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf4=tfid_transformer.transform(bow4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 9554)\t0.5385626262927564\n",
      "  (0, 7186)\t0.4389365653379857\n",
      "  (0, 6222)\t0.3187216892949149\n",
      "  (0, 6204)\t0.29953799723697416\n",
      "  (0, 5261)\t0.29729957405868723\n",
      "  (0, 4629)\t0.26619801906087187\n",
      "  (0, 4068)\t0.40832589933384067\n"
     ]
    }
   ],
   "source": [
    "print(tfidf4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.527076498901426"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfid_transformer.idf_[bow_transformer.vocabulary_['university']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages_tfid = tfid_transformer.transform(messages_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_detect_model = MultinomialNB().fit(messages_tfid, messages['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ham'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_detect_model.predict(tfidf4)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_train,msg_test,label_train,label_test=train_test_split(messages['message'],messages['label'],test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=text_process)),\n",
    "    ('tfifd',TfidfTransformer()),\n",
    "    ('classifier',MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x00000203A8710940>,\n",
       "                                 binary=False, decode_error='strict',\n",
       "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
       "                                 input='content', lowercase=True, max_df=1.0,\n",
       "                                 max_features=None, min_df=1,\n",
       "                                 ngram_range=(1, 1), preprocessor=None,\n",
       "                                 stop_words=None, strip_accents=None,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, vocabulary=None)),\n",
       "                ('tfifd',\n",
       "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
       "                                  sublinear_tf=False, use_idf=True)),\n",
       "                ('classifier',\n",
       "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(msg_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(msg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.96      1.00      0.98      1461\n",
      "        spam       1.00      0.70      0.82       211\n",
      "\n",
      "    accuracy                           0.96      1672\n",
      "   macro avg       0.98      0.85      0.90      1672\n",
      "weighted avg       0.96      0.96      0.96      1672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(label_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}