18.  NLP(spam detection) -NLTK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nltk.download_gui()  # downloaded stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5574\n"
     ]
    }
   ],
   "source": [
    "messages = [line.rstrip() for line in open('./0 resources/SMSSpamCollection.csv')]\n",
    "print(len(messages))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
     ]
    }
   ],
   "source": [
    "for message_no, message in enumerate(messages[:3]):\n",
    "    print(message_no, message)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Due to the spacing we can tell that this is a TSV (\"tab separated values\") file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages=pd.read_csv(\"./0 resources/SMSSpamCollection.csv\",sep='\\t',\n",
    "                    names=[\"label\",\"message\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\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",
       "    </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",
       "</div>"
      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.describe()    # a lot of messages are repeated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "    </tr>\n",
       "  </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.groupby(\"label\").describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding a new feature -> len (number of letters in a text message)\n",
    "\n",
    "messages[\"length\"]=messages[\"message\"].apply(len)\n",
    "messages.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "messages['length'].plot(bins=50, kind='hist',figsize=(12,4)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.length.describe()   #check out the max column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the longest message could be a outlier\n",
    "messages[messages['length'] == 910]['message'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'ham'}>,\n",
       "       <AxesSubplot:title={'center':'spam'}>], dtype=object)"
      ]
     },
     "execution_count": 15,
     "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": [
    "# x-axis =\"length\", separated by label\n",
    "messages.hist(column='length', by='label', bins=50,figsize=(12,4))\n",
    "\n",
    "#from the plots below we can conclude that-> spam messages(130-170) tend to be longer than ham messages(10-50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sample message Notice it has punctuation'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample\n",
    "\n",
    "import string\n",
    "\n",
    "mess = 'Sample message! Notice: it has punctuation.'\n",
    "\n",
    "# Check characters to see if they are in punctuation\n",
    "nopunc = [char for char in mess if char not in string.punctuation]\n",
    "\n",
    "# Join the characters again to form the string.\n",
    "nopunc = ''.join(nopunc)\n",
    "nopunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sample', 'message', 'Notice', 'it', 'has', 'punctuation']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nopunc.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords.words('english')[0:10] # Show some stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sample', 'message', 'Notice', 'punctuation']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return word if lower case version of that word is not in stopwords\n",
    "\n",
    "clean_mess=[word for word in nopunc.split() if word.lower() not in stopwords.words(\"english\")]\n",
    "clean_mess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a func to carry out this task\n",
    "\n",
    "def text_process(mess):\n",
    "    \n",
    "    nopunc=[char for char in mess if char not in string.punctuation]\n",
    "    \n",
    "    nopunc=''.join(nopunc)\n",
    "    \n",
    "    return [word for word in nopunc.split() if word.lower() not in stopwords.words(\"english\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "Name: message, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages[\"message\"].head(3).apply(text_process)  #returns list of words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "\n",
    "bow_transformer=CountVectorizer(analyzer=text_process).fit(messages[\"message\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11425\n"
     ]
    }
   ],
   "source": [
    "print(len(bow_transformer.vocabulary_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U dun say so early hor... U c already then say...\n"
     ]
    }
   ],
   "source": [
    "message4 = messages['message'][3]\n",
    "print(message4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
      "(1, 11425)\n"
     ]
    }
   ],
   "source": [
    "bow4 = bow_transformer.transform([message4])\n",
    "print(bow4)\n",
    "print(bow4.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This means that there are seven unique words in message number 4 (after removing common stop words). Two of them appear twice, the rest only once. Let's go ahead and check and confirm which ones appear twice:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U\n",
      "say\n"
     ]
    }
   ],
   "source": [
    "print(bow_transformer.get_feature_names()[4068])\n",
    "print(bow_transformer.get_feature_names()[9554])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we can use .transform on our Bag-of-Words (bow) transformed object and transform the entire DataFrame of messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages_bow = bow_transformer.transform(messages['message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 11425)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_bow.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note:\n",
    "**TfidfVectorizer is used on sentences, while TfidfTransformer is used on an existing count matrix, such as one returned by CountVectorizer**\n",
    "\n",
    "**With Tfidftransformer you will compute word counts using CountVectorizer and then compute the IDF values and only then compute the Tf-idf scores. With Tfidfvectorizer you will do all three steps at once.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfTransformer()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #TF-IDF\n",
    "    \n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "tfidf_transformer= TfidfTransformer()\n",
    "tfidf_transformer.fit(messages_bow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "#sample-> how it looks\n",
    "tfidf4 = tfidf_transformer.transform(bow4)\n",
    "print(tfidf4)\n",
    "\n",
    "#tfidf value is now associated with this message -> bow4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#final after pre-processing\n",
    "\n",
    "messages_tfidf = tfidf_transformer.transform(messages_bow) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=messages_tfidf\n",
    "y=messages[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=4)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "spam_detect_model= LinearSVC(C=4)\n",
    "spam_detect_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### use train test split as a good practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9820627802690582"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_detect_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ham'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_detect_model.predict(messages_tfidf)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ham'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages[\"label\"][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using just CountVectorizer without analyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9901345291479821"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=CountVectorizer()\n",
    "\n",
    "X=cv.fit_transform(messages[\"message\"])\n",
    "y=messages[\"label\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "clf=LinearSVC()\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_train, msg_test, label_train, label_test =train_test_split(messages['message'], messages['label'], test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will use SciKit Learn's pipeline capabilities to store a pipeline of workflow. This will allow us to set up all the transformations that we will do to the data for future use. Let's see an example of how it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating pipeline object (imp part)\n",
    "# TfidfVectorizer can also be used directly to reduce steps\n",
    "\n",
    "pipeline= Pipeline([\n",
    "    (\"bow\",CountVectorizer(analyzer=text_process)),\n",
    "    (\"tfidf\",TfidfTransformer()),\n",
    "    (\"classifier\",LinearSVC(C=6)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function text_process at 0x7fa13e9c47a0>)),\n",
       "                ('tfidf', TfidfTransformer()), ('classifier', LinearSVC(C=6))])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(msg_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9874439461883409"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# improved performance with LinearSVC()\n",
    "\n",
    "pipeline.score(msg_test,label_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       1.00      0.99      0.99       987\n",
      "        spam       0.91      0.98      0.95       128\n",
      "\n",
      "    accuracy                           0.99      1115\n",
      "   macro avg       0.96      0.99      0.97      1115\n",
      "weighted avg       0.99      0.99      0.99      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "predictions=pipeline.predict(msg_test)\n",
    "print(classification_report(predictions,label_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv\n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham', 'ham', 'spam', ..., 'ham', 'ham', 'ham'], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(cv.transform(messages.message))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(textInput):\n",
    "    vectors=cv.transform(textInput)\n",
    "    return clf.predict(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham'], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict([  \"User input\"  ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham'], dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict([messages.message[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.message[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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