Spam_Dataset_using_RF_on_CountVectorizer_&_TF-IDF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Natural Language Processing\n",
    "\n",
    "NLP is a field in machine learning with the ability of a computer to understand, analyze, manipulate, and potentially generate human language."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (Natural Language Toolkit)NLTK \n",
    "NLTK is a popular open-source package in Python. Rather than building all tools from scratch, NLTK provides all common NLP Tasks.\n",
    "\n",
    "##### Installing NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\nirmala patel\\anaconda3\\lib\\site-packages (3.3)\n",
      "Requirement already satisfied: six in c:\\users\\nirmala patel\\anaconda3\\lib\\site-packages (from nltk) (1.11.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "twisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.\n",
      "You are using pip version 10.0.1, however version 19.0.3 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading and Exploring Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read raw data\n",
    "rawData = open(\"SMSSpamCollection.csv\").read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ï»¿ham,\"Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\"\\nham,Ok lar... Joking wif u oni...\\nspam,Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to re'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print raw data\n",
    "rawData[0:250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No.of rows: 5572\n",
      "No.of columns: (5572, 2)\n"
     ]
    },
    {
     "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>body_text</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                                          body_text\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_table('SMSSpamCollection', \n",
    "                   sep='\\t', \n",
    "                   header=None, \n",
    "                   names=['label', 'body_text'])\n",
    "\n",
    "print('No.of rows:', data.shape[0])\n",
    "print('No.of columns:', data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing Data\n",
    "\n",
    "Cleaning (or pre-processing) the data typically consists of a number of steps:\n",
    "\n",
    "#### 1. Remove punctuation\n",
    "\n",
    "Punctuation can provide grammatical context to a sentence which supports our understanding. But for our vectorizer which counts the number of words and not the context, it does not add value, so we remove all special characters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>body_text</th>\n",
       "      <th>body_text_clean</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>Go until jurong point crazy Available only in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</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",
       "      <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",
       "      <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",
       "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                          body_text  \\\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...   \n",
       "\n",
       "                                     body_text_clean  \n",
       "0  Go until jurong point crazy Available only in ...  \n",
       "1                            Ok lar Joking wif u oni  \n",
       "2  Free entry in 2 a wkly comp to win FA Cup fina...  \n",
       "3        U dun say so early hor U c already then say  \n",
       "4  Nah I dont think he goes to usf he lives aroun...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Function to remove Punctuation\n",
    "def remove_punct(text):\n",
    "    text_nopunct = \"\".join([char for char in text if char not in string.punctuation])# It will discard all punctuations\n",
    "    return text_nopunct\n",
    "\n",
    "data['body_text_clean'] = data['body_text'].apply(lambda x: remove_punct(x))\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Tokenization\n",
    "\n",
    "Tokenizing separates text into units such as sentences or words. It gives structure to previously unstructured text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>body_text</th>\n",
       "      <th>body_text_clean</th>\n",
       "      <th>body_text_tokenized</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>Go until jurong point crazy Available only in ...</td>\n",
       "      <td>[go, until, jurong, point, crazy, available, o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>Ok lar Joking wif u oni</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",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>[free, entry, in, 2, a, wkly, comp, to, win, f...</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>U dun say so early hor U c already then say</td>\n",
       "      <td>[u, dun, say, so, early, hor, u, c, already, t...</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>Nah I dont think he goes to usf he lives aroun...</td>\n",
       "      <td>[nah, i, dont, think, he, goes, to, usf, he, l...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                          body_text  \\\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...   \n",
       "\n",
       "                                     body_text_clean  \\\n",
       "0  Go until jurong point crazy Available only in ...   \n",
       "1                            Ok lar Joking wif u oni   \n",
       "2  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3        U dun say so early hor U c already then say   \n",
       "4  Nah I dont think he goes to usf he lives aroun...   \n",
       "\n",
       "                                 body_text_tokenized  \n",
       "0  [go, until, jurong, point, crazy, available, o...  \n",
       "1                     [ok, lar, joking, wif, u, oni]  \n",
       "2  [free, entry, in, 2, a, wkly, comp, to, win, f...  \n",
       "3  [u, dun, say, so, early, hor, u, c, already, t...  \n",
       "4  [nah, i, dont, think, he, goes, to, usf, he, l...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Function to Tokenize words\n",
    "def tokenize(text):\n",
    "    tokens = re.split('\\W+', text) #W+ means that either a word character (A-Za-z0-9_) or a dash (-) can go there.\n",
    "    return tokens\n",
    "\n",
    "data['body_text_tokenized'] = data['body_text_clean'].apply(lambda x: tokenize(x.lower())) \n",
    "#We convert to lower as Python is case-sensitive. \n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Remove stopwords\n",
    "\n",
    "Stopwords are common words that will likely appear in any text. They don’t tell us much about our data so we remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "\n",
    "stopword = nltk.corpus.stopwords.words('english')# All English Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>body_text</th>\n",
       "      <th>body_text_clean</th>\n",
       "      <th>body_text_tokenized</th>\n",
       "      <th>body_text_nostopwords</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>Go until jurong point crazy Available only in ...</td>\n",
       "      <td>[go, until, jurong, point, crazy, available, o...</td>\n",
       "      <td>[go, jurong, point, crazy, available, bugis, n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>Ok lar Joking wif u oni</td>\n",
       "      <td>[ok, lar, joking, wif, u, oni]</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",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>[free, entry, in, 2, a, wkly, comp, to, win, f...</td>\n",
       "      <td>[free, entry, 2, wkly, comp, win, fa, cup, fin...</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>U dun say so early hor U c already then say</td>\n",
       "      <td>[u, dun, say, so, early, hor, u, c, already, t...</td>\n",
       "      <td>[u, dun, say, early, hor, u, c, already, 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",
       "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
       "      <td>[nah, i, dont, think, he, goes, to, usf, he, l...</td>\n",
       "      <td>[nah, dont, think, goes, usf, lives, around, t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                          body_text  \\\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...   \n",
       "\n",
       "                                     body_text_clean  \\\n",
       "0  Go until jurong point crazy Available only in ...   \n",
       "1                            Ok lar Joking wif u oni   \n",
       "2  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3        U dun say so early hor U c already then say   \n",
       "4  Nah I dont think he goes to usf he lives aroun...   \n",
       "\n",
       "                                 body_text_tokenized  \\\n",
       "0  [go, until, jurong, point, crazy, available, o...   \n",
       "1                     [ok, lar, joking, wif, u, oni]   \n",
       "2  [free, entry, in, 2, a, wkly, comp, to, win, f...   \n",
       "3  [u, dun, say, so, early, hor, u, c, already, t...   \n",
       "4  [nah, i, dont, think, he, goes, to, usf, he, l...   \n",
       "\n",
       "                               body_text_nostopwords  \n",
       "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...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to remove Stopwords\n",
    "def remove_stopwords(tokenized_list):\n",
    "    text = [word for word in tokenized_list if word not in stopword]# To remove all stopwords\n",
    "    return text\n",
    "\n",
    "data['body_text_nostopwords'] = data['body_text_tokenized'].apply(lambda x: remove_stopwords(x))\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing Data: Stemming\n",
    "\n",
    "Stemming helps reduce a word to its stem form. It often makes sense to treat related words in the same way. It removes suffices, like “ing”, “ly”, “s”, etc. by a simple rule-based approach. It reduces the corpus of words but often the actual words get neglected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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>body_text</th>\n",
       "      <th>body_text_clean</th>\n",
       "      <th>body_text_tokenized</th>\n",
       "      <th>body_text_nostopwords</th>\n",
       "      <th>body_text_stemmed</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>Go until jurong point crazy Available only in ...</td>\n",
       "      <td>[go, until, jurong, point, crazy, available, o...</td>\n",
       "      <td>[go, jurong, point, crazy, available, bugis, n...</td>\n",
       "      <td>[go, jurong, point, crazi, avail, bugi, n, gre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>Ok lar Joking wif u oni</td>\n",
       "      <td>[ok, lar, joking, wif, u, oni]</td>\n",
       "      <td>[ok, lar, joking, wif, u, oni]</td>\n",
       "      <td>[ok, lar, joke, 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",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>[free, entry, in, 2, a, wkly, comp, to, win, f...</td>\n",
       "      <td>[free, entry, 2, wkly, comp, win, fa, cup, fin...</td>\n",
       "      <td>[free, entri, 2, wkli, comp, win, fa, cup, fin...</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>U dun say so early hor U c already then say</td>\n",
       "      <td>[u, dun, say, so, early, hor, u, c, already, t...</td>\n",
       "      <td>[u, dun, say, early, hor, u, c, already, say]</td>\n",
       "      <td>[u, dun, say, earli, hor, u, c, alreadi, 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",
       "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
       "      <td>[nah, i, dont, think, he, goes, to, usf, he, l...</td>\n",
       "      <td>[nah, dont, think, goes, usf, lives, around, t...</td>\n",
       "      <td>[nah, dont, think, goe, usf, live, around, tho...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                          body_text  \\\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...   \n",
       "\n",
       "                                     body_text_clean  \\\n",
       "0  Go until jurong point crazy Available only in ...   \n",
       "1                            Ok lar Joking wif u oni   \n",
       "2  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3        U dun say so early hor U c already then say   \n",
       "4  Nah I dont think he goes to usf he lives aroun...   \n",
       "\n",
       "                                 body_text_tokenized  \\\n",
       "0  [go, until, jurong, point, crazy, available, o...   \n",
       "1                     [ok, lar, joking, wif, u, oni]   \n",
       "2  [free, entry, in, 2, a, wkly, comp, to, win, f...   \n",
       "3  [u, dun, say, so, early, hor, u, c, already, t...   \n",
       "4  [nah, i, dont, think, he, goes, to, usf, he, l...   \n",
       "\n",
       "                               body_text_nostopwords  \\\n",
       "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",
       "\n",
       "                                   body_text_stemmed  \n",
       "0  [go, jurong, point, crazi, avail, bugi, n, gre...  \n",
       "1                       [ok, lar, joke, wif, u, oni]  \n",
       "2  [free, entri, 2, wkli, comp, win, fa, cup, fin...  \n",
       "3      [u, dun, say, earli, hor, u, c, alreadi, say]  \n",
       "4  [nah, dont, think, goe, usf, live, around, tho...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = nltk.PorterStemmer()\n",
    "\n",
    "def stemming(tokenized_text):\n",
    "    text = [ps.stem(word) for word in tokenized_text]\n",
    "    return text\n",
    "\n",
    "data['body_text_stemmed'] = data['body_text_nostopwords'].apply(lambda x: stemming(x))\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing Data: Lemmatizing\n",
    "\n",
    "Lemmatizing derives the canonical form (‘lemma’) of a word. i.e the root form. It is better than stemming as it uses a dictionary-based approach i.e a morphological analysis to the root word.eg: Entitling, Entitled->Entitle\n",
    "\n",
    "In Short, Stemming is typically faster as it simply chops off the end of the word, without understanding the context of the word. Lemmatizing is slower and more accurate as it takes an informed analysis with the context of the word in mind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to C:\\Users\\Nirmala\n",
      "[nltk_data]     Patel\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>body_text</th>\n",
       "      <th>body_text_clean</th>\n",
       "      <th>body_text_tokenized</th>\n",
       "      <th>body_text_nostopwords</th>\n",
       "      <th>body_text_stemmed</th>\n",
       "      <th>body_text_lemmatized</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>Go until jurong point crazy Available only in ...</td>\n",
       "      <td>[go, until, jurong, point, crazy, available, o...</td>\n",
       "      <td>[go, jurong, point, crazy, available, bugis, n...</td>\n",
       "      <td>[go, jurong, point, crazi, avail, bugi, n, gre...</td>\n",
       "      <td>[go, jurong, point, crazy, available, bugis, n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>Ok lar Joking wif u oni</td>\n",
       "      <td>[ok, lar, joking, wif, u, oni]</td>\n",
       "      <td>[ok, lar, joking, wif, u, oni]</td>\n",
       "      <td>[ok, lar, joke, wif, u, oni]</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",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>[free, entry, in, 2, a, wkly, comp, to, win, f...</td>\n",
       "      <td>[free, entry, 2, wkly, comp, win, fa, cup, fin...</td>\n",
       "      <td>[free, entri, 2, wkli, comp, win, fa, cup, fin...</td>\n",
       "      <td>[free, entry, 2, wkly, comp, win, fa, cup, fin...</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>U dun say so early hor U c already then say</td>\n",
       "      <td>[u, dun, say, so, early, hor, u, c, already, t...</td>\n",
       "      <td>[u, dun, say, early, hor, u, c, already, say]</td>\n",
       "      <td>[u, dun, say, earli, hor, u, c, alreadi, say]</td>\n",
       "      <td>[u, dun, say, early, hor, u, c, already, 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",
       "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
       "      <td>[nah, i, dont, think, he, goes, to, usf, he, l...</td>\n",
       "      <td>[nah, dont, think, goes, usf, lives, around, t...</td>\n",
       "      <td>[nah, dont, think, goe, usf, live, around, tho...</td>\n",
       "      <td>[nah, dont, think, go, usf, life, around, though]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                          body_text  \\\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...   \n",
       "\n",
       "                                     body_text_clean  \\\n",
       "0  Go until jurong point crazy Available only in ...   \n",
       "1                            Ok lar Joking wif u oni   \n",
       "2  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3        U dun say so early hor U c already then say   \n",
       "4  Nah I dont think he goes to usf he lives aroun...   \n",
       "\n",
       "                                 body_text_tokenized  \\\n",
       "0  [go, until, jurong, point, crazy, available, o...   \n",
       "1                     [ok, lar, joking, wif, u, oni]   \n",
       "2  [free, entry, in, 2, a, wkly, comp, to, win, f...   \n",
       "3  [u, dun, say, so, early, hor, u, c, already, t...   \n",
       "4  [nah, i, dont, think, he, goes, to, usf, he, l...   \n",
       "\n",
       "                               body_text_nostopwords  \\\n",
       "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",
       "\n",
       "                                   body_text_stemmed  \\\n",
       "0  [go, jurong, point, crazi, avail, bugi, n, gre...   \n",
       "1                       [ok, lar, joke, wif, u, oni]   \n",
       "2  [free, entri, 2, wkli, comp, win, fa, cup, fin...   \n",
       "3      [u, dun, say, earli, hor, u, c, alreadi, say]   \n",
       "4  [nah, dont, think, goe, usf, live, around, tho...   \n",
       "\n",
       "                                body_text_lemmatized  \n",
       "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, go, usf, life, around, though]  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk import WordNetLemmatizer\n",
    "\n",
    "wn = nltk.WordNetLemmatizer()\n",
    "\n",
    "def lemmatizing(tokenized_text):\n",
    "    text = [wn.lemmatize(word) for word in tokenized_text]\n",
    "    return text\n",
    "\n",
    "data['body_text_lemmatized'] = data['body_text_nostopwords'].apply(lambda x: lemmatizing(x))\n",
    "\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the cleaned Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We save the file as CSV \n",
    "# CSV(Comma-separated-value), It is easier to read compared to tsv(Tab-separated-value) and can be opened on Excel.\n",
    "\n",
    "data.to_csv(\"SMSSpamCollection_cleaned.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorizing Data\n",
    "\n",
    "Vectorizing is the process of encoding text as integers i.e. numeric form to create feature vectors so that machine learning algorithms can understand our data.\n",
    "\n",
    "### Vectorizing Data: Bag-Of-Words\n",
    "\n",
    "Bag of Words (BoW) or CountVectorizer describes the presence of words within the text data. It gives a result of 1 if present in the sentence and 0 if not present. It, therefore, creates a bag of words with a document-matrix count in each text document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "ps = nltk.PorterStemmer() #\n",
    "\n",
    "def count_punct(text):\n",
    "    count = sum([1 for char in text if char in string.punctuation])\n",
    "    return round(count/(len(text) - text.count(\" \")), 3)*100\n",
    "\n",
    "data['body_len'] = data['body_text'].apply(lambda x: len(x) - x.count(\" \"))\n",
    "data['punct%'] = data['body_text'].apply(lambda x: count_punct(x))\n",
    "\n",
    "\n",
    "#Function to remove punctuation, tokenize, remove stopwords, and stem\n",
    "\n",
    "def clean_text(text):\n",
    "    text = \"\".join([word.lower() for word in text if word not in string.punctuation])\n",
    "    tokens = re.split('\\W+', text)\n",
    "    text = [ps.stem(word) for word in tokens if word not in stopwords]\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572, 8109)\n",
      "['', '0', '008704050406', '0089mi', '0121', '01223585236', '01223585334', '0125698789', '02', '020603', '0207', '02070836089', '02072069400', '02073162414', '02085076972', '020903', '021', '050703', '0578', '06', '060505', '061104', '07008009200', '07046744435', '07090201529', '07090298926', '07099833605', '071104', '07123456789', '0721072', '07732584351', '07734396839', '07742676969', '07753741225', '0776xxxxxxx', '07786200117', '077xxx', '078', '07801543489', '07808', '07808247860', '07808726822', '07815296484', '07821230901', '0784987', '0789xxxxxxx', '0794674629107880867867', '0796xxxxxx', '07973788240', '07xxxxxxxxx', '0800', '08000407165', '08000776320', '08000839402', '08000930705', '08000938767', '08001950382', '08002888812', '08002986030', '08002986906', '08002988890', '08006344447', '0808', '08081263000', '08081560665', '0825', '0844', '08448350055', '08448714184', '0845', '08450542832', '08452810071', '08452810073', '08452810075over18', '0870', '08700621170150p', '08701213186', '08701237397', '08701417012', '08701417012150p', '0870141701216', '087016248', '08701752560', '087018728737', '0870241182716', '08702490080', '08702840625', '08702840625comuk', '08704439680', '08704439680tsc', '08706091795', '0870737910216yr', '08707500020', '08707509020', '0870753331018', '08707808226', '08708034412', '08708800282', '08709222922', '08709501522', '0870k', '087104711148', '08712101358', '08712103738', '0871212025016', '08712300220', '087123002209am7pm', '08712317606', '08712400200', '08712400603', '08712402050', '08712402578', '08712402779', '08712402902', '08712402972', '08712404000', '08712405020', '08712405022', '08712460324', '08712460324nat', '08712466669', '0871277810710pmin', '0871277810810', '0871277810910pmin', '087143423992stop', '087147123779am7pm', '08714712379', '08714712388', '08714712394', '08714712412', '08714714011', '08714719523', '08715203028', '08715203649', '08715203652', '08715203656', '08715203677', '08715203685', '08715203694', 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'thirunelvali', 'thisdon', 'thk', 'thkin', 'thm', 'thnk', 'thnq', 'thnx', 'tho', 'thoso', 'thot', 'thou', 'though', 'thought', 'thoughtsi', 'thousand', 'thout', 'thread', 'threat', 'three', 'threw', 'thriller', 'throat', 'throw', 'throwin', 'thrown', 'thru', 'thrurespect', 'tht', 'thu', 'thuglyf', 'thur', 'thursday', 'thx', 'ti', 'tick', 'ticket', 'tiempo', 'tiger', 'tight', 'tightli', 'tigress', 'tih', 'tiim', 'til', 'till', 'tim', 'time', 'timedhoni', 'timegud', 'timehop', 'timeslil', 'timey', 'timeyour', 'timi', 'timin', 'tini', 'tip', 'tire', 'tirunelvai', 'tirunelvali', 'tirupur', 'tisscotays', 'titl', 'titleso', 'tiwari', 'tix', 'tiz', 'tke', 'tkt', 'tlk', 'tm', 'tming', 'tmobil', 'tmorrowpl', 'tmr', 'tmrw', 'tmw', 'tnc', 'toa', 'toaday', 'tobacco', 'tobe', 'tocallshal', 'toclaim', 'today', 'todaybut', 'todaydo', 'todayfrom', 'todaygood', 'todayh', 'todaysundaysunday', 'todo', 'tog', 'togeth', 'tohar', 'toilet', 'tok', 'toke', 'token', 'tol', 'told', 'toldsh', 'toledo', 'toler', 'toleratbc', 'toll', 'tom', 'tomarrow', 'tome', 'tomeandsaidthi', 'tomo', 'tomoc', 'tomorro', 'tomorrow', 'tomorrowcal', 'tomorrowtoday', 'tomorw', 'ton', 'tone', 'tones2u', 'tones2youcouk', 'tonesrepli', 'tonex', 'tonght', 'tongu', 'tonight', 'tonit', 'tonitebusi', 'toniteth', 'tonsolitusaswel', 'took', 'tookplac', 'tool', 'toolet', 'tooo', 'toopray', 'toot', 'toothpast', 'tootsi', 'top', 'topic', 'topicsorri', 'toplay', 'toppoli', 'tor', 'torch', 'torrent', 'tortilla', 'tortur', 'tosend', 'toshiba', 'toss', 'tot', 'total', 'tote', 'touch', 'tough', 'toughest', 'tour', 'toward', 'town', 'towncud', 'towndontmatt', 'toxic', 'toyota', 'tp', 'track', 'trackmarqu', 'trade', 'tradit', 'traffic', 'train', 'trainner', 'tram', 'tranquil', 'transact', 'transcrib', 'transfer', 'transferacc', 'transfr', 'transport', 'trash', 'trauma', 'trav', 'travel', 'treacl', 'treadmil', 'treasur', 'treat', 'treatin', 'trebl', 'tree', 'trek', 'trend', 'tri', 'trial', 'trip', 'tripl', 'trishul', 'triumph', 'tron', 'troubl', 'troubleshoot', 'trouser', 'trubl', 'truck', 'true', 'truekdo', 'truffl', 'truli', 'truro', 'trust', 'truth', 'tryin', 'trywal', 'ts', 'tsandc', 'tsc', 'tscs08714740323', 'tscs087147403231winawkage16', 'tshirt', 'tsunami', 'tt', 'ttyl', 'tue', 'tuesday', 'tui', 'tuition', 'tul', 'tulip', 'tund', 'tune', 'tunji', 'turkey', 'turn', 'tuth', 'tv', 'tvhe', 'tvlol', 'twat', 'twelv', 'twenti', 'twice', 'twigg', 'twilight', 'twin', 'twink', 'twitter', 'two', 'tx', 'txt', 'txt250com', 'txtauction', 'txtauctiontxt', 'txtin', 'txting', 'txtjourney', 'txtno', 'txtx', 'tyler', 'type', 'typelyk', 'typic', 'u', 'u2moro', 'u4', 'uawakefeellikw', 'ubandu', 'ubi', 'ucal', 'ufind', 'ugadi', 'ugh', 'ugo', 'uh', 'uhhhhrmm', 'uif', 'uin', 'ujhhhhhhh', 'uk', 'ukmobiled', 'ukp2000', 'ull', 'ultim', 'ultimatum', 'um', 'umma', 'ummmawil', 'ummmmmaah', 'un', 'unabl', 'unbeliev', 'uncl', 'unclaim', 'uncomfort', 'uncondit', 'unconsci', 'unconvinc', 'uncount', 'uncut', 'underdtand', 'understand', 'understood', 'underwear', 'undrstnd', 'undrstndng', 'unemploy', 'unev', 'unfold', 'unfortun', 'unfortuntli', 'unhappi', 'uni', 'unicef', 'uniform', 'unintent', 'uniqu', 'uniquei', 'unit', 'univ', 'univers', 'unknown', 'unless', 'unlik', 'unlimit', 'unmit', 'unnecessarili', 'unni', 'unrecogn', 'unredeem', 'unsecur', 'unsold', 'unsoldmik', 'unsoldnow', 'unspoken', 'unsub', 'unsubscrib', 'unusu', 'uothrwis', 'up', 'up4', 'upcharg', 'upd8', 'updat', 'updatenow', 'upgrad', 'upgrdcentr', 'uphad', 'upload', 'upnot', 'upon', 'upset', 'upseti', 'upsetit', 'upstair', 'upto', 'uptown', 'upyeh', 'ur', 'ure', 'urfeel', 'urgent', 'urgentbut', 'urgentlyit', 'urgh', 'urgnt', 'urgoin', 'urgran', 'urin', 'url', 'urmomi', 'urn', 'urself', 'us', 'usb', 'usc', 'uscedu', 'use', 'useless', 'user', 'usf', 'usget', 'usher', 'uslet', 'usml', 'usno', 'uso', 'usp', 'usual', 'usualiam', 'uteru', 'utter', 'uu', 'uup', 'uv', 'uve', 'uwana', 'uwant', 'uworld', 'uxxxx', 'v', 'vaazhthukk', 'vagu', 'vai', 'vale', 'valentin', 'valid', 'valid12hr', 'valu', 'valuabl', 'valuemorn', 'varaya', 'vargu', 'vari', 'variou', 'varma', 'vasai', 'vat', 'vatian', 'vava', 'vco', 'vday', 'vega', 'veget', 'veggi', 'vehicl', 'velacheri', 'velli', 'velusami', 'venaam', 'venugop', 'verifi', 'version', 'versu', 'vettam', 'vewi', 'via', 'vibrant', 'vibrat', 'vic', 'victor', 'victoria', 'vid', 'video', 'videochat', 'videop', 'videophon', 'videosound', 'videosounds2', 'vidnot', 'view', 'vijay', 'vijaykanth', 'vikki', 'vikkyim', 'vilikkamt', 'vill', 'villa', 'villag', 'vinobanagar', 'violat', 'violenc', 'violet', 'vip', 'virgil', 'virgin', 'virtual', 'visa', 'visionsmscom', 'visit', 'visitne', 'visitor', 'vital', 'vitamin', 'viva', 'vivek', 'vivekanand', 'viveki', 'vl', 'vldo', 'voda', 'vodafon', 'vodka', 'voic', 'voicemail', 'voila', 'volcano', 'vomit', 'vomitin', 'vote', 'voucher', 'voucherstext', 'vpist', 'vpod', 'vri', 'vs', 'vth', 'vtire', 'vu', 'w', 'w111wx', 'w14rg', 'w1a', 'w1j', 'w1t1ji', 'w4', 'w45wq', 'w8in', 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'wellda', 'welli', 'welltak', 'wellyou', 'welp', 'wen', 'wendi', 'wenev', 'went', 'wenwecan', 'wer', 'werear', 'werebor', 'werent', 'wereth', 'wesley', 'west', 'western', 'westlif', 'westonzoyland', 'westshor', 'wet', 'wetherspoon', 'weve', 'wewa', 'whassup', 'what', 'whatev', 'whatsup', 'wheat', 'wheel', 'wheellock', 'when', 'whenev', 'whenevr', 'whenr', 'whenwher', 'where', 'wherear', 'wherebtw', 'wherev', 'wherevr', 'wherr', 'whether', 'whileamp', 'whilltak', 'whisper', 'white', 'whn', 'who', 'whole', 'whore', 'whose', 'whr', 'wi', 'wick', 'wicket', 'wicklow', 'wid', 'widelivecomindex', 'wif', 'wife', 'wifedont', 'wifehow', 'wifi', 'wihtuot', 'wikipediacom', 'wil', 'wild', 'wildest', 'wildlif', 'will', 'willpow', 'win', 'win150ppmx3age16', 'wind', 'windi', 'window', 'wine', 'wing', 'winner', 'winnersclub', 'winterston', 'wipe', 'wipro', 'wiproy', 'wire3net', 'wisdom', 'wise', 'wish', 'wishin', 'wishlist', 'wiskey', 'wit', 'withdraw', 'wither', 'within', 'without', 'witin', 'witot', 'witout', 'wiv', 'wizzl', 'wk', 'wkend', 'wkent150p16', 'wkg', 'wkli', 'wknd', 'wktxt', 'wlcome', 'wld', 'wmlid1b6a5ecef91ff937819firsttrue180430jul05', 'wmlid820554ad0a1705572711firsttru', 'wn', 'wnevr', 'wnt', 'wo', 'woah', 'wocay', 'woke', 'woken', 'woman', 'womdarful', 'women', 'wondar', 'wondarful', 'wonder', 'wont', 'woo', 'wood', 'woodland', 'woohoo', 'woot', 'woould', 'woozl', 'worc', 'word', 'wordcollect', 'wordnot', 'wordsevri', 'wordstart', 'work', 'workag', 'workand', 'workin', 'worklov', 'workout', 'world', 'worldgnun', 'worldmay', 'worldveri', 'worm', 'worri', 'worriedx', 'worryc', 'worryus', 'wors', 'worst', 'worth', 'worthless', 'wot', 'wotu', 'wotz', 'woul', 'would', 'woulda', 'wouldnt', 'wound', 'wow', 'wquestion', 'wrc', 'wreck', 'wrench', 'wright', 'write', 'writh', 'wrk', 'wrki', 'wrkin', 'wrking', 'wrld', 'wrnog', 'wrong', 'wrongli', 'wrongtak', 'wrote', 'ws', 'wt', 'wtc', 'wtf', 'wth', 'wthout', 'wud', 'wudnt', 'wuld', 'wuldnt', 'wun', 'www07781482378com', 'www4tcbiz', 'www80488biz', 'wwwapplausestorecom', 'wwwareyouuniquecouk', 'wwwasjesuscom', 'wwwb4utelecom', 'wwwbridalpetticoatdreamscouk', 'wwwcashbincouk', 'wwwclubmobycom', 'wwwclubzedcouk', 'wwwcnupdatescomnewslett', 'wwwcomuknet', 'wwwdbuknet', 'wwwflirtpartyu', 'wwwfullonsmscom', 'wwwgambtv', 'wwwgetzedcouk', 'wwwidewcom', 'wwwldewcom', 'wwwldewcom1win150ppmx3age16', 'wwwldewcom1win150ppmx3age16subscript', 'wwwldewcomsubs161win150ppmx3', 'wwwmovietriviatv', 'wwwmusictrivianet', 'wwworangecoukow', 'wwwphb1com', 'wwwregalportfoliocouk', 'wwwringtonekingcouk', 'wwwringtonescouk', 'wwwrtfsphostingcom', 'wwwsantacallingcom', 'wwwshortbreaksorguk', 'wwwsmsacubootydeli', 'wwwsmsacugoldvik', 'wwwsmsacuhmmross', 'wwwsmsacunat27081980', 'wwwsmsacunatalie2k9', 'wwwsmsconet', 'wwwtcbiz', 'wwwtelediscountcouk', 'wwwtextcompcom', 'wwwtextpodnet', 'wwwtklscom', 'wwwtxt2shopcom', 'wwwtxt43com', 'wwwtxt82228com', 'wwwtxttowincouk', 'wwwwin82050couk', 'wyli', 'x', 'x2', 'x29', 'x49', 'x49your', 'xafter', 'xam', 'xavier', 'xchat', 'xclusiveclubsaisai', 'xin', 'xma', 'xnet', 'xoxo', 'xt', 'xuhui', 'xx', 'xxsp', 'xxuk', 'xxx', 'xxxmobilemovieclub', 'xxxmobilemovieclubcomnqjkgighjjgcbl', 'xxxx', 'xxxxx', 'xxxxxx', 'xxxxxxx', 'xxxxxxxx', 'xxxxxxxxxxxxxx', 'xy', 'y87', 'ya', 'yago', 'yah', 'yahoo', 'yalrigu', 'yalru', 'yam', 'yan', 'yar', 'yard', 'yavnt', 'yaxx', 'yaxxx', 'yay', 'yck', 'yday', 'ye', 'yeah', 'yeahand', 'year', 'yeesh', 'yeh', 'yell', 'yellow', 'yelowi', 'yen', 'yeovil', 'yep', 'yer', 'yes165', 'yes434', 'yes440', 'yes762', 'yes910', 'yesbut', 'yesfrom', 'yesgauti', 'yesh', 'yesher', 'yesim', 'yesmum', 'yessura', 'yest', 'yesterday', 'yet', 'yetti', 'yetund', 'yi', 'yifeng', 'yiju', 'yijuehotmailcom', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yoher', 'yor', 'yorg', 'youani', 'youcarlo', 'youclean', 'youd', 'youdearwith', 'youdo', 'youhow', 'youi', 'youkwher', 'yould', 'youll', 'youmi', 'youmoney', 'young', 'younger', 'youphon', 'your', 'yourinclus', 'yourjob', 'youso', 'youthat', 'youto', 'youuuuu', 'youv', 'youwanna', 'youwhen', 'yovil', 'yowif', 'yoyyooo', 'yr', 'ystrdayic', 'yummi', 'yummmm', 'yun', 'yunni', 'yuo', 'yuou', 'yup', 'yupz', 'ywhere', 'z', 'zac', 'zaher', 'zealand', 'zebra', 'zed', 'zero', 'zhong', 'zindgi', 'zoe', 'zogtoriu', 'zoom', 'zouk', 'zyada', 'é', 'ü', 'üll', '〨ud']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect = CountVectorizer(analyzer=clean_text)\n",
    "X_counts = count_vect.fit_transform(data['body_text'])\n",
    "print(X_counts.shape)\n",
    "print(count_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BOW is applied on the body_text, so the count of each word is stored in the document matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5572x8109 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 50185 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 8109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  008704050406  0089mi  0121  01223585236  01223585334  0125698789  02  \\\n",
       "0  0  0             0       0     0            0            0           0   0   \n",
       "1  0  0             0       0     0            0            0           0   0   \n",
       "2  0  0             0       0     0            0            0           0   0   \n",
       "3  0  0             0       0     0            0            0           0   0   \n",
       "4  0  0             0       0     0            0            0           0   0   \n",
       "5  0  0             0       0     0            0            0           0   0   \n",
       "6  0  0             0       0     0            0            0           0   0   \n",
       "7  0  0             0       0     0            0            0           0   0   \n",
       "8  0  0             0       0     0            0            0           0   0   \n",
       "9  0  0             0       0     0            0            0           0   0   \n",
       "\n",
       "   020603 ...   zindgi  zoe  zogtoriu  zoom  zouk  zyada  é  ü  üll  〨ud  \n",
       "0       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "1       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "2       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "3       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "4       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "5       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "6       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "7       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "8       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "9       0 ...        0    0         0     0     0      0  0  0    0    0  \n",
       "\n",
       "[10 rows x 8109 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_counts_df = pd.DataFrame(X_counts.toarray(), columns=count_vect.get_feature_names())\n",
    "X_counts_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorizing Data: N-Grams\n",
    "\n",
    "***N-grams*** are simply all combinations of adjacent words or letters of length n that we can find in our source text. Ngrams with n=1 are called unigrams. Similarly, bigrams (n=2), trigrams (n=3) and so on can also be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unigrams usually don’t contain much information as compared to bigrams and trigrams. The basic principle behind n-grams is that they capture the letter or word is likely to follow the given word. The longer the n-gram (higher n), the more context you have to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Apply CountVectorizer (N-Grams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572, 8109)\n",
      "['', '0', '008704050406', '0089mi', '0121', '01223585236', '01223585334', '0125698789', '02', '020603', '0207', '02070836089', '02072069400', '02073162414', '02085076972', '020903', '021', '050703', '0578', '06', '060505', '061104', '07008009200', '07046744435', '07090201529', '07090298926', '07099833605', '071104', '07123456789', '0721072', '07732584351', '07734396839', '07742676969', '07753741225', '0776xxxxxxx', '07786200117', '077xxx', '078', '07801543489', '07808', '07808247860', '07808726822', '07815296484', '07821230901', '0784987', '0789xxxxxxx', '0794674629107880867867', '0796xxxxxx', '07973788240', '07xxxxxxxxx', '0800', '08000407165', '08000776320', '08000839402', '08000930705', '08000938767', '08001950382', '08002888812', '08002986030', '08002986906', '08002988890', '08006344447', '0808', '08081263000', '08081560665', '0825', '0844', '08448350055', '08448714184', '0845', '08450542832', '08452810071', '08452810073', '08452810075over18', '0870', '08700621170150p', '08701213186', '08701237397', '08701417012', '08701417012150p', '0870141701216', '087016248', '08701752560', '087018728737', '0870241182716', '08702490080', '08702840625', '08702840625comuk', '08704439680', '08704439680tsc', '08706091795', '0870737910216yr', '08707500020', '08707509020', '0870753331018', '08707808226', '08708034412', '08708800282', '08709222922', '08709501522', '0870k', '087104711148', '08712101358', '08712103738', '0871212025016', '08712300220', '087123002209am7pm', '08712317606', '08712400200', '08712400603', '08712402050', '08712402578', '08712402779', '08712402902', '08712402972', '08712404000', '08712405020', '08712405022', '08712460324', '08712460324nat', '08712466669', '0871277810710pmin', '0871277810810', '0871277810910pmin', '087143423992stop', '087147123779am7pm', '08714712379', '08714712388', '08714712394', '08714712412', '08714714011', '08714719523', '08715203028', '08715203649', '08715203652', '08715203656', '08715203677', '08715203685', '08715203694', '08715205273', '08715500022', '08715705022', '08717111821', '08717168528', '08717205546', '08717507382', '08717507711', '08717509990', '08717890890', '08717895698', '08717898035', '08718711108', '08718720201', '08718723815', '08718725756', '08718726270', '08718726270150gbpmtmsg18', '08718726970', '08718726971', '08718726978', '087187272008', '08718727868', '08718727870', '08718729755', '08718729758', '08718730555', '08718730666', '08718738001', '08718738002', '08718738034', '08719180219', '08719180248', '08719181259', '08719181503', '08719181513', '08719839835', '08719899217', '08719899229', '08719899230', '09041940223', '09050000301', '09050000332', '09050000460', '09050000555', '09050000878', '09050000928', '09050001295', '09050001808', '09050002311', '09050003091', '09050005321', '09050090044', '09050280520', '09053750005', '09056242159', '09057039994', '09058091854', '09058091870', '09058094454', '09058094455', '09058094507', '09058094565', '09058094583', '09058094594', '09058094597', '09058094599', '09058095107', '09058095201', '09058097189', '09058097218', '09058098002', '09058099801', '09061104276', '09061104283', '09061209465', '09061213237', '09061221061', '09061221066', '09061701444', '09061701461', '09061701851', '09061701939', '09061702893', '09061743386', '09061743806', '09061743810', '09061743811', '09061744553', '09061749602', '09061790121', '09061790125', '09061790126', '09063440451', '09063442151', '09063458130', '0906346330', '09064011000', '09064012103', '09064012160', '09064015307', '09064017295', '09064017305', '09064018838', '09064019014', '09064019788', '09065069120', '09065069154', '09065171142stopsms08', '09065171142stopsms08718727870150ppm', '09065174042', '09065394514', '09065394973', '09065989180', '09065989182', '09066350750', '09066358152', '09066358361', '09066361921', '09066362206', '09066362220', '09066362231', '09066364311', '09066364349', '09066364589', '09066368327', '09066368470', '09066368753', '09066380611', '09066382422', '09066612661', '09066649731from', '09066660100', '09071512432', '09071512433', '09071517866', '09077818151', '09090204448', '09090900040', '09094100151', '09094646631', '09094646899', '09095350301', '09096102316', '09099725823', '09099726395', '09099726429', '09099726481', '09099726553', '09111030116', '09111032124', '09701213186', '0anetwork', '1', '10', '100', '1000', '10000', '100000', '1000call', '100603', '100psm', '1010', '1013', '101mega', '1030', '10803', '10am', '10am7pm', '10am9pm', '10k', '10p', '10pmin', '10ppm', '10th', '11', '1120', '113', '1131', '11414', '1146', '1148', '116', '1172', '118pmsg', '11mth', '12', '120', '12000pe', '1205', '121', '1225', '123', '1230', '125', '1250', '125gift', '128', '12hour', '12hr', '12mth', '12price', '13', '130', '131004', '1327', '13404', '139', '140', '1405', '140ppm', '145', '1450', '146tf150p', '14thmarch', '150', '1500', '150ea', '150morefrmmob', '150msg', '150mtmsgrcvd18', '150p', '150pday', '150perweeksub', '150perwksub', '150pm', '150pmeg', '150pmin', '150pmmorefrommobile2bremovedmobypobox734ls27yf', '150pmsg', '150pmsgrcvd', '150pmsgrcvdhgsuite3422landsroww1j6hl', '150pmt', '150pmtmsg', '150pmtmsgrcvd18', '150ppermesssubscript', '150ppm', '150ppmpobox10183bhamb64x', '150ppmsg', '150prcvd', '150psm', '150ptext', '150ptone', '150pw', '150pwk', '150rcvd', '150week', '150wk', '151', '1526', '153', '15541', '15pmin', '16', '1680', '169', '16onli', '177', '18', '180', '181104', '1843', '186', '18onli', '18ptxt', '18yr', '195', '1956669', '1appledayno', '1childish', '1cup', '1da', '1er', '1hanuman', '1hi', '1hr', '1im', '1lemondayno', '1mcflyall', '1million', '1minmobsmor', '1minmobsmorelkpobox177hp51fl', '1minmoremobsemspobox45po139wa', '1month', '1pm', '1s', '1st', '1st4term', '1stchoicecouk', '1stone', '1tulsi', '1u', '1unbreak', '1winaweek', '1winawk', '1x150pwk', '1yf', '2', '20', '200', '2000', '20000', '2003', '2004', '2005', '2006', '2007', '2025050', '20f', '20m12aq', '20p', '20pmin', '21', '211104', '215', '21870000hi', '21m', '21st', '22', '220cm2', '23', '2309', '230ish', '24', '241', '241004', '247mp', '24hr', '24m', '24th', '25', '250', '250k', '255', '25f', '25p', '260305', '261004', '261104', '2667', '26th', '2703', '27603', '28', '2814032', '285', '28day', '28th', '28thfebtc', '290305', '29100', '29m', '2b', '2bajarangabali', '2bold', '2c', '2channel', '2day', '2daylov', '2docdpleas', '2end', '2exit', '2ez', '2getha', '2geva', '2go', '2godid', '2gthr', '2hook', '2hr', '2i', '2im', '2kbsubject', '2marrow', '2moro', '2morow', '2morro', '2morrow', '2morrowxxxx', '2mro', '2mrw', '2mwen', '2naughti', '2nd', '2nhite', '2night', '2nite', '2nitetel', '2optout', '2optoutd3wv', '2p', '2polic', '2px', '2rcv', '2stop', '2stoptx', '2stoptxt', '2u', '2u2', '2untam', '2watershd', '2waxsto', '2when', '2wk', '2wt', '2wu', '2year', '2yr', '3', '30', '300', '3000', '300603', '300603tcsbcm4235wc1n3xxcallcost150ppmmobilesvari', '300p', '3030', '30apr', '30pptxt', '30th', '31', '3100', '310303', '311004', '31pmsg150p', '32000', '3230', '32323', '326', '32f', '330', '3350', '3365', '350', '3510i', '35p', '3650', '36504', '3680', '3680offer', '373', '3750', '375max', '38', '391784', '399', '3aj', '3cover', '3d', '3day', '3db', '3g', '3gbp', '3hr', '3lion', '3lp', '3maruti', '3mile', '3min', '3mobil', '3optic', '3pound', '3qxj9', '3rd', '3sentiment', '3ss', '3u', '3unkempt', '3uz', '3wife', '3wk', '3x', '3xx', '4', '40', '400', '400minscal', '402', '4041', '40411', '40533', '40gb', '40mph', '415', '41685', '41782', '420', '42049', '4217', '42478', '42810', '430', '434', '44', '4403ldnw1a7rw18', '447797706009', '447801259231', '447per', '448712404000pleas', '449050000301', '449071512431', '449month', '45', '450', '450p', '450ppw', '450pw', '45239', '46', '47', '4712', '4742', '48', '4882', '48922', '49557', '4a', '4brekki', '4cook', '4d', '4eva', '4few', '4fil', '4get', '4give', '4got', '4goten', '4info', '4jx', '4lux', '4mi', '4mth', '4o', '4pavanaputra', '4press', '4rowdi', '4some1', '4tctxt', '4th', '4the', '4thnovbehind', '4txt120p', '4txtú120', '4u', '4ui', '4utxt', '4w', '4ward', '4wrd', '4year', '5', '50', '500', '5000', '500000', '505060', '50award', '50p', '515', '515pm', '5226', '5249', '526', '528', '530', '532', '54', '542', '545', '5903', '5digit', '5free', '5ful', '5garden', '5gentli', '5i', '5ish', '5k', '5min', '5ml', '5month', '5p', '5pm', '5sankatmochan', '5terror', '5th', '5wb', '5we', '5wkg', '5wq', '5year', '6', '600', '6031', '60400thousadi', '60p', '60pmin', '61200', '61610', '62220cncl', '6230', '62468', '62735', '630', '63mile', '645', '645pm', '650', '6669', '67441233', '68866', '69101', '69200', '69669', '69696', '69698', '69855', '6986618', '69876', '69888', '69888nyt', '69911', '69969', '69988', '6cruel', '6day', '6hl', '6housemaid', '6hr', '6ish', '6miss', '6month', '6pm', '6ramaduth', '6romant', '6th', '6time', '6wu', '6zf', '7', '700', '71', '725', '7250', '7250i', '730', '730ish', '730pm', '731', '74355', 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'witout', 'wiv', 'wizzl', 'wk', 'wkend', 'wkent150p16', 'wkg', 'wkli', 'wknd', 'wktxt', 'wlcome', 'wld', 'wmlid1b6a5ecef91ff937819firsttrue180430jul05', 'wmlid820554ad0a1705572711firsttru', 'wn', 'wnevr', 'wnt', 'wo', 'woah', 'wocay', 'woke', 'woken', 'woman', 'womdarful', 'women', 'wondar', 'wondarful', 'wonder', 'wont', 'woo', 'wood', 'woodland', 'woohoo', 'woot', 'woould', 'woozl', 'worc', 'word', 'wordcollect', 'wordnot', 'wordsevri', 'wordstart', 'work', 'workag', 'workand', 'workin', 'worklov', 'workout', 'world', 'worldgnun', 'worldmay', 'worldveri', 'worm', 'worri', 'worriedx', 'worryc', 'worryus', 'wors', 'worst', 'worth', 'worthless', 'wot', 'wotu', 'wotz', 'woul', 'would', 'woulda', 'wouldnt', 'wound', 'wow', 'wquestion', 'wrc', 'wreck', 'wrench', 'wright', 'write', 'writh', 'wrk', 'wrki', 'wrkin', 'wrking', 'wrld', 'wrnog', 'wrong', 'wrongli', 'wrongtak', 'wrote', 'ws', 'wt', 'wtc', 'wtf', 'wth', 'wthout', 'wud', 'wudnt', 'wuld', 'wuldnt', 'wun', 'www07781482378com', 'www4tcbiz', 'www80488biz', 'wwwapplausestorecom', 'wwwareyouuniquecouk', 'wwwasjesuscom', 'wwwb4utelecom', 'wwwbridalpetticoatdreamscouk', 'wwwcashbincouk', 'wwwclubmobycom', 'wwwclubzedcouk', 'wwwcnupdatescomnewslett', 'wwwcomuknet', 'wwwdbuknet', 'wwwflirtpartyu', 'wwwfullonsmscom', 'wwwgambtv', 'wwwgetzedcouk', 'wwwidewcom', 'wwwldewcom', 'wwwldewcom1win150ppmx3age16', 'wwwldewcom1win150ppmx3age16subscript', 'wwwldewcomsubs161win150ppmx3', 'wwwmovietriviatv', 'wwwmusictrivianet', 'wwworangecoukow', 'wwwphb1com', 'wwwregalportfoliocouk', 'wwwringtonekingcouk', 'wwwringtonescouk', 'wwwrtfsphostingcom', 'wwwsantacallingcom', 'wwwshortbreaksorguk', 'wwwsmsacubootydeli', 'wwwsmsacugoldvik', 'wwwsmsacuhmmross', 'wwwsmsacunat27081980', 'wwwsmsacunatalie2k9', 'wwwsmsconet', 'wwwtcbiz', 'wwwtelediscountcouk', 'wwwtextcompcom', 'wwwtextpodnet', 'wwwtklscom', 'wwwtxt2shopcom', 'wwwtxt43com', 'wwwtxt82228com', 'wwwtxttowincouk', 'wwwwin82050couk', 'wyli', 'x', 'x2', 'x29', 'x49', 'x49your', 'xafter', 'xam', 'xavier', 'xchat', 'xclusiveclubsaisai', 'xin', 'xma', 'xnet', 'xoxo', 'xt', 'xuhui', 'xx', 'xxsp', 'xxuk', 'xxx', 'xxxmobilemovieclub', 'xxxmobilemovieclubcomnqjkgighjjgcbl', 'xxxx', 'xxxxx', 'xxxxxx', 'xxxxxxx', 'xxxxxxxx', 'xxxxxxxxxxxxxx', 'xy', 'y87', 'ya', 'yago', 'yah', 'yahoo', 'yalrigu', 'yalru', 'yam', 'yan', 'yar', 'yard', 'yavnt', 'yaxx', 'yaxxx', 'yay', 'yck', 'yday', 'ye', 'yeah', 'yeahand', 'year', 'yeesh', 'yeh', 'yell', 'yellow', 'yelowi', 'yen', 'yeovil', 'yep', 'yer', 'yes165', 'yes434', 'yes440', 'yes762', 'yes910', 'yesbut', 'yesfrom', 'yesgauti', 'yesh', 'yesher', 'yesim', 'yesmum', 'yessura', 'yest', 'yesterday', 'yet', 'yetti', 'yetund', 'yi', 'yifeng', 'yiju', 'yijuehotmailcom', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yoher', 'yor', 'yorg', 'youani', 'youcarlo', 'youclean', 'youd', 'youdearwith', 'youdo', 'youhow', 'youi', 'youkwher', 'yould', 'youll', 'youmi', 'youmoney', 'young', 'younger', 'youphon', 'your', 'yourinclus', 'yourjob', 'youso', 'youthat', 'youto', 'youuuuu', 'youv', 'youwanna', 'youwhen', 'yovil', 'yowif', 'yoyyooo', 'yr', 'ystrdayic', 'yummi', 'yummmm', 'yun', 'yunni', 'yuo', 'yuou', 'yup', 'yupz', 'ywhere', 'z', 'zac', 'zaher', 'zealand', 'zebra', 'zed', 'zero', 'zhong', 'zindgi', 'zoe', 'zogtoriu', 'zoom', 'zouk', 'zyada', 'é', 'ü', 'üll', '〨ud']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "ngram_vect = CountVectorizer(ngram_range=(2,2),analyzer=clean_text) # It applies only bigram vectorizer\n",
    "X_counts = ngram_vect.fit_transform(data['body_text'])\n",
    "print(X_counts.shape)\n",
    "print(ngram_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N-Gram is applied on the body_text, so the count of each group words in a sentence word is stored in the document matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      0  008704050406  0089mi  0121  01223585236  01223585334  0125698789  02  \\\n",
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       "\n",
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       "\n",
       "[10 rows x 8109 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_counts_df = pd.DataFrame(X_counts.toarray(), columns=ngram_vect.get_feature_names())\n",
    "X_counts_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorizing Data: TF-IDF\n",
    "\n",
    "Used for search engine scoring, text summarization, document clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** The TF (term frequency) of a word is the frequency of a word (i.e. number of times it appears) in a document. When you know it, you’re able to see if you’re using a term too much or too little.***\n",
    "\n",
    "For example, when a 100 word document contains the term “cat” 12 times, the TF for the word ‘cat’ is\n",
    "\n",
    "TFcat = 12/100 i.e. 0.12\n",
    "\n",
    "***The IDF (inverse document frequency) of a word is the measure of how significant that term is in the whole corpus.***\n",
    "\n",
    "For example, say the term “cat” appears x amount of times in a 10,000,000 million document-sized corpus (i.e. web). Let’s assume there are 0.3 million documents that contain the term “cat”, then the IDF (i.e. log {DF}) is given by the total number of documents (10,000,000) divided by the number of documents containing the term “cat” (300,000).\n",
    "\n",
    "IDF (cat) = log (10,000,000/300,000) = 1.52\n",
    "\n",
    "∴ Wcat = (TF*IDF) cat = 0.12 * 1.52 = 0.182"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***TF*IDF is an information retrieval technique that weighs a term’s frequency (TF) and its inverse document frequency (IDF). Each word or term has its respective TF and IDF score. The product of the TF and IDF scores of a term is called the TF*IDF weight of that term.***\n",
    "\n",
    "***Put simply, the higher the TF*IDF score (weight), the rarer the term and vice versa.***\n",
    "\n",
    "The TF*IDF algorithm is used to weigh a keyword in any content and assign the importance to that keyword based on the number of times it appears in the document. More importantly, it checks how relevant the keyword is throughout the web, which is referred to as corpus.\n",
    "\n",
    "For a term t in a document d, the weight Wt,d of term t in document d is given by:\n",
    "\n",
    "Wt,d = TFt,d log (N/DFt)\n",
    "\n",
    "Where:\n",
    "\n",
    "TFt,d is the number of occurrences of t in document d.\n",
    "DFt is the number of documents containing the term t.\n",
    "N is the total number of documents in the corpus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Apply TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572, 8109)\n",
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'youso', 'youthat', 'youto', 'youuuuu', 'youv', 'youwanna', 'youwhen', 'yovil', 'yowif', 'yoyyooo', 'yr', 'ystrdayic', 'yummi', 'yummmm', 'yun', 'yunni', 'yuo', 'yuou', 'yup', 'yupz', 'ywhere', 'z', 'zac', 'zaher', 'zealand', 'zebra', 'zed', 'zero', 'zhong', 'zindgi', 'zoe', 'zogtoriu', 'zoom', 'zouk', 'zyada', 'é', 'ü', 'üll', '〨ud']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vect = TfidfVectorizer(analyzer=clean_text)\n",
    "X_tfidf = tfidf_vect.fit_transform(data['body_text'])\n",
    "print(X_tfidf.shape)\n",
    "print(tfidf_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF-IDF is applied on the body_text, so the relative count of each word in the sentences is stored in the document matrix. \n",
    "\n",
    "***Note***: Vectorizers outputs sparse matrices. Sparse Matrix is a matrix in which most entries are 0. In the interest of efficient storage, a sparse matrix will be stored by only storing the locations of the non-zero elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 8109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
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       "\n",
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       "9  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0  0.0  0.0  0.0   \n",
       "\n",
       "   〨ud  \n",
       "0  0.0  \n",
       "1  0.0  \n",
       "2  0.0  \n",
       "3  0.0  \n",
       "4  0.0  \n",
       "5  0.0  \n",
       "6  0.0  \n",
       "7  0.0  \n",
       "8  0.0  \n",
       "9  0.0  \n",
       "\n",
       "[10 rows x 8109 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tfidf_df = pd.DataFrame(X_tfidf.toarray(), columns=tfidf_vect.get_feature_names())\n",
    "X_tfidf_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "28           0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "29           0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "...          ...  ...     ... ...      ...  ...       ...   ...   ...    ...   \n",
      "5542         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5543         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5544         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5545         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5546         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5547         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5548         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5549         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5550         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5551         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5552         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5553         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5554         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5555         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5556         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5557         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5558         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5559         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5560         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5561         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5562         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5563         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5564         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5565         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5566         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5567         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5568         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5569         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5570         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "5571         0.0  0.0     0.0 ...      0.0  0.0       0.0   0.0   0.0    0.0   \n",
      "\n",
      "        é         ü  üll  〨ud  \n",
      "0     0.0  0.000000  0.0  0.0  \n",
      "1     0.0  0.000000  0.0  0.0  \n",
      "2     0.0  0.000000  0.0  0.0  \n",
      "3     0.0  0.000000  0.0  0.0  \n",
      "4     0.0  0.000000  0.0  0.0  \n",
      "5     0.0  0.000000  0.0  0.0  \n",
      "6     0.0  0.000000  0.0  0.0  \n",
      "7     0.0  0.000000  0.0  0.0  \n",
      "8     0.0  0.000000  0.0  0.0  \n",
      "9     0.0  0.000000  0.0  0.0  \n",
      "10    0.0  0.000000  0.0  0.0  \n",
      "11    0.0  0.000000  0.0  0.0  \n",
      "12    0.0  0.000000  0.0  0.0  \n",
      "13    0.0  0.000000  0.0  0.0  \n",
      "14    0.0  0.000000  0.0  0.0  \n",
      "15    0.0  0.000000  0.0  0.0  \n",
      "16    0.0  0.000000  0.0  0.0  \n",
      "17    0.0  0.000000  0.0  0.0  \n",
      "18    0.0  0.000000  0.0  0.0  \n",
      "19    0.0  0.000000  0.0  0.0  \n",
      "20    0.0  0.000000  0.0  0.0  \n",
      "21    0.0  0.000000  0.0  0.0  \n",
      "22    0.0  0.293435  0.0  0.0  \n",
      "23    0.0  0.000000  0.0  0.0  \n",
      "24    0.0  0.000000  0.0  0.0  \n",
      "25    0.0  0.000000  0.0  0.0  \n",
      "26    0.0  0.000000  0.0  0.0  \n",
      "27    0.0  0.000000  0.0  0.0  \n",
      "28    0.0  0.000000  0.0  0.0  \n",
      "29    0.0  0.000000  0.0  0.0  \n",
      "...   ...       ...  ...  ...  \n",
      "5542  0.0  0.000000  0.0  0.0  \n",
      "5543  0.0  0.000000  0.0  0.0  \n",
      "5544  0.0  0.000000  0.0  0.0  \n",
      "5545  0.0  0.000000  0.0  0.0  \n",
      "5546  0.0  0.000000  0.0  0.0  \n",
      "5547  0.0  0.000000  0.0  0.0  \n",
      "5548  0.0  0.000000  0.0  0.0  \n",
      "5549  0.0  0.000000  0.0  0.0  \n",
      "5550  0.0  0.000000  0.0  0.0  \n",
      "5551  0.0  0.000000  0.0  0.0  \n",
      "5552  0.0  0.000000  0.0  0.0  \n",
      "5553  0.0  0.000000  0.0  0.0  \n",
      "5554  0.0  0.000000  0.0  0.0  \n",
      "5555  0.0  0.000000  0.0  0.0  \n",
      "5556  0.0  0.000000  0.0  0.0  \n",
      "5557  0.0  0.000000  0.0  0.0  \n",
      "5558  0.0  0.000000  0.0  0.0  \n",
      "5559  0.0  0.000000  0.0  0.0  \n",
      "5560  0.0  0.000000  0.0  0.0  \n",
      "5561  0.0  0.000000  0.0  0.0  \n",
      "5562  0.0  0.000000  0.0  0.0  \n",
      "5563  0.0  0.000000  0.0  0.0  \n",
      "5564  0.0  0.000000  0.0  0.0  \n",
      "5565  0.0  0.000000  0.0  0.0  \n",
      "5566  0.0  0.000000  0.0  0.0  \n",
      "5567  0.0  0.000000  0.0  0.0  \n",
      "5568  0.0  0.335240  0.0  0.0  \n",
      "5569  0.0  0.000000  0.0  0.0  \n",
      "5570  0.0  0.000000  0.0  0.0  \n",
      "5571  0.0  0.000000  0.0  0.0  \n",
      "\n",
      "[5571 rows x 8109 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_tfidf_df.loc[(X_tfidf_df!=0).any(axis=1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering: Feature Creation\n",
    "\n",
    "***Feature engineering*** is the process of using domain knowledge of the data to create features that make machine learning algorithms work. It is like an art as it requires domain knowledge and it can tough to create features, but it can be fruitful for ML algorithm to predict results as they can be related to the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_table('SMSSpamCollection', \n",
    "                   sep='\\t', \n",
    "                   header=None, \n",
    "                   names=['label', 'body_text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create feature for text message length and % of punctuation in text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>body_text</th>\n",
       "      <th>body_len</th>\n",
       "      <th>punct%</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>92</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>128</td>\n",
       "      <td>4.7</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>39</td>\n",
       "      <td>15.4</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>49</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                          body_text  body_len  punct%\n",
       "0   ham  Go until jurong point, crazy.. Available only ...        92     9.8\n",
       "1   ham                      Ok lar... Joking wif u oni...        24    25.0\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...       128     4.7\n",
       "3   ham  U dun say so early hor... U c already then say...        39    15.4\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        49     4.1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "\n",
    "# Function to calculate length of message excluding space\n",
    "data['body_len'] = data['body_text'].apply(lambda x: len(x) - x.count(\" \"))\n",
    "\n",
    "data.head()\n",
    "\n",
    "def count_punct(text):\n",
    "    count = sum([1 for char in text if char in string.punctuation])\n",
    "    return round(count/(len(text) - text.count(\" \")), 3)*100\n",
    "\n",
    "data['punct%'] = data['body_text'].apply(lambda x: count_punct(x))\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ***body_len*** shows the length of words excluding whitespaces in a message body.\n",
    "\n",
    "* ***punct%*** shows the percentage of punctuation marks in a message body.\n",
    "\n",
    "##### Check If Features are good or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For Visualizing to check if message body length and % of punctuation is good feature or not.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "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": [
    "bins = np.linspace(0, 200, 40)\n",
    "\n",
    "plt.hist(data[data['label']=='spam']['body_len'], bins, alpha=0.5, normed=True, label='spam')\n",
    "plt.hist(data[data['label']=='ham']['body_len'], bins, alpha=0.5, normed=True, label='ham')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can clearly see that Spams have a high number of words as compared to Hams. So it’s a good feature to distinguish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "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": [
    "bins = np.linspace(0, 50, 40)\n",
    "\n",
    "plt.hist(data[data['label']=='spam']['punct%'], bins, alpha=0.5, normed=True, label='spam')\n",
    "plt.hist(data[data['label']=='ham']['punct%'], bins, alpha=0.5, normed=True, label='ham')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spam has a percentage of punctuations but not that far away from Ham. Surprising as at times spam emails can contain a lot of punctuation marks. But still, it can be identified as a good feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building ML Classifiers: Model selection\n",
    "\n",
    "We use an ***ensemble method*** of machine learning where multiple models are used and their combination produces better results than a single model(Support Vector Machine/Naive Bayes). Ensemble methods are the first choice for many Kaggle Competitions. ***Random Forest*** i.e multiple random decision trees are constructed and the aggregates of each tree are used for the final prediction. It can be used for classification as well as regression problems. It follows a bagging strategy.\n",
    "\n",
    "***Grid-search:*** It exhaustively searches overall parameter combinations in a given grid to determine the best model.\n",
    "\n",
    "***Cross-validation:*** It divides a data set into k subsets and repeat the method k times where a different subset is used as the test set i.e in each iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def clean_text(text):\n",
    "    text = \"\".join([word.lower() for word in text if word not in string.punctuation])\n",
    "    tokens = re.split('\\W+', text)\n",
    "    text = [ps.stem(word) for word in tokens if word not in stopwords]\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TF-IDF\n",
    "tfidf_vect = TfidfVectorizer(analyzer=clean_text)\n",
    "X_tfidf = tfidf_vect.fit_transform(data['body_text'])\n",
    "X_tfidf_feat = pd.concat([data['body_len'], data['punct%'], pd.DataFrame(X_tfidf.toarray())], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_len</th>\n",
       "      <th>punct%</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>8099</th>\n",
       "      <th>8100</th>\n",
       "      <th>8101</th>\n",
       "      <th>8102</th>\n",
       "      <th>8103</th>\n",
       "      <th>8104</th>\n",
       "      <th>8105</th>\n",
       "      <th>8106</th>\n",
       "      <th>8107</th>\n",
       "      <th>8108</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>15.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_len  punct%  0  1  2  3  4  5  6  7  ...   8099  8100  8101  8102  \\\n",
       "0        92     9.8  0  0  0  0  0  0  0  0  ...      0     0     0     0   \n",
       "1        24    25.0  0  0  0  0  0  0  0  0  ...      0     0     0     0   \n",
       "2       128     4.7  0  0  0  0  0  0  0  0  ...      0     0     0     0   \n",
       "3        39    15.4  0  0  0  0  0  0  0  0  ...      0     0     0     0   \n",
       "4        49     4.1  0  0  0  0  0  0  0  0  ...      0     0     0     0   \n",
       "\n",
       "   8103  8104  8105  8106  8107  8108  \n",
       "0     0     0     0     0     0     0  \n",
       "1     0     0     0     0     0     0  \n",
       "2     0     0     0     0     0     0  \n",
       "3     0     0     0     0     0     0  \n",
       "4     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 8111 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CountVectorizer\n",
    "count_vect = CountVectorizer(analyzer=clean_text)\n",
    "X_count = count_vect.fit_transform(data['body_text'])\n",
    "X_count_feat = pd.concat([data['body_len'], data['punct%'], pd.DataFrame(X_count.toarray())], axis=1)\n",
    "\n",
    "X_count_feat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29.379666</td>\n",
       "      <td>2.531687</td>\n",
       "      <td>0.323683</td>\n",
       "      <td>0.051139</td>\n",
       "      <td>None</td>\n",
       "      <td>150</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 150}</td>\n",
       "      <td>0.975785</td>\n",
       "      <td>0.974888</td>\n",
       "      <td>0.973968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.972362</td>\n",
       "      <td>0.003190</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53.790550</td>\n",
       "      <td>0.801180</td>\n",
       "      <td>0.522086</td>\n",
       "      <td>0.087328</td>\n",
       "      <td>90</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': 90, 'n_estimators': 300}</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.973991</td>\n",
       "      <td>0.973070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.972182</td>\n",
       "      <td>0.003644</td>\n",
       "      <td>2</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.999103</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.999103</td>\n",
       "      <td>0.000201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44.641423</td>\n",
       "      <td>0.495957</td>\n",
       "      <td>0.316264</td>\n",
       "      <td>0.009872</td>\n",
       "      <td>None</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 300}</td>\n",
       "      <td>0.975785</td>\n",
       "      <td>0.973094</td>\n",
       "      <td>0.973968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>0.002979</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.482992</td>\n",
       "      <td>0.152525</td>\n",
       "      <td>0.296593</td>\n",
       "      <td>0.036598</td>\n",
       "      <td>90</td>\n",
       "      <td>150</td>\n",
       "      <td>{'max_depth': 90, 'n_estimators': 150}</td>\n",
       "      <td>0.975785</td>\n",
       "      <td>0.974888</td>\n",
       "      <td>0.971275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.971644</td>\n",
       "      <td>0.003252</td>\n",
       "      <td>4</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.998429</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.998654</td>\n",
       "      <td>0.998833</td>\n",
       "      <td>0.000298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.112491</td>\n",
       "      <td>0.183310</td>\n",
       "      <td>0.170155</td>\n",
       "      <td>0.009737</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 90, 'n_estimators': 10}</td>\n",
       "      <td>0.973991</td>\n",
       "      <td>0.974888</td>\n",
       "      <td>0.973070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.971464</td>\n",
       "      <td>0.003190</td>\n",
       "      <td>5</td>\n",
       "      <td>0.997532</td>\n",
       "      <td>0.997756</td>\n",
       "      <td>0.998205</td>\n",
       "      <td>0.997308</td>\n",
       "      <td>0.997084</td>\n",
       "      <td>0.997577</td>\n",
       "      <td>0.000386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "10      29.379666      2.531687         0.323683        0.051139   \n",
       "8       53.790550      0.801180         0.522086        0.087328   \n",
       "11      44.641423      0.495957         0.316264        0.009872   \n",
       "7       26.482992      0.152525         0.296593        0.036598   \n",
       "6        4.112491      0.183310         0.170155        0.009737   \n",
       "\n",
       "   param_max_depth param_n_estimators  \\\n",
       "10            None                150   \n",
       "8               90                300   \n",
       "11            None                300   \n",
       "7               90                150   \n",
       "6               90                 10   \n",
       "\n",
       "                                      params  split0_test_score  \\\n",
       "10  {'max_depth': None, 'n_estimators': 150}           0.975785   \n",
       "8     {'max_depth': 90, 'n_estimators': 300}           0.977578   \n",
       "11  {'max_depth': None, 'n_estimators': 300}           0.975785   \n",
       "7     {'max_depth': 90, 'n_estimators': 150}           0.975785   \n",
       "6      {'max_depth': 90, 'n_estimators': 10}           0.973991   \n",
       "\n",
       "    split1_test_score  split2_test_score       ...         mean_test_score  \\\n",
       "10           0.974888           0.973968       ...                0.972362   \n",
       "8            0.973991           0.973070       ...                0.972182   \n",
       "11           0.973094           0.973968       ...                0.972003   \n",
       "7            0.974888           0.971275       ...                0.971644   \n",
       "6            0.974888           0.973070       ...                0.971464   \n",
       "\n",
       "    std_test_score  rank_test_score  split0_train_score  split1_train_score  \\\n",
       "10        0.003190                1            1.000000            1.000000   \n",
       "8         0.003644                2            0.999327            0.999103   \n",
       "11        0.002979                3            1.000000            1.000000   \n",
       "7         0.003252                4            0.998878            0.998429   \n",
       "6         0.003190                5            0.997532            0.997756   \n",
       "\n",
       "    split2_train_score  split3_train_score  split4_train_score  \\\n",
       "10            1.000000            1.000000            1.000000   \n",
       "8             0.998878            0.998878            0.999327   \n",
       "11            1.000000            1.000000            1.000000   \n",
       "7             0.998878            0.999327            0.998654   \n",
       "6             0.998205            0.997308            0.997084   \n",
       "\n",
       "    mean_train_score  std_train_score  \n",
       "10          1.000000         0.000000  \n",
       "8           0.999103         0.000201  \n",
       "11          1.000000         0.000000  \n",
       "7           0.998833         0.000298  \n",
       "6           0.997577         0.000386  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "param = {'n_estimators': [10, 150, 300],\n",
    "        'max_depth': [30, 60, 90, None]}\n",
    "\n",
    "gs = GridSearchCV(rf, param, cv=5, n_jobs=-1)# n_jobs=-1 for parallelizing search\n",
    "gs_fit = gs.fit(X_count_feat, data['label'])\n",
    "\n",
    "pd.DataFrame(gs_fit.cv_results_).sort_values('mean_test_score', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean_test_score for ***n_estimators = 300*** and ***max_depth = 90*** gives the best result of ***97.7578%***. Where n_estimators is the number of trees in the forest.(group of decision trees) and max_depth is the max number of levels in each decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 8111)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_count_feat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split into train/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = X_count_feat\n",
    "y = data['label']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final evaluation of Count Vectorizer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "from sklearn.metrics import accuracy_score as acs\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 1.0 / Recall: 0.852 / F1-Score: 0.92 / Accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=300, max_depth=90, n_jobs=-1)\n",
    "\n",
    "rf_cv = rf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_cv.predict(X_test)\n",
    "\n",
    "precision, recall, fscore, train_support = score(y_test, y_pred, pos_label='spam', average='binary')\n",
    "\n",
    "print('Precision: {} / Recall: {} / F1-Score: {} / Accuracy: {}'.format(\n",
    "    round(precision, 3), round(recall, 3), round(fscore,3), round(acs(y_test,y_pred), 3)))\n",
    "\n",
    "\n",
    "# Making the Confusion Matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "class_label = [\"ham\", \"spam\"]\n",
    "df_cm = pd.DataFrame(cm, index=class_label,columns=class_label)\n",
    "sns.heatmap(df_cm, annot=True, fmt='d')\n",
    "plt.title(\"Confusion Matrix for Count Vectorizer model\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For TF-IDFVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\Nirmala Patel\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>49.031429</td>\n",
       "      <td>0.380914</td>\n",
       "      <td>0.455482</td>\n",
       "      <td>0.049246</td>\n",
       "      <td>90</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': 90, 'n_estimators': 300}</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.975785</td>\n",
       "      <td>0.974865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.973798</td>\n",
       "      <td>0.002980</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.999103</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.999551</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.999148</td>\n",
       "      <td>0.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.760606</td>\n",
       "      <td>0.431351</td>\n",
       "      <td>0.303626</td>\n",
       "      <td>0.030421</td>\n",
       "      <td>90</td>\n",
       "      <td>150</td>\n",
       "      <td>{'max_depth': 90, 'n_estimators': 150}</td>\n",
       "      <td>0.978475</td>\n",
       "      <td>0.975785</td>\n",
       "      <td>0.973070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.973439</td>\n",
       "      <td>0.003349</td>\n",
       "      <td>2</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.998654</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.999103</td>\n",
       "      <td>0.000284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.844095</td>\n",
       "      <td>0.282093</td>\n",
       "      <td>0.160465</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 90, 'n_estimators': 10}</td>\n",
       "      <td>0.970404</td>\n",
       "      <td>0.979372</td>\n",
       "      <td>0.975763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.973259</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>3</td>\n",
       "      <td>0.997532</td>\n",
       "      <td>0.997756</td>\n",
       "      <td>0.998654</td>\n",
       "      <td>0.997084</td>\n",
       "      <td>0.998878</td>\n",
       "      <td>0.997981</td>\n",
       "      <td>0.000680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>41.423028</td>\n",
       "      <td>0.634653</td>\n",
       "      <td>0.302734</td>\n",
       "      <td>0.030168</td>\n",
       "      <td>None</td>\n",
       "      <td>300</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 300}</td>\n",
       "      <td>0.976682</td>\n",
       "      <td>0.973094</td>\n",
       "      <td>0.974865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.973080</td>\n",
       "      <td>0.003009</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26.452103</td>\n",
       "      <td>1.699623</td>\n",
       "      <td>0.317937</td>\n",
       "      <td>0.063768</td>\n",
       "      <td>None</td>\n",
       "      <td>150</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 150}</td>\n",
       "      <td>0.975785</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.973070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.972900</td>\n",
       "      <td>0.003749</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "8       49.031429      0.380914         0.455482        0.049246   \n",
       "7       25.760606      0.431351         0.303626        0.030421   \n",
       "6        3.844095      0.282093         0.160465        0.018732   \n",
       "11      41.423028      0.634653         0.302734        0.030168   \n",
       "10      26.452103      1.699623         0.317937        0.063768   \n",
       "\n",
       "   param_max_depth param_n_estimators  \\\n",
       "8               90                300   \n",
       "7               90                150   \n",
       "6               90                 10   \n",
       "11            None                300   \n",
       "10            None                150   \n",
       "\n",
       "                                      params  split0_test_score  \\\n",
       "8     {'max_depth': 90, 'n_estimators': 300}           0.977578   \n",
       "7     {'max_depth': 90, 'n_estimators': 150}           0.978475   \n",
       "6      {'max_depth': 90, 'n_estimators': 10}           0.970404   \n",
       "11  {'max_depth': None, 'n_estimators': 300}           0.976682   \n",
       "10  {'max_depth': None, 'n_estimators': 150}           0.975785   \n",
       "\n",
       "    split1_test_score  split2_test_score       ...         mean_test_score  \\\n",
       "8            0.975785           0.974865       ...                0.973798   \n",
       "7            0.975785           0.973070       ...                0.973439   \n",
       "6            0.979372           0.975763       ...                0.973259   \n",
       "11           0.973094           0.974865       ...                0.973080   \n",
       "10           0.977578           0.973070       ...                0.972900   \n",
       "\n",
       "    std_test_score  rank_test_score  split0_train_score  split1_train_score  \\\n",
       "8         0.002980                1            0.999327            0.999103   \n",
       "7         0.003349                2            0.998878            0.999327   \n",
       "6         0.003743                3            0.997532            0.997756   \n",
       "11        0.003009                4            1.000000            1.000000   \n",
       "10        0.003749                5            1.000000            1.000000   \n",
       "\n",
       "    split2_train_score  split3_train_score  split4_train_score  \\\n",
       "8             0.998878            0.999551            0.998878   \n",
       "7             0.998654            0.999327            0.999327   \n",
       "6             0.998654            0.997084            0.998878   \n",
       "11            1.000000            1.000000            1.000000   \n",
       "10            1.000000            1.000000            1.000000   \n",
       "\n",
       "    mean_train_score  std_train_score  \n",
       "8           0.999148         0.000262  \n",
       "7           0.999103         0.000284  \n",
       "6           0.997981         0.000680  \n",
       "11          1.000000         0.000000  \n",
       "10          1.000000         0.000000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "param = {'n_estimators': [10, 150, 300],\n",
    "        'max_depth': [30, 60, 90, None]}\n",
    "\n",
    "gs = GridSearchCV(rf, param, cv=5, n_jobs=-1)# n_jobs=-1 for parallelizing search\n",
    "gs_fit = gs.fit(X_tfidf_feat, data['label'])\n",
    "\n",
    "pd.DataFrame(gs_fit.cv_results_).sort_values('mean_test_score', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, the mean_test_score for ***n_estimators = 300*** and ***max_depth = 90*** gives the best result of ***97.7578%***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 8111)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tfidf_feat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572,)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split into train/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = X_tfidf_feat\n",
    "y = data['label']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final evaluation of TF-IDFVectorizer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "from sklearn.metrics import accuracy_score as acs\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 1.0 / Recall: 0.846 / F1-Score: 0.916 / Accuracy: 0.979\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=300, max_depth=90, n_jobs=-1)\n",
    "\n",
    "rf_tdidf = rf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_tdidf.predict(X_test)\n",
    "\n",
    "precision, recall, fscore, train_support = score(y_test, y_pred, pos_label='spam', average='binary')\n",
    "\n",
    "print('Precision: {} / Recall: {} / F1-Score: {} / Accuracy: {}'.format(\n",
    "    round(precision, 3), round(recall, 3), round(fscore,3), round(acs(y_test,y_pred), 3)))\n",
    "\n",
    "\n",
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "class_label = [\"ham\", \"spam\"]\n",
    "df_cm = pd.DataFrame(cm, index=class_label,columns=class_label)\n",
    "sns.heatmap(df_cm, annot=True, fmt='d')\n",
    "plt.title(\"Confusion Matrix for TF-IDFVectorizer model\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using ***Random Forest*** for ***TF-IDFVectorizer model*** gives us the following values of metrics:\n",
    "\n",
    "***Precision: 1.0 / Recall: 0.846 / F1-Score: 0.916 / Accuracy: 0.979***\n",
    "\n",
    "Confusion Matrix for ***TF-IDFVectorizer model*** tells us that we correctly predicted 966 hams and 126 spams.0 hams were incorrectly identified as spams and 23 spams were incorrectly predicted as hams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using ***Random Forest*** for ***Count Vectorizer Model*** gives us the following values of metrics:\n",
    "\n",
    "***Precision: 1.0 / Recall: 0.852 / F1-Score: 0.92 / Accuracy: 0.98***\n",
    "\n",
    "Confusion Matrix for ***Count Vectorizer Model*** tells us that we correctly predicted 966 hams and 127 spams.0 hams were incorrectly identified as spams and 22 spams were incorrectly predicted as hams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}