Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddcc3ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4fea0ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b7526ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('spam.csv',encoding = 'ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7677232e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>spam</td>\n",
       "      <td>Summers finally here! Fancy a chat or flirt wi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372</th>\n",
       "      <td>ham</td>\n",
       "      <td>1) Go to write msg 2) Put on Dictionary mode 3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2656</th>\n",
       "      <td>ham</td>\n",
       "      <td>Feel like trying kadeem again? :V</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>ham</td>\n",
       "      <td>\\Hi its Kate it was lovely to see you tonight ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>ham</td>\n",
       "      <td>Is there any training tomorrow?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2 Unnamed: 2  \\\n",
       "1455  spam  Summers finally here! Fancy a chat or flirt wi...        NaN   \n",
       "1372   ham  1) Go to write msg 2) Put on Dictionary mode 3...        NaN   \n",
       "2656   ham                  Feel like trying kadeem again? :V        NaN   \n",
       "777    ham  \\Hi its Kate it was lovely to see you tonight ...        NaN   \n",
       "2062   ham                    Is there any training tomorrow?        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "1455        NaN        NaN  \n",
       "1372        NaN        NaN  \n",
       "2656        NaN        NaN  \n",
       "777         NaN        NaN  \n",
       "2062        NaN        NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a1eb863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eed3d63e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. Data cleaning..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a71a307b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "301786ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'v1':'target','v2':'text'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "293c1c0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4594</th>\n",
       "      <td>ham</td>\n",
       "      <td>Yo sorry was in the shower sup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2603</th>\n",
       "      <td>ham</td>\n",
       "      <td>\\Im at arestaurant eating squid! i will be out...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2364</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok then no need to tell me anything i am going...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3666</th>\n",
       "      <td>ham</td>\n",
       "      <td>Honey ? Sweetheart ? Darling ? Sexy buns ? Sug...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5360</th>\n",
       "      <td>ham</td>\n",
       "      <td>Hey, iouri gave me your number, I'm wylie, rya...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     target                                               text Unnamed: 2  \\\n",
       "4594    ham                     Yo sorry was in the shower sup        NaN   \n",
       "2603    ham  \\Im at arestaurant eating squid! i will be out...        NaN   \n",
       "2364    ham  Ok then no need to tell me anything i am going...        NaN   \n",
       "3666    ham  Honey ? Sweetheart ? Darling ? Sexy buns ? Sug...        NaN   \n",
       "5360    ham  Hey, iouri gave me your number, I'm wylie, rya...        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "4594        NaN        NaN  \n",
       "2603        NaN        NaN  \n",
       "2364        NaN        NaN  \n",
       "3666        NaN        NaN  \n",
       "5360        NaN        NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9eab36a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['Unnamed: 3', 'Unnamed: 4'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "01b0a9fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   target      5572 non-null   object\n",
      " 1   text        5572 non-null   object\n",
      " 2   Unnamed: 2  50 non-null     object\n",
      "dtypes: object(3)\n",
      "memory usage: 130.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40b61bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nothing. I meant that once the money enters yo...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>spam</td>\n",
       "      <td>Guess who am I?This is the first time I create...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>ham</td>\n",
       "      <td>No no:)this is kallis home ground.amla home to...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>spam</td>\n",
       "      <td>I want some cock! My hubby's away, I need a re...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3805</th>\n",
       "      <td>spam</td>\n",
       "      <td>URGENT! We are trying to contact you. Last wee...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     target                                               text Unnamed: 2\n",
       "472     ham  Nothing. I meant that once the money enters yo...        NaN\n",
       "2429   spam  Guess who am I?This is the first time I create...        NaN\n",
       "1114    ham  No no:)this is kallis home ground.amla home to...        NaN\n",
       "4525   spam  I want some cock! My hubby's away, I need a re...        NaN\n",
       "3805   spam  URGENT! We are trying to contact you. Last wee...        NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "883c915b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0912a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e516e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']=encoder.fit_transform(df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f892aea3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text Unnamed: 2\n",
       "0       0  Go until jurong point, crazy.. Available only ...        NaN\n",
       "1       0                      Ok lar... Joking wif u oni...        NaN\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...        NaN\n",
       "3       0  U dun say so early hor... U c already then say...        NaN\n",
       "4       0  Nah I don't think he goes to usf, he lives aro...        NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dfbc289",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9f8bc24",
   "metadata": {},
   "outputs": [
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       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>Unnamed: 2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text Unnamed: 2\n",
       "0       0  Go until jurong point, crazy.. Available only ...        NaN\n",
       "1       0                      Ok lar... Joking wif u oni...        NaN\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...        NaN\n",
       "3       0  U dun say so early hor... U c already then say...        NaN\n",
       "4       0  Nah I don't think he goes to usf, he lives aro...        NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "af2f0849",
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "feafad09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target           0\n",
       "text             0\n",
       "Unnamed: 2    5522\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7785b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94174f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop_duplicates(keep = 'first')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91b5d9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8597d066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32af923f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. EDA (Exporatory data analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb6ec476",
   "metadata": {},
   "outputs": [
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       "<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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text Unnamed: 2\n",
       "0       0  Go until jurong point, crazy.. Available only ...        NaN\n",
       "1       0                      Ok lar... Joking wif u oni...        NaN\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...        NaN\n",
       "3       0  U dun say so early hor... U c already then say...        NaN\n",
       "4       0  Nah I don't think he goes to usf, he lives aro...        NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a3b219ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "963fedd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.pie(df['target'].value_counts(), labels = ['ham','spam'], autopct = \"%0.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0d917887",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data is imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "993161b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fef144b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e49a53d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading punkt: <urlopen error [WinError 10060] A\n",
      "[nltk_data]     connection attempt failed because the connected party\n",
      "[nltk_data]     did not properly respond after a period of time, or\n",
      "[nltk_data]     established connection failed because connected host\n",
      "[nltk_data]     has failed to respond>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6194412",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_char'] = df['text'].apply(len)"
   ]
  },
  {
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       "\n",
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  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5845f044",
   "metadata": {},
   "outputs": [],
   "source": [
    "#num of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "32a315b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Go, until, jurong, point, ,, crazy, .., Avail...\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, alrea...\n",
       "4       [Nah, I, do, n't, think, he, goes, to, usf, ,,...\n",
       "                              ...                        \n",
       "5567    [This, is, the, 2nd, time, we, have, tried, 2,...\n",
       "5568     [Will, Ì_, b, going, to, esplanade, fr, home, ?]\n",
       "5569    [Pity, ,, *, was, in, mood, for, that, ., So, ...\n",
       "5570    [The, guy, did, some, bitching, but, I, acted,...\n",
       "5571                  [Rofl, ., Its, true, to, its, name]\n",
       "Name: text, Length: 5169, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'].apply(lambda x: nltk.word_tokenize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9050be27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       24\n",
       "1        8\n",
       "2       37\n",
       "3       13\n",
       "4       15\n",
       "        ..\n",
       "5567    35\n",
       "5568     9\n",
       "5569    15\n",
       "5570    27\n",
       "5571     7\n",
       "Name: text, Length: 5169, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['text'].apply(lambda x: len(nltk.word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cfab9af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_words'] = df['text'].apply(lambda x: len(nltk.word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d30e8d47",
   "metadata": {},
   "outputs": [
    {
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       "   target                                               text Unnamed: 2  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...        NaN   \n",
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       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3       0  U dun say so early hor... U c already then say...        NaN   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "   num_char  num_words  \n",
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       "1        29          8  \n",
       "2       155         37  \n",
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       "4        61         15  "
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    "df.head()"
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   "cell_type": "code",
   "execution_count": 37,
   "id": "ed56394a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Go until jurong point, crazy.., Available onl...\n",
       "1                        [Ok lar..., Joking wif u oni...]\n",
       "2       [Free entry in 2 a wkly comp to win FA Cup fin...\n",
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       "4       [Nah I don't think he goes to usf, he lives ar...\n",
       "                              ...                        \n",
       "5567    [This is the 2nd time we have tried 2 contact ...\n",
       "5568              [Will Ì_ b going to esplanade fr home?]\n",
       "5569    [Pity, * was in mood for that., So...any other...\n",
       "5570    [The guy did some bitching but I acted like i'...\n",
       "5571                        [Rofl., Its true to its name]\n",
       "Name: text, Length: 5169, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df['text'].apply(lambda x: nltk.sent_tokenize(x))  #tokenize based on sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "43a5b50f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_sentences']=df['text'].apply(lambda x: len(nltk.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5847877b",
   "metadata": {},
   "outputs": [
    {
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       "   target                                               text Unnamed: 2  \\\n",
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       "3       0  U dun say so early hor... U c already then say...        NaN   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "   num_char  num_words  num_sentences  \n",
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       "4        61         15              1  "
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   "id": "f45272cc",
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    "df.drop(columns = ['Unnamed: 2'],inplace = True)"
   ]
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   "cell_type": "code",
   "execution_count": 41,
   "id": "5420a936",
   "metadata": {},
   "outputs": [
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       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...       155   \n",
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       "4       0  Nah I don't think he goes to usf, he lives aro...        61   \n",
       "\n",
       "   num_words  num_sentences  \n",
       "0         24              2  \n",
       "1          8              2  \n",
       "2         37              2  \n",
       "3         13              1  \n",
       "4         15              1  "
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   "execution_count": 42,
   "id": "a5be061b",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "      <th>mean</th>\n",
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       "      <td>1.965564</td>\n",
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       "      <th>std</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>220.000000</td>\n",
       "      <td>38.000000</td>\n",
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       "          num_char    num_words  num_sentences\n",
       "count  5169.000000  5169.000000    5169.000000\n",
       "mean     78.977945    18.455794       1.965564\n",
       "std      58.236293    13.324758       1.448541\n",
       "min       2.000000     1.000000       1.000000\n",
       "25%      36.000000     9.000000       1.000000\n",
       "50%      60.000000    15.000000       1.000000\n",
       "75%     117.000000    26.000000       2.000000\n",
       "max     910.000000   220.000000      38.000000"
      ]
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     "execution_count": 42,
     "metadata": {},
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   "source": [
    "df[['num_char','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "42594952",
   "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>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.459256</td>\n",
       "      <td>17.123782</td>\n",
       "      <td>1.820195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.358207</td>\n",
       "      <td>13.493970</td>\n",
       "      <td>1.383657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          num_char    num_words  num_sentences\n",
       "count  4516.000000  4516.000000    4516.000000\n",
       "mean     70.459256    17.123782       1.820195\n",
       "std      56.358207    13.493970       1.383657\n",
       "min       2.000000     1.000000       1.000000\n",
       "25%      34.000000     8.000000       1.000000\n",
       "50%      52.000000    13.000000       1.000000\n",
       "75%      90.000000    22.000000       2.000000\n",
       "max     910.000000   220.000000      38.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ham\n",
    "df[df['target']==0][['num_char','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2a6ecb23",
   "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>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.891271</td>\n",
       "      <td>27.667688</td>\n",
       "      <td>2.970904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.137753</td>\n",
       "      <td>7.008418</td>\n",
       "      <td>1.488425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_char   num_words  num_sentences\n",
       "count  653.000000  653.000000     653.000000\n",
       "mean   137.891271   27.667688       2.970904\n",
       "std     30.137753    7.008418       1.488425\n",
       "min     13.000000    2.000000       1.000000\n",
       "25%    132.000000   25.000000       2.000000\n",
       "50%    149.000000   29.000000       3.000000\n",
       "75%    157.000000   32.000000       4.000000\n",
       "max    224.000000   46.000000       9.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#spam\n",
    "df[df['target']==1][['num_char','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "be193b40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.12.2)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (3.5.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (1.21.5)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (1.3.5)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.34.4)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (21.3)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas>=0.25->seaborn) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\amit\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a7f89fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fd5ef445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_char', ylabel='Count'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (12,6))\n",
    "sns.histplot(df[df['target']==0]['num_char'])\n",
    "sns.histplot(df[df['target']==1]['num_char'],color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7952475e",
   "metadata": {},
   "source": [
    "# Data preprocessing\n",
    "1 lower case\n",
    "2 tokenization\n",
    "3 removing special character\n",
    "4 removing stop words and punctuation\n",
    "stemming\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1fc27713",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the whole function to do the preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1b2d17d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_text(text):\n",
    "    text = text.lower()\n",
    "    text = nltk.word_tokenize(text)\n",
    "    \n",
    "    y=[]\n",
    "    for i in text:\n",
    "        if i.isalnum():\n",
    "            y.append(i)\n",
    "    text = y[:]\n",
    "    y.clear()\n",
    "    \n",
    "    for i in text:\n",
    "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
    "             y.append(i)\n",
    "    \n",
    "    text = y[:]\n",
    "    y.clear()\n",
    "    \n",
    "    for i in text:\n",
    "        y.append(ps.stem(i))\n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "802b8778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords.words('english') # stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b7c1353d",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ps' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [54]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtransform_text\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBut i\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mll b going 2 sch on mon. My sis need 2 take smth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[1;32mIn [52]\u001b[0m, in \u001b[0;36mtransform_text\u001b[1;34m(text)\u001b[0m\n\u001b[0;32m     17\u001b[0m y\u001b[38;5;241m.\u001b[39mclear()\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m text:\n\u001b[1;32m---> 20\u001b[0m     y\u001b[38;5;241m.\u001b[39mappend(\u001b[43mps\u001b[49m\u001b[38;5;241m.\u001b[39mstem(i))\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(y)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'ps' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "08591fa1",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1ca74d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b7acfcdc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Idea will soon get converted to live:)'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'][2001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af47bed3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "ps.stem('loving')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "34596c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transform_text'] = df['text'].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "675bfeb0",
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "      <th>transform_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>go jurong point crazi avail bugi n great world...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>ok lar joke wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>u dun say earli hor u c alreadi say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>nah think goe usf live around though</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_char  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...       111   \n",
       "1       0                      Ok lar... Joking wif u oni...        29   \n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...       155   \n",
       "3       0  U dun say so early hor... U c already then say...        49   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...        61   \n",
       "\n",
       "   num_words  num_sentences                                     transform_text  \n",
       "0         24              2  go jurong point crazi avail bugi n great world...  \n",
       "1          8              2                              ok lar joke wif u oni  \n",
       "2         37              2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
       "3         13              1                u dun say earli hor u c alreadi say  \n",
       "4         15              1               nah think goe usf live around though  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eebcb42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0522e587",
   "metadata": {},
   "outputs": [],
   "source": [
    "# word cloud creation\n",
    "# 1. spam\n",
    "\n",
    "from wordcloud import WordCloud\n",
    "wc = WordCloud(width=500, height = 500, min_font_size = 8,background_color = 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ce64aeea",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_wc = wc.generate(df[df['target']== 1]['transform_text'].str.cat(sep = \" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7f7bf573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1db6c87e220>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "54ff4f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_wc = wc.generate(df[df['target']== 0]['transform_text'].str.cat(sep = \" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3443d22b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1db6cce7220>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(ham_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "62e5986c",
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "      <th>transform_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>go jurong point crazi avail bugi n great world...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>ok lar joke wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>u dun say earli hor u c alreadi say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>nah think goe usf live around though</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_char  \\\n",
       "0       0  Go until jurong point, crazy.. Available only ...       111   \n",
       "1       0                      Ok lar... Joking wif u oni...        29   \n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...       155   \n",
       "3       0  U dun say so early hor... U c already then say...        49   \n",
       "4       0  Nah I don't think he goes to usf, he lives aro...        61   \n",
       "\n",
       "   num_words  num_sentences                                     transform_text  \n",
       "0         24              2  go jurong point crazi avail bugi n great world...  \n",
       "1          8              2                              ok lar joke wif u oni  \n",
       "2         37              2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
       "3         13              1                u dun say earli hor u c alreadi say  \n",
       "4         15              1               nah think goe usf live around though  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6a9fae5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_corpus=[]\n",
    "for msg in df[df['target']==1]['transform_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0e7e305",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ac976221",
   "metadata": {},
   "outputs": [
    {
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       " 'ur',\n",
       " 'send',\n",
       " 'cash',\n",
       " '86688',\n",
       " 'cc',\n",
       " '08708800282',\n",
       " 'xma',\n",
       " 'offer',\n",
       " 'latest',\n",
       " 'motorola',\n",
       " 'sonyericsson',\n",
       " 'nokia',\n",
       " 'free',\n",
       " 'bluetooth',\n",
       " 'doubl',\n",
       " 'min',\n",
       " '1000',\n",
       " 'txt',\n",
       " 'orang',\n",
       " 'call',\n",
       " 'mobileupd8',\n",
       " '08000839402',\n",
       " 'discount',\n",
       " 'code',\n",
       " 'rp176781',\n",
       " 'stop',\n",
       " 'messag',\n",
       " 'repli',\n",
       " 'stop',\n",
       " 'custom',\n",
       " 'servic',\n",
       " '08717205546',\n",
       " 'thank',\n",
       " 'rington',\n",
       " 'order',\n",
       " 'refer',\n",
       " 't91',\n",
       " 'charg',\n",
       " 'gbp',\n",
       " '4',\n",
       " 'per',\n",
       " 'week',\n",
       " 'unsubscrib',\n",
       " 'anytim',\n",
       " 'call',\n",
       " 'custom',\n",
       " 'servic',\n",
       " '09057039994',\n",
       " 'doubl',\n",
       " 'min',\n",
       " 'txt',\n",
       " '4',\n",
       " '6month',\n",
       " 'free',\n",
       " 'bluetooth',\n",
       " 'orang',\n",
       " 'avail',\n",
       " 'soni',\n",
       " 'nokia',\n",
       " 'motorola',\n",
       " 'phone',\n",
       " 'call',\n",
       " 'mobileupd8',\n",
       " '08000839402',\n",
       " '4mth',\n",
       " 'half',\n",
       " 'price',\n",
       " 'orang',\n",
       " 'line',\n",
       " 'rental',\n",
       " 'latest',\n",
       " 'camera',\n",
       " 'phone',\n",
       " '4',\n",
       " 'free',\n",
       " 'phone',\n",
       " '11mth',\n",
       " 'call',\n",
       " 'mobilesdirect',\n",
       " 'free',\n",
       " '08000938767',\n",
       " 'updat',\n",
       " 'or2stoptxt',\n",
       " 'free',\n",
       " 'rington',\n",
       " 'text',\n",
       " 'first',\n",
       " '87131',\n",
       " 'poli',\n",
       " 'text',\n",
       " 'get',\n",
       " '87131',\n",
       " 'true',\n",
       " 'tone',\n",
       " 'help',\n",
       " '0845',\n",
       " '2814032',\n",
       " '16',\n",
       " '1st',\n",
       " 'free',\n",
       " 'tone',\n",
       " 'txt',\n",
       " 'stop',\n",
       " '100',\n",
       " 'date',\n",
       " 'servic',\n",
       " 'cal',\n",
       " 'l',\n",
       " '09064012103',\n",
       " 'box334sk38ch',\n",
       " 'free',\n",
       " 'entri',\n",
       " 'weekli',\n",
       " 'competit',\n",
       " 'text',\n",
       " 'word',\n",
       " 'win',\n",
       " '80086',\n",
       " '18',\n",
       " 'c',\n",
       " 'send',\n",
       " 'logo',\n",
       " '2',\n",
       " 'ur',\n",
       " 'lover',\n",
       " '2',\n",
       " 'name',\n",
       " 'join',\n",
       " 'heart',\n",
       " 'txt',\n",
       " 'love',\n",
       " 'name1',\n",
       " 'name2',\n",
       " 'mobno',\n",
       " 'eg',\n",
       " 'love',\n",
       " 'adam',\n",
       " 'eve',\n",
       " '07123456789',\n",
       " '87077',\n",
       " 'yahoo',\n",
       " 'pobox36504w45wq',\n",
       " 'txtno',\n",
       " '4',\n",
       " 'ad',\n",
       " '150p',\n",
       " 'someon',\n",
       " 'contact',\n",
       " 'date',\n",
       " 'servic',\n",
       " 'enter',\n",
       " 'phone',\n",
       " 'fanci',\n",
       " 'find',\n",
       " 'call',\n",
       " 'landlin',\n",
       " '09111032124',\n",
       " 'pobox12n146tf150p',\n",
       " 'urgent',\n",
       " 'mobil',\n",
       " 'number',\n",
       " 'award',\n",
       " 'prize',\n",
       " 'guarante',\n",
       " 'call',\n",
       " '09058094455',\n",
       " 'land',\n",
       " 'line',\n",
       " 'claim',\n",
       " 'valid',\n",
       " '12hr',\n",
       " 'congrat',\n",
       " 'nokia',\n",
       " '3650',\n",
       " 'video',\n",
       " 'camera',\n",
       " ...]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2fabf062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9939"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bb74b1ed",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'free': 191,\n",
       "         'entri': 21,\n",
       "         '2': 155,\n",
       "         'wkli': 9,\n",
       "         'comp': 8,\n",
       "         'win': 48,\n",
       "         'fa': 2,\n",
       "         'cup': 3,\n",
       "         'final': 14,\n",
       "         'tkt': 2,\n",
       "         '21st': 1,\n",
       "         'may': 6,\n",
       "         'text': 122,\n",
       "         '87121': 2,\n",
       "         'receiv': 31,\n",
       "         'question': 9,\n",
       "         'std': 6,\n",
       "         'txt': 141,\n",
       "         'rate': 26,\n",
       "         'c': 45,\n",
       "         'appli': 24,\n",
       "         '08452810075over18': 1,\n",
       "         'freemsg': 14,\n",
       "         'hey': 5,\n",
       "         'darl': 2,\n",
       "         '3': 20,\n",
       "         'week': 49,\n",
       "         'word': 21,\n",
       "         'back': 20,\n",
       "         'like': 12,\n",
       "         'fun': 8,\n",
       "         'still': 5,\n",
       "         'tb': 1,\n",
       "         'ok': 5,\n",
       "         'xxx': 10,\n",
       "         'chg': 2,\n",
       "         'send': 60,\n",
       "         'rcv': 2,\n",
       "         'winner': 13,\n",
       "         'valu': 8,\n",
       "         'network': 26,\n",
       "         'custom': 42,\n",
       "         'select': 26,\n",
       "         'receivea': 1,\n",
       "         'prize': 82,\n",
       "         'reward': 9,\n",
       "         'claim': 98,\n",
       "         'call': 320,\n",
       "         'code': 27,\n",
       "         'kl341': 1,\n",
       "         'valid': 21,\n",
       "         '12': 3,\n",
       "         'hour': 4,\n",
       "         'mobil': 114,\n",
       "         '11': 3,\n",
       "         'month': 5,\n",
       "         'u': 119,\n",
       "         'r': 24,\n",
       "         'entitl': 6,\n",
       "         'updat': 14,\n",
       "         'latest': 30,\n",
       "         'colour': 14,\n",
       "         'camera': 23,\n",
       "         'co': 3,\n",
       "         '08002986030': 1,\n",
       "         'six': 2,\n",
       "         'chanc': 22,\n",
       "         'cash': 51,\n",
       "         '100': 14,\n",
       "         'pound': 19,\n",
       "         'csh11': 1,\n",
       "         'cost': 24,\n",
       "         '6day': 1,\n",
       "         'tsandc': 1,\n",
       "         'repli': 103,\n",
       "         'hl': 3,\n",
       "         '4': 97,\n",
       "         'info': 11,\n",
       "         'urgent': 57,\n",
       "         '1': 28,\n",
       "         'membership': 1,\n",
       "         'jackpot': 1,\n",
       "         '81010': 1,\n",
       "         'lccltd': 1,\n",
       "         'pobox': 11,\n",
       "         '4403ldnw1a7rw18': 1,\n",
       "         'xxxmobilemovieclub': 1,\n",
       "         'use': 12,\n",
       "         'credit': 18,\n",
       "         'click': 5,\n",
       "         'wap': 9,\n",
       "         'link': 6,\n",
       "         'next': 16,\n",
       "         'messag': 42,\n",
       "         'http': 18,\n",
       "         'england': 7,\n",
       "         'v': 4,\n",
       "         'macedonia': 1,\n",
       "         'dont': 9,\n",
       "         'miss': 8,\n",
       "         'news': 8,\n",
       "         'ur': 119,\n",
       "         'nation': 11,\n",
       "         'team': 2,\n",
       "         '87077': 8,\n",
       "         'eg': 11,\n",
       "         'tri': 37,\n",
       "         'wale': 1,\n",
       "         'scotland': 1,\n",
       "         'poboxox36504w45wq': 1,\n",
       "         'thank': 16,\n",
       "         'subscript': 5,\n",
       "         'rington': 30,\n",
       "         'uk': 20,\n",
       "         'charg': 22,\n",
       "         'pleas': 51,\n",
       "         'confirm': 1,\n",
       "         'ye': 12,\n",
       "         '07732584351': 1,\n",
       "         'rodger': 1,\n",
       "         'burn': 1,\n",
       "         'msg': 35,\n",
       "         'sm': 22,\n",
       "         'nokia': 57,\n",
       "         'camcord': 15,\n",
       "         '08000930705': 16,\n",
       "         'deliveri': 18,\n",
       "         'tomorrow': 10,\n",
       "         'ac': 4,\n",
       "         'sptv': 2,\n",
       "         'new': 64,\n",
       "         'jersey': 1,\n",
       "         'devil': 1,\n",
       "         'detroit': 1,\n",
       "         'red': 1,\n",
       "         'wing': 1,\n",
       "         'play': 14,\n",
       "         'ice': 1,\n",
       "         'hockey': 1,\n",
       "         'correct': 4,\n",
       "         'incorrect': 1,\n",
       "         'end': 24,\n",
       "         'congrat': 6,\n",
       "         'year': 9,\n",
       "         'special': 16,\n",
       "         'cinema': 2,\n",
       "         'pass': 5,\n",
       "         '09061209465': 2,\n",
       "         'suprman': 2,\n",
       "         'matrix3': 2,\n",
       "         'starwars3': 2,\n",
       "         'etc': 6,\n",
       "         '150pm': 4,\n",
       "         'advis': 2,\n",
       "         'follow': 10,\n",
       "         'recent': 4,\n",
       "         'review': 3,\n",
       "         'mob': 20,\n",
       "         'award': 55,\n",
       "         'bonu': 17,\n",
       "         '09066364589': 1,\n",
       "         'complimentari': 11,\n",
       "         'trip': 3,\n",
       "         'eurodisinc': 1,\n",
       "         'trav': 1,\n",
       "         'aco': 1,\n",
       "         'entry41': 1,\n",
       "         'di': 1,\n",
       "         'morefrmmob': 1,\n",
       "         'shracomorsglsuplt': 1,\n",
       "         '10': 7,\n",
       "         'ls1': 1,\n",
       "         '3aj': 1,\n",
       "         'hear': 3,\n",
       "         'come': 5,\n",
       "         'ken': 1,\n",
       "         'stuff': 1,\n",
       "         'servic': 64,\n",
       "         'repres': 6,\n",
       "         '0800': 9,\n",
       "         '169': 1,\n",
       "         '6031': 1,\n",
       "         'guarante': 42,\n",
       "         'wait': 17,\n",
       "         'collect': 45,\n",
       "         'simpli': 2,\n",
       "         'password': 2,\n",
       "         '85069': 1,\n",
       "         'verifi': 2,\n",
       "         'get': 74,\n",
       "         'usher': 1,\n",
       "         'britney': 1,\n",
       "         'fml': 1,\n",
       "         'gent': 2,\n",
       "         'contact': 56,\n",
       "         'last': 12,\n",
       "         'weekend': 13,\n",
       "         'draw': 35,\n",
       "         'show': 33,\n",
       "         'k52': 3,\n",
       "         '12hr': 16,\n",
       "         '150ppm': 29,\n",
       "         'holiday': 27,\n",
       "         'flight': 6,\n",
       "         'inc': 7,\n",
       "         'speak': 7,\n",
       "         'live': 22,\n",
       "         'oper': 11,\n",
       "         'privat': 16,\n",
       "         '2004': 7,\n",
       "         'account': 18,\n",
       "         'statement': 15,\n",
       "         '07742676969': 1,\n",
       "         '786': 3,\n",
       "         'unredeem': 3,\n",
       "         'point': 15,\n",
       "         '08719180248': 1,\n",
       "         'identifi': 15,\n",
       "         '45239': 2,\n",
       "         'expir': 17,\n",
       "         'caller': 12,\n",
       "         'landlin': 30,\n",
       "         '09064019788': 1,\n",
       "         'box42wr29c': 1,\n",
       "         'today': 33,\n",
       "         'voda': 5,\n",
       "         'number': 35,\n",
       "         '7548': 1,\n",
       "         '350': 2,\n",
       "         'match': 14,\n",
       "         '08712300220': 6,\n",
       "         'quot': 6,\n",
       "         '4041': 1,\n",
       "         'standard': 6,\n",
       "         'app': 4,\n",
       "         'sunshin': 5,\n",
       "         'quiz': 9,\n",
       "         'q': 2,\n",
       "         'top': 14,\n",
       "         'soni': 5,\n",
       "         'dvd': 5,\n",
       "         'player': 10,\n",
       "         'know': 19,\n",
       "         'countri': 2,\n",
       "         'algarv': 1,\n",
       "         'ansr': 2,\n",
       "         'sp': 6,\n",
       "         'tyron': 2,\n",
       "         'want': 31,\n",
       "         'laid': 4,\n",
       "         'tonight': 3,\n",
       "         'real': 12,\n",
       "         'dog': 12,\n",
       "         'locat': 5,\n",
       "         'sent': 10,\n",
       "         'direct': 10,\n",
       "         'join': 18,\n",
       "         'largest': 4,\n",
       "         'bt': 5,\n",
       "         'txting': 7,\n",
       "         'gravel': 1,\n",
       "         '69888': 2,\n",
       "         'nt': 1,\n",
       "         'ec2a': 4,\n",
       "         '150p': 26,\n",
       "         'chat': 38,\n",
       "         'svc': 1,\n",
       "         'hardcor': 2,\n",
       "         'go': 32,\n",
       "         '69988': 1,\n",
       "         'noth': 4,\n",
       "         'must': 5,\n",
       "         'age': 9,\n",
       "         'yr': 14,\n",
       "         'randi': 2,\n",
       "         'sexi': 14,\n",
       "         'femal': 1,\n",
       "         'local': 5,\n",
       "         'luv': 5,\n",
       "         'netcollex': 4,\n",
       "         'ltd': 11,\n",
       "         '08700621170150p': 2,\n",
       "         'per': 41,\n",
       "         'stop': 104,\n",
       "         'annonc': 1,\n",
       "         '07046744435': 1,\n",
       "         'arrang': 2,\n",
       "         '0871277810810': 1,\n",
       "         'bootydeli': 1,\n",
       "         'invit': 10,\n",
       "         'friend': 13,\n",
       "         'see': 16,\n",
       "         'frnd': 5,\n",
       "         '62468': 5,\n",
       "         'bangbab': 1,\n",
       "         'order': 17,\n",
       "         'way': 1,\n",
       "         'download': 7,\n",
       "         'content': 14,\n",
       "         'goto': 5,\n",
       "         'bangb': 1,\n",
       "         'tv': 3,\n",
       "         'menu': 1,\n",
       "         's89': 1,\n",
       "         'freephon': 4,\n",
       "         '0808': 1,\n",
       "         '145': 1,\n",
       "         '4742': 1,\n",
       "         'uniqu': 2,\n",
       "         'enough': 2,\n",
       "         'find': 21,\n",
       "         '30th': 1,\n",
       "         'august': 2,\n",
       "         '500': 19,\n",
       "         '89545': 3,\n",
       "         '2optout': 4,\n",
       "         'meet': 5,\n",
       "         'dream': 2,\n",
       "         'partner': 5,\n",
       "         'soon': 2,\n",
       "         'career': 1,\n",
       "         'flyng': 1,\n",
       "         'start': 12,\n",
       "         'horo': 2,\n",
       "         'star': 3,\n",
       "         'sign': 2,\n",
       "         'ari': 1,\n",
       "         'someon': 13,\n",
       "         'date': 26,\n",
       "         'even': 7,\n",
       "         'flirt': 5,\n",
       "         '10p': 13,\n",
       "         'name': 15,\n",
       "         'sam': 2,\n",
       "         '25': 3,\n",
       "         '18': 20,\n",
       "         'recd': 3,\n",
       "         'thirtyeight': 1,\n",
       "         'penc': 3,\n",
       "         '447801259231': 1,\n",
       "         'secret': 7,\n",
       "         'admir': 7,\n",
       "         'look': 9,\n",
       "         'make': 11,\n",
       "         'reveal': 12,\n",
       "         'think': 11,\n",
       "         '09058094597': 1,\n",
       "         'congratul': 12,\n",
       "         'cd': 11,\n",
       "         'voucher': 28,\n",
       "         '125gift': 2,\n",
       "         'music': 15,\n",
       "         '87066': 10,\n",
       "         'tnc': 7,\n",
       "         'offer': 33,\n",
       "         'video': 29,\n",
       "         'handset': 6,\n",
       "         '750': 17,\n",
       "         'anytim': 12,\n",
       "         'min': 45,\n",
       "         'unlimit': 10,\n",
       "         'realli': 1,\n",
       "         'horni': 7,\n",
       "         'nake': 1,\n",
       "         'hot': 13,\n",
       "         '69698': 2,\n",
       "         'unsubscrib': 17,\n",
       "         'chang': 1,\n",
       "         'choos': 9,\n",
       "         'club': 19,\n",
       "         '87070': 1,\n",
       "         'club4': 1,\n",
       "         'po': 24,\n",
       "         'box1146': 1,\n",
       "         'mk45': 1,\n",
       "         '2wt': 1,\n",
       "         'singl': 5,\n",
       "         'chart': 3,\n",
       "         'qualiti': 2,\n",
       "         'hmv': 4,\n",
       "         'genuin': 1,\n",
       "         'answer': 6,\n",
       "         'easi': 10,\n",
       "         '86688': 14,\n",
       "         'phone': 52,\n",
       "         'upgrad': 4,\n",
       "         'pay': 2,\n",
       "         'sim': 2,\n",
       "         'card': 4,\n",
       "         'loyalti': 6,\n",
       "         '0845': 3,\n",
       "         '021': 2,\n",
       "         'blind': 2,\n",
       "         '4u': 4,\n",
       "         'rodds1': 1,\n",
       "         'aberdeen': 1,\n",
       "         'unit': 1,\n",
       "         'kingdom': 1,\n",
       "         'check': 4,\n",
       "         'hide': 1,\n",
       "         'themob': 5,\n",
       "         'newest': 3,\n",
       "         'game': 20,\n",
       "         'tone': 63,\n",
       "         'gossip': 1,\n",
       "         'babe': 10,\n",
       "         'sport': 7,\n",
       "         'keep': 5,\n",
       "         'fit': 1,\n",
       "         'funki': 1,\n",
       "         '82468': 2,\n",
       "         'smart': 2,\n",
       "         'weekli': 20,\n",
       "         '85222': 1,\n",
       "         'cs': 35,\n",
       "         'winnersclub': 1,\n",
       "         'box': 26,\n",
       "         '84': 1,\n",
       "         'm26': 3,\n",
       "         '3uz': 3,\n",
       "         'decemb': 4,\n",
       "         '08002986906': 3,\n",
       "         'germani': 3,\n",
       "         'minut': 12,\n",
       "         'fix': 2,\n",
       "         'line': 33,\n",
       "         'via': 3,\n",
       "         'access': 8,\n",
       "         '0844': 2,\n",
       "         '861': 2,\n",
       "         '85': 2,\n",
       "         'prepay': 2,\n",
       "         'valentin': 3,\n",
       "         'day': 26,\n",
       "         'take': 16,\n",
       "         'lifetim': 2,\n",
       "         '83600': 3,\n",
       "         'rcvd': 5,\n",
       "         'fanci': 12,\n",
       "         'shag': 2,\n",
       "         'xxuk': 1,\n",
       "         'suzi': 1,\n",
       "         'websit': 1,\n",
       "         'x': 8,\n",
       "         'current': 11,\n",
       "         'maxim': 7,\n",
       "         'cc': 6,\n",
       "         '08708800282': 1,\n",
       "         'xma': 15,\n",
       "         'motorola': 10,\n",
       "         'sonyericsson': 5,\n",
       "         'bluetooth': 7,\n",
       "         'doubl': 13,\n",
       "         '1000': 9,\n",
       "         'orang': 24,\n",
       "         'mobileupd8': 13,\n",
       "         '08000839402': 12,\n",
       "         'discount': 6,\n",
       "         'rp176781': 1,\n",
       "         '08717205546': 1,\n",
       "         'refer': 4,\n",
       "         't91': 1,\n",
       "         'gbp': 1,\n",
       "         '09057039994': 1,\n",
       "         '6month': 1,\n",
       "         'avail': 2,\n",
       "         '4mth': 2,\n",
       "         'half': 12,\n",
       "         'price': 15,\n",
       "         'rental': 11,\n",
       "         '11mth': 3,\n",
       "         'mobilesdirect': 2,\n",
       "         '08000938767': 2,\n",
       "         'or2stoptxt': 2,\n",
       "         'first': 6,\n",
       "         '87131': 3,\n",
       "         'poli': 22,\n",
       "         'true': 2,\n",
       "         'help': 12,\n",
       "         '2814032': 1,\n",
       "         '16': 18,\n",
       "         '1st': 19,\n",
       "         'cal': 1,\n",
       "         'l': 1,\n",
       "         '09064012103': 1,\n",
       "         'box334sk38ch': 2,\n",
       "         'competit': 2,\n",
       "         '80086': 2,\n",
       "         'logo': 6,\n",
       "         'lover': 2,\n",
       "         'heart': 3,\n",
       "         'love': 10,\n",
       "         'name1': 2,\n",
       "         'name2': 2,\n",
       "         'mobno': 2,\n",
       "         'adam': 2,\n",
       "         'eve': 5,\n",
       "         '07123456789': 2,\n",
       "         'yahoo': 2,\n",
       "         'pobox36504w45wq': 5,\n",
       "         'txtno': 2,\n",
       "         'ad': 4,\n",
       "         'enter': 15,\n",
       "         '09111032124': 1,\n",
       "         'pobox12n146tf150p': 1,\n",
       "         '09058094455': 1,\n",
       "         'land': 16,\n",
       "         '3650': 1,\n",
       "         '09066382422': 1,\n",
       "         'ave': 2,\n",
       "         '3min': 3,\n",
       "         'vari': 4,\n",
       "         'close': 3,\n",
       "         '300603': 1,\n",
       "         'post': 4,\n",
       "         'bcm4284': 1,\n",
       "         'ldn': 7,\n",
       "         'wc1n3xx': 3,\n",
       "         'loan': 3,\n",
       "         'purpos': 2,\n",
       "         'homeown': 1,\n",
       "         'tenant': 2,\n",
       "         'welcom': 8,\n",
       "         'previous': 1,\n",
       "         'refus': 2,\n",
       "         '1956669': 1,\n",
       "         'upgrdcentr': 1,\n",
       "         '0207': 2,\n",
       "         '153': 2,\n",
       "         '26th': 1,\n",
       "         'juli': 2,\n",
       "         'okmail': 1,\n",
       "         'dear': 16,\n",
       "         'dave': 2,\n",
       "         'notic': 3,\n",
       "         'tenerif': 7,\n",
       "         '5000': 4,\n",
       "         '09061743806': 2,\n",
       "         'tc': 7,\n",
       "         'sae': 18,\n",
       "         'box326': 2,\n",
       "         'cw25wx': 4,\n",
       "         'moan': 1,\n",
       "         '69888nyt': 1,\n",
       "         'activ': 5,\n",
       "         'term': 8,\n",
       "         'condit': 4,\n",
       "         'visit': 5,\n",
       "         '09050002311': 1,\n",
       "         'b4280703': 1,\n",
       "         '40gb': 6,\n",
       "         'ipod': 10,\n",
       "         'mp3': 5,\n",
       "         '83355': 4,\n",
       "         'ibhltd': 3,\n",
       "         'ldnw15h': 3,\n",
       "         'boltblu': 1,\n",
       "         'mono': 4,\n",
       "         'poly3': 1,\n",
       "         'cha': 2,\n",
       "         'slide': 1,\n",
       "         'yeah': 1,\n",
       "         'slow': 1,\n",
       "         'jamz': 1,\n",
       "         'toxic': 1,\n",
       "         'renew': 3,\n",
       "         'pin': 1,\n",
       "         'tgxxrz': 1,\n",
       "         '2nd': 19,\n",
       "         'attempt': 22,\n",
       "         'box95qu': 3,\n",
       "         'worth': 8,\n",
       "         '85023': 4,\n",
       "         'savamob': 8,\n",
       "         'member': 6,\n",
       "         'sub': 5,\n",
       "         'unsub': 3,\n",
       "         'reciev': 1,\n",
       "         'within': 4,\n",
       "         '24hr': 2,\n",
       "         'channel': 1,\n",
       "         'teletext': 1,\n",
       "         'pg': 1,\n",
       "         '2003': 13,\n",
       "         '07815296484': 1,\n",
       "         '800': 13,\n",
       "         '08718738001': 2,\n",
       "         '41782': 1,\n",
       "         'monthlysubscript': 1,\n",
       "         'csc': 1,\n",
       "         'web': 2,\n",
       "         'age16': 6,\n",
       "         '2stop': 1,\n",
       "         'call09050000327': 2,\n",
       "         'us': 7,\n",
       "         'ring': 5,\n",
       "         '09050005321': 1,\n",
       "         '150': 9,\n",
       "         'textand': 1,\n",
       "         '08002988890': 1,\n",
       "         'shop': 10,\n",
       "         'spree': 6,\n",
       "         'custcar': 7,\n",
       "         '08715705022': 3,\n",
       "         '2000': 2,\n",
       "         '08712402050': 2,\n",
       "         '10ppm': 2,\n",
       "         'ag': 2,\n",
       "         'promo': 2,\n",
       "         '07753741225': 1,\n",
       "         '08715203677': 1,\n",
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       "         'rose': 1,\n",
       "         'ladi': 3,\n",
       "         '22': 1,\n",
       "         'kick': 3,\n",
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       "         'result': 2,\n",
       "         'daili': 3,\n",
       "         'remov': 7,\n",
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       "         'textbuddi': 2,\n",
       "         'guy': 5,\n",
       "         'area': 8,\n",
       "         '25p': 7,\n",
       "         'search': 2,\n",
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       "         'one': 10,\n",
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       "         'vodafon': 5,\n",
       "         '4882': 1,\n",
       "         '09064019014': 1,\n",
       "         'holder': 6,\n",
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       "         'ts': 9,\n",
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       "         '08715203694': 1,\n",
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       "         'sw7': 2,\n",
       "         '3ss': 2,\n",
       "         '88800': 1,\n",
       "         '89034': 1,\n",
       "         'premium': 2,\n",
       "         '08718711108': 1,\n",
       "         'sun0819': 1,\n",
       "         'hello': 4,\n",
       "         'seem': 1,\n",
       "         'cool': 1,\n",
       "         'gr8': 7,\n",
       "         '20': 3,\n",
       "         'everi': 26,\n",
       "         'wk': 17,\n",
       "         'opt': 10,\n",
       "         '08452810071': 1,\n",
       "         'hi': 15,\n",
       "         'sue': 2,\n",
       "         'old': 2,\n",
       "         'work': 3,\n",
       "         'lapdanc': 1,\n",
       "         'sex': 10,\n",
       "         'bedroom': 1,\n",
       "         'textoper': 3,\n",
       "         'g2': 1,\n",
       "         '1da': 1,\n",
       "         '150ppmsg': 1,\n",
       "         'forward': 4,\n",
       "         '448712404000': 1,\n",
       "         '08712404000': 1,\n",
       "         'immedi': 5,\n",
       "         'fantast': 7,\n",
       "         'deck': 1,\n",
       "         'alert': 5,\n",
       "         '08714712388': 1,\n",
       "         '09071512433': 2,\n",
       "         'b4': 6,\n",
       "         '050703': 2,\n",
       "         'csbcm4235wc1n3xx': 2,\n",
       "         'callcost': 2,\n",
       "         'mobilesvari': 2,\n",
       "         '50': 3,\n",
       "         '08714712394': 1,\n",
       "         'email': 2,\n",
       "         'alertfrom': 1,\n",
       "         'jeri': 1,\n",
       "         'stewarts': 1,\n",
       "         '2kbsubject': 1,\n",
       "         'prescripiton': 1,\n",
       "         'drvgsto': 1,\n",
       "         'listen': 3,\n",
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       "         'txtauction': 5,\n",
       "         '81151': 2,\n",
       "         '4t': 3,\n",
       "         'ctxt': 2,\n",
       "         'subscrib': 9,\n",
       "         'best': 7,\n",
       "         'helplin': 2,\n",
       "         '08706091795': 2,\n",
       "         'realiz': 1,\n",
       "         '40': 2,\n",
       "         'thousand': 1,\n",
       "         'run': 1,\n",
       "         'around': 2,\n",
       "         'tattoo': 1,\n",
       "         'premier': 2,\n",
       "         'romant': 1,\n",
       "         'pari': 3,\n",
       "         'night': 7,\n",
       "         'book': 6,\n",
       "         '08704439680t': 1,\n",
       "         'unclaim': 1,\n",
       "         '09066368327': 1,\n",
       "         'claimcod': 1,\n",
       "         'm39m51': 1,\n",
       "         'citi': 3,\n",
       "         'break': 5,\n",
       "         'could': 7,\n",
       "         'summer': 7,\n",
       "         'store': 7,\n",
       "         '88039': 2,\n",
       "         'skilgm': 3,\n",
       "         'tscs087147403231winawk': 2,\n",
       "         '0578': 2,\n",
       "         'ever': 3,\n",
       "         'thought': 1,\n",
       "         'good': 12,\n",
       "         'life': 3,\n",
       "         'perfect': 1,\n",
       "         'commun': 2,\n",
       "         '5': 8,\n",
       "         'polyphon': 4,\n",
       "         '087018728737': 1,\n",
       "         'toppoli': 1,\n",
       "         'tune': 1,\n",
       "         'subpoli': 2,\n",
       "         '81618': 1,\n",
       "         'pole': 1,\n",
       "         '08718727870': 2,\n",
       "         '14thmarch': 1,\n",
       "         'availa': 1,\n",
       "         'pobox84': 4,\n",
       "         'm263uz': 2,\n",
       "         'no1': 5,\n",
       "         '8077': 1,\n",
       "         'tell': 13,\n",
       "         'mate': 8,\n",
       "         '36504': 4,\n",
       "         'w45wq': 3,\n",
       "         'cashto': 2,\n",
       "         '08000407165': 2,\n",
       "         'getstop': 2,\n",
       "         '88222': 2,\n",
       "         'php': 2,\n",
       "         'rg21': 1,\n",
       "         '4jx': 1,\n",
       "         'either': 8,\n",
       "         'gift': 16,\n",
       "         'outbid': 1,\n",
       "         'simonwatson5120': 1,\n",
       "         'shinco': 1,\n",
       "         'plyr': 1,\n",
       "         'bid': 8,\n",
       "         'notif': 1,\n",
       "         'smsservic': 1,\n",
       "         'yourinclus': 1,\n",
       "         'pl': 8,\n",
       "         '3qxj9': 3,\n",
       "         'extra': 6,\n",
       "         '9ae': 3,\n",
       "         'alfi': 3,\n",
       "         'moon': 3,\n",
       "         'children': 3,\n",
       "         'need': 11,\n",
       "         'song': 3,\n",
       "         'm8': 4,\n",
       "         'chariti': 9,\n",
       "         '8007': 19,\n",
       "         'zed': 6,\n",
       "         '08701417012': 3,\n",
       "         'profit': 3,\n",
       "         'cust': 3,\n",
       "         'care': 6,\n",
       "         '07821230901': 1,\n",
       "         'five': 3,\n",
       "         '08002888812': 2,\n",
       "         'wed': 2,\n",
       "         '09066350750': 2,\n",
       "         'ibiza': 4,\n",
       "         'await': 24,\n",
       "         '434': 3,\n",
       "         'sk3': 3,\n",
       "         '8wp': 3,\n",
       "         'ppm': 4,\n",
       "         'talk': 5,\n",
       "         'fall': 1,\n",
       "         'world': 2,\n",
       "         'discreet': 2,\n",
       "         'vip': 4,\n",
       "         '83110': 1,\n",
       "         'suppli': 2,\n",
       "         'virgin': 2,\n",
       "         'record': 6,\n",
       "         'mysteri': 1,\n",
       "         '09061104283': 1,\n",
       "         'approx': 1,\n",
       "         '07808': 1,\n",
       "         'xxxxxx': 1,\n",
       "         '08719899217': 1,\n",
       "         '41685': 2,\n",
       "         'posh': 1,\n",
       "         'bird': 1,\n",
       "         'chap': 1,\n",
       "         'user': 8,\n",
       "         'trial': 1,\n",
       "         'prod': 1,\n",
       "         'champney': 1,\n",
       "         'put': 1,\n",
       "         'address': 3,\n",
       "         'dob': 1,\n",
       "         'asap': 6,\n",
       "         'ta': 1,\n",
       "         '0721072': 1,\n",
       "         'till': 2,\n",
       "         'drop': 1,\n",
       "         '10k': 2,\n",
       "         '5k': 1,\n",
       "         'travel': 1,\n",
       "         'ntt': 7,\n",
       "         'cr01327bt': 1,\n",
       "         'fixedlin': 1,\n",
       "         'liverpool': 2,\n",
       "         'mid': 1,\n",
       "         '09058094565': 2,\n",
       "         'remind': 2,\n",
       "         'alreadi': 1,\n",
       "         'paid': 1,\n",
       "         'mymobi': 1,\n",
       "         'lastest': 1,\n",
       "         'stereophon': 1,\n",
       "         'marley': 1,\n",
       "         'dizze': 1,\n",
       "         'racal': 1,\n",
       "         'libertin': 1,\n",
       "         'stroke': 1,\n",
       "         'nookii': 1,\n",
       "         'bookmark': 1,\n",
       "         'januari': 1,\n",
       "         'male': 3,\n",
       "         'sale': 4,\n",
       "         'gay': 6,\n",
       "         'cheaper': 2,\n",
       "         'cheap': 3,\n",
       "         'peak': 2,\n",
       "         '08712460324': 8,\n",
       "         'money': 4,\n",
       "         'lucki': 8,\n",
       "         '88600': 2,\n",
       "         'give': 8,\n",
       "         'away': 4,\n",
       "         'box403': 1,\n",
       "         'w1t1ji': 1,\n",
       "         'matthew': 1,\n",
       "         '09063440451': 1,\n",
       "         'lux': 2,\n",
       "         'ppm150': 1,\n",
       "         'box334': 1,\n",
       "         'sk38xh': 4,\n",
       "         '09061749602': 1,\n",
       "         '528': 1,\n",
       "         'hp20': 1,\n",
       "         '1yf': 1,\n",
       "         'touch': 1,\n",
       "         'folk': 1,\n",
       "         'compani': 3,\n",
       "         'enjoy': 12,\n",
       "         '08718720201': 5,\n",
       "         'filthi': 2,\n",
       "         'stori': 1,\n",
       "         'girl': 9,\n",
       "         '09050001808': 1,\n",
       "         'm95': 1,\n",
       "         'valid12hr': 2,\n",
       "         '3g': 3,\n",
       "         'videophon': 3,\n",
       "         '09063458130': 2,\n",
       "         'videochat': 3,\n",
       "         'wid': 3,\n",
       "         'java': 3,\n",
       "         'dload': 3,\n",
       "         'polyph': 2,\n",
       "         'nolin': 3,\n",
       "         'rentl': 3,\n",
       "         'panason': 1,\n",
       "         'bluetoothhdset': 1,\n",
       "         'doublemin': 1,\n",
       "         'doubletxt': 1,\n",
       "         'contract': 4,\n",
       "         'guess': 6,\n",
       "         'somebodi': 2,\n",
       "         'secretli': 2,\n",
       "         'wan': 11,\n",
       "         'na': 10,\n",
       "         '09065394514': 1,\n",
       "         'datebox1282essexcm61xn': 2,\n",
       "         '09058097218': 1,\n",
       "         '6': 5,\n",
       "         'ls15hb': 2,\n",
       "         'bloke': 1,\n",
       "         'zoe': 1,\n",
       "         'kickoff': 1,\n",
       "         'inform': 9,\n",
       "         'euro': 2,\n",
       "         'eastend': 1,\n",
       "         'flower': 2,\n",
       "         'dot': 1,\n",
       "         'compar': 1,\n",
       "         'violet': 1,\n",
       "         'tulip': 1,\n",
       "         'lili': 1,\n",
       "         'e': 1,\n",
       "         'f': 3,\n",
       "         '84025': 2,\n",
       "         'lot': 2,\n",
       "         'peopl': 3,\n",
       "         'regist': 5,\n",
       "         'replys150': 1,\n",
       "         'ask': 5,\n",
       "         'cant': 3,\n",
       "         '09058091854': 1,\n",
       "         'box385': 1,\n",
       "         'm6': 1,\n",
       "         '6wu': 1,\n",
       "         '09050003091': 2,\n",
       "         'c52': 2,\n",
       "         'xchat': 5,\n",
       "         'sipix': 3,\n",
       "         'digit': 6,\n",
       "         '09061221061': 1,\n",
       "         '28day': 1,\n",
       "         'box177': 1,\n",
       "         'm221bp': 1,\n",
       "         '2yr': 1,\n",
       "         'warranti': 1,\n",
       "         'p': 2,\n",
       "         '09061790121': 2,\n",
       "         '3030': 1,\n",
       "         'b': 10,\n",
       "         'receipt': 3,\n",
       "         'an': 6,\n",
       "         'elvi': 1,\n",
       "         'presley': 1,\n",
       "         'birthday': 1,\n",
       "         'o2': 5,\n",
       "         'log': 7,\n",
       "         'onto': 7,\n",
       "         'surpris': 5,\n",
       "         '449050000301': 1,\n",
       "         '09050000301': 1,\n",
       "         'bore': 3,\n",
       "         'speed': 1,\n",
       "         'speedchat': 2,\n",
       "         '80155': 1,\n",
       "         'em': 1,\n",
       "         'swap': 1,\n",
       "         'chatter': 1,\n",
       "         'chat80155': 1,\n",
       "         'rcd': 1,\n",
       "         '08000776320': 2,\n",
       "         'part': 8,\n",
       "         'survey': 1,\n",
       "         'yesterday': 3,\n",
       "         'howev': 1,\n",
       "         'wish': 2,\n",
       "         '80160': 1,\n",
       "         'hmv1': 1,\n",
       "         'forget': 2,\n",
       "         'place': 4,\n",
       "         'mani': 3,\n",
       "         'request': 2,\n",
       "         '08707808226': 1,\n",
       "         'let': 3,\n",
       "         'notifi': 1,\n",
       "         'luck': 6,\n",
       "         'futur': 3,\n",
       "         'market': 1,\n",
       "         '84122': 1,\n",
       "         '08450542832': 1,\n",
       "         ...})"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(spam_corpus)      #going to create a dictionary includes each word with their frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c0ee0187",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Model builing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3561ecc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>free</th>\n",
       "      <td>call</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
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       "</div>"
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      "text/plain": [
       "         0\n",
       "free  call\n",
       "191    320"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#NAIVE BAYES AND ANOTHER ALGORITHMS (NAIVE BAYES IS PERFORM VERY EFFICIENT IN TEXT DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "98a7e4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since algo need numerical dataabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "4d1d0764",
   "metadata": {},
   "outputs": [],
   "source": [
    "#so text to vector(or number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "61446e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d9f44857",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cv.fit_transform(df['transform_text']).toarray()   #toarray = to convert the sparse array to dense one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5c4d2582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 6708)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "75686960",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c2ac22f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c961f272",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "6611a8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7c9981a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "8eaa62c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#object creation\n",
    "gaussNB = GaussianNB()\n",
    "multiNB = MultinomialNB()\n",
    "bernNB = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f763b06a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8800773694390716\n",
      "[[792 104]\n",
      " [ 20 118]]\n",
      "0.5315315315315315\n"
     ]
    }
   ],
   "source": [
    "gaussNB.fit(X_train,y_train)\n",
    "y_pred1 = gaussNB.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred1))\n",
    "print(confusion_matrix(y_test,y_pred1))\n",
    "print(precision_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "62d21362",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9642166344294004\n",
      "[[871  25]\n",
      " [ 12 126]]\n",
      "0.8344370860927153\n"
     ]
    }
   ],
   "source": [
    "multiNB.fit(X_train,y_train)\n",
    "y_pred2 = multiNB.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred2))\n",
    "print(confusion_matrix(y_test,y_pred2))\n",
    "print(precision_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a71b51e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9700193423597679\n",
      "[[893   3]\n",
      " [ 28 110]]\n",
      "0.9734513274336283\n"
     ]
    }
   ],
   "source": [
    "bernNB.fit(X_train,y_train)\n",
    "y_pred3 = bernNB.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred3))\n",
    "print(confusion_matrix(y_test,y_pred3))\n",
    "print(precision_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "889641c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using another vectrorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c993ab67",
   "metadata": {},
   "outputs": [],
   "source": [
    "#----#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "8688f017",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(max_features=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "31d57f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transform_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "abe0bd5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['target'].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "abd5ea9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "8541a34c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "c770fa0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussNB = GaussianNB()\n",
    "multiNB = MultinomialNB()\n",
    "bernNB = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "24a71aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8694390715667312\n",
      "[[788 108]\n",
      " [ 27 111]]\n",
      "0.5068493150684932\n"
     ]
    }
   ],
   "source": [
    "gaussNB.fit(X_train,y_train)\n",
    "y_pred1 = gaussNB.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred1))\n",
    "print(confusion_matrix(y_test,y_pred1))\n",
    "print(precision_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "5371cad6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9709864603481625\n",
      "[[896   0]\n",
      " [ 30 108]]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "multiNB.fit(X_train,y_train)\n",
    "y_pred2 = multiNB.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred2))\n",
    "print(confusion_matrix(y_test,y_pred2))\n",
    "print(precision_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "878b3488",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9835589941972921\n",
      "[[895   1]\n",
      " [ 16 122]]\n",
      "0.991869918699187\n"
     ]
    }
   ],
   "source": [
    "bernNB.fit(X_train, y_train)\n",
    "y_pred3 = bernNB.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred3))\n",
    "print(confusion_matrix(y_test,y_pred3))\n",
    "print(precision_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "2ad58ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multinomial is resulting in an effective precision score while tokenizer \n",
    "# taken as tfid choosing multiNB becz in the text filtering we more focused on\n",
    "# precision rather accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35bde44a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# summary till now\n",
    "# when a email recieve do 3 steps\n",
    "# preprocessing(text_tranform) --> vecterize --> apply model\n",
    "\n",
    "#pickle 2 files i.e vectorizer, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "0f76512c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
    "pickle.dump(multiNB,open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d516976",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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