Spam Detection Practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db978c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "01ad4f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\Arindham Krishna\\OneDrive\\Desktop\\Projects for Portfolio\\Spam Detection\\spam.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1835f36b",
   "metadata": {},
   "outputs": [
    {
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       "  <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>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>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",
       "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3915067d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbfd8463",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "816a2103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   v1          5572 non-null   object\n",
      " 1   v2          5572 non-null   object\n",
      " 2   Unnamed: 2  50 non-null     object\n",
      " 3   Unnamed: 3  12 non-null     object\n",
      " 4   Unnamed: 4  6 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 217.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80446098",
   "metadata": {},
   "source": [
    "We can see that there are null values in col, unnamed:2 3 and 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ed0964",
   "metadata": {},
   "source": [
    "We shall drop these columns as they bring no help in our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f8e0a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping columns. \n",
    "df.drop(columns=['Unnamed: 2','Unnamed: 3','Unnamed: 4'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aeb29eb1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ham</td>\n",
       "      <td>I'm back &amp;amp; we're packing the car now, I'll...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ugh I don't wanna get out of bed. It's so warm.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ard 530 lor. I ok then message �_ lor.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4603</th>\n",
       "      <td>ham</td>\n",
       "      <td>THANX 4 PUTTIN DA FONE DOWN ON ME!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>ham</td>\n",
       "      <td>Good night. Am going to sleep.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       v1                                                 v2\n",
       "28    ham  I'm back &amp; we're packing the car now, I'll...\n",
       "809   ham    Ugh I don't wanna get out of bed. It's so warm.\n",
       "3914  ham             Ard 530 lor. I ok then message �_ lor.\n",
       "4603  ham                THANX 4 PUTTIN DA FONE DOWN ON ME!!\n",
       "1954  ham                     Good night. Am going to sleep."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9f27318",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3035</th>\n",
       "      <td>ham</td>\n",
       "      <td>;-) ok. I feel like john lennon.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>ham</td>\n",
       "      <td>Hey so this sat are we going for the intro pil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sir send to group mail check it.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>ham</td>\n",
       "      <td>Dear,Me at cherthala.in case u r coming cochin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4127</th>\n",
       "      <td>ham</td>\n",
       "      <td>I dont thnk its a wrong calling between us</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     target                                               text\n",
       "3035    ham                   ;-) ok. I feel like john lennon.\n",
       "391     ham  Hey so this sat are we going for the intro pil...\n",
       "2959    ham                   Sir send to group mail check it.\n",
       "687     ham  Dear,Me at cherthala.in case u r coming cochin...\n",
       "4127    ham         I dont thnk its a wrong calling between us"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we will rename the columns. \n",
    "df.rename(columns={'v1':'target','v2':'text'},inplace=True)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8474bab",
   "metadata": {},
   "source": [
    "Now, as we can see that we have our target and text column. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6415c8",
   "metadata": {},
   "source": [
    "We will do the label encoding for our target column, that means the spam and ham values will be converted to (0,1 or 1,0) format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90f40399",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fe85750",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = encoder.fit_transform(df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b8102f0",
   "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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking 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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   target                                               text\n",
       "0       0  Go until jurong point, crazy.. Available only ...\n",
       "1       0                      Ok lar... Joking wif u oni...\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       0  U dun say so early hor... U c already then say...\n",
       "4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50eb9274",
   "metadata": {},
   "source": [
    "You can see here that, ham values are converted to 0 and spam values are converted to 1. \n",
    "\n",
    "ham: 0  and spam: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b84f409f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check for missing values. \n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc065e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicate values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f3870fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove duplicates\n",
    "df = df.drop_duplicates(keep='first')\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5411b6f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "178e516b",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0aa0658",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking 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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   target                                               text\n",
       "0       0  Go until jurong point, crazy.. Available only ...\n",
       "1       0                      Ok lar... Joking wif u oni...\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       0  U dun say so early hor... U c already then say...\n",
       "4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b85b3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ce1e58",
   "metadata": {},
   "source": [
    "We can see that, there are 4516 hams and only 653 spams. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24db91b",
   "metadata": {},
   "source": [
    "The data is imbalanced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09696a4b",
   "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": 19,
   "id": "d94a54e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\arindham krishna\\anaconda3\\lib\\site-packages (3.6.2)\n",
      "Requirement already satisfied: joblib in c:\\users\\arindham krishna\\anaconda3\\lib\\site-packages (from nltk) (1.0.1)\n",
      "Requirement already satisfied: regex in c:\\users\\arindham krishna\\anaconda3\\lib\\site-packages (from nltk) (2021.7.6)\n",
      "Requirement already satisfied: tqdm in c:\\users\\arindham krishna\\anaconda3\\lib\\site-packages (from nltk) (4.61.2)\n",
      "Requirement already satisfied: click in c:\\users\\arindham krishna\\anaconda3\\lib\\site-packages (from nltk) (8.0.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\arindham krishna\\anaconda3\\lib\\site-packages (from click->nltk) (0.4.4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to C:\\Users\\Arindham\n",
      "[nltk_data]     Krishna\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "!pip install nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28aa5af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_characters'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1339eb03",
   "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_characters</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters\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"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c32a85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# num of words\n",
    "df['num_words'] = df['text'].apply(lambda x:len(nltk.word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b1e005f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</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",
       "    </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",
       "    </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",
       "    </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",
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       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters  \\\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  \n",
       "0         24  \n",
       "1          8  \n",
       "2         37  \n",
       "3         13  \n",
       "4         15  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5781a580",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</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>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</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>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</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",
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       "    <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",
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       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters  \\\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  \n",
       "0         24              2  \n",
       "1          8              2  \n",
       "2         37              2  \n",
       "3         13              1  \n",
       "4         15              1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num_sentences'] = df['text'].apply(lambda x:len(nltk.sent_tokenize(x)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d18e2cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_characters</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>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.923776</td>\n",
       "      <td>18.456375</td>\n",
       "      <td>1.962275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.174846</td>\n",
       "      <td>13.323322</td>\n",
       "      <td>1.433892</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>36.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>26.000000</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_characters    num_words  num_sentences\n",
       "count     5169.000000  5169.000000    5169.000000\n",
       "mean        78.923776    18.456375       1.962275\n",
       "std         58.174846    13.323322       1.433892\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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['num_characters','num_words','num_sentences']].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a7135fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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_characters</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>70.456820</td>\n",
       "      <td>17.123339</td>\n",
       "      <td>1.815545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.356802</td>\n",
       "      <td>13.491315</td>\n",
       "      <td>1.364098</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_characters    num_words  num_sentences\n",
       "count     4516.000000  4516.000000    4516.000000\n",
       "mean        70.456820    17.123339       1.815545\n",
       "std         56.356802    13.491315       1.364098\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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ham\n",
    "df[df['target'] == 0][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b287b151",
   "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_characters</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>137.479326</td>\n",
       "      <td>27.675345</td>\n",
       "      <td>2.977029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.014336</td>\n",
       "      <td>7.011513</td>\n",
       "      <td>1.493676</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>131.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>148.000000</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>223.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters   num_words  num_sentences\n",
       "count      653.000000  653.000000     653.000000\n",
       "mean       137.479326   27.675345       2.977029\n",
       "std         30.014336    7.011513       1.493676\n",
       "min         13.000000    2.000000       1.000000\n",
       "25%        131.000000   25.000000       2.000000\n",
       "50%        148.000000   29.000000       3.000000\n",
       "75%        157.000000   32.000000       4.000000\n",
       "max        223.000000   46.000000       9.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#spam\n",
    "df[df['target'] == 1][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3719c256",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b3824a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_characters', ylabel='Count'>"
      ]
     },
     "execution_count": 29,
     "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_characters'])\n",
    "sns.histplot(df[df['target'] == 1]['num_characters'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "97d90747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 30,
     "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_words'])\n",
    "sns.histplot(df[df['target'] == 1]['num_words'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3a5b4026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/gl3c2MnTuX/ISxQWFvKrnsdx6TmnFju+Zt16/vSPp/lxcS41MzK467pL2L9NS+Zl5/D7+58oKpe9eClXXnQW/fvs0ITOKaNB9060vvsSlEiQ+/KH5PzrrW3K1Dv6EFoPugSlp5G/Yi2z+t6BamZw8Jt/QTUyUHqCFe99zsIHX43gCnaQN9m5qCQSCW6451puPP/3LM1ZypARjzN21Of88N0PRWUmj/2SsaPGAbBPu324a/Ad9D9+AI2bNebsS86i/wmXsHnTZu4cfAcn9jmRD14bGdXlVKkze5/EBX3P4Na7H4w6lCpXUFDIvU+8wJC/3MRejRpy/g2D6H5UR/bdO6uozNDXhnPgPq34x+3XMO/HHO554gWeuvf3tG3ZnNcfHVR0nl/+5gZ6HH14VJeycxIJ2tw7kNn97mJzznIOGfE3Vo2cyMbvsouKpNWvTdu/XsbsC+9m88JlpDdqAID9lMesc/5M4YZNKD2Ng9++h9X/m8K6L7+N6moqZFdoskv5FWMldZc0vJrf89bqfL+q0K7TQSycv5CcBTnk5+Uz+p2P6Nara7EyGzdsKnpeq3YmJN04l5aeRs3MmqSlJcislcnyxWWuq7VL69yxAw3q14s6jGrx9bdz2bt5U1o2a0pGRjonH9eFj8ZPKVZm7oJFHHXYwQC0bdWcRbnLWL5ydbEyX3w1k1bNm9KiaeNqi70y1e20H5vm5/DTgiVYXj4r3hnLnr26FCvT6KzjWDFiPJsXBn/7+cu3fgaF4b8dZaShjPRd4qZTy7cKP6KS8glpZ0n6ObXAHU5IktJ+xvtUmcbNGpO7aGnR9tKcpTRptu2Xx7EnH8MLnzzD/c/dw32/C2oIyxYv45XBr/P6hJd5a8rrrF+zjoljJldb7K7qLFm+kr2aNCza3qtxQ3KXryxW5oC2rRg9Lvj/e/o3c8nJXc6SEmU+GPMFpxx3VNUHXEVqNGvE5kXLi7Y35ywno3nDYmUy92lB+h51afd/g2j/wQM0Prv71oOJBO3/+xCHT3uG1WO+Yv2U76op8p1QuAOPiJSbkCS1kTRL0lBJMySNklRL0seSOodlGkuaHz6/WNLbkt6VNE/S1ZJulDRF0nhJDct4r/0kfSjpK0lfSto3PFRX0v9Jmi3pRUkKy/9J0kRJX0sakrT/Y0n3SvoEuE7S6ZK+CGP4MFzfHUl1JT0jabqkaZL6SroPqCVpqqQXw3IXSZoQ7ntyS/KRtE7SIElfAEdLuk/SzPBcpbb/SLpM0iRJk3LWL6zQ/0k/R/BJFFfar7hPP/iM/scP4LZL/8SlN18MQN0GdenWqyvn/eJCzjr8XDJr1+KkX/2yymJ10VKJP5ZLzzmVNes3cM41f+Ll4R9y0L57k5bY+lWRl5fPxxOm0rPbkdUdauUp5d8HJf55KD1BnQ778k3/e5h9wSCyrj+bzH2aBwcLC/n6pN8x5YiB1O24H7UO3LvKQ95ZVljxR1QqWkPaH3jMzA4BVgF9yynfHrgA6ALcA2wws07A58Cvy3jdi+H7HAZ0BXLC/Z2A64GDgX2AY8L9/zKzI82sPVALOC3pXHuY2fFm9hAwFvhFGMMrwO/DMncAq82sg5kdCvzPzP4IbDSzjmZ2oaR2wHnAMWbWESgALgxfXwf42syOAmYCZwGHhOf6S2kXaGZDzKyzmXVuXiertCKVYmnOMpq2aFK03aR5E5YtWb7d8l99MZ2s1i1osGd9Oh97ODkLFrN6xWoK8gsY8/6ntO98cJXF6qrPXo32ZMnSFUXbS5atoEnDPYqVqVu7FndffymvPzqIe24cyMrVa8lqtvVvaezkabTbtzWN9mxQXWFXus05y6nRolHRdo3mjchbvGKbMqs+nkLhxp/IX7GWNV/MpPbBbYqVKVizgTWfz6DBCZ2qI+ydszvUkELzzGxq+Hwy0Kac8h+Z2VozWwqsBt4N90/f3msl1QOyzOwtADPbZGYbwsMTzCzbzAqBqUnnOCGs+UwHTgQOSTpl8rCXlsDIsNzNSeV+CTy2pZCZFW+XCPQAjgAmSpoabu8THisA3gifrwE2AU9J+hWwgQjNnjqblm2zaN6qGekZ6fTocwKfhQMYtshq06Lo+QHt9yc9I4PVK9ewZGEuBx/ejpqZNQE4otvh/PDdgmqN31WNQw5oyw+LcslevJS8vHw+GDOB7kcV/zJds24DeXn5ALwxcgyHH3IgdWvXKjr+/ie7dnMdwLqpc8hs25yarZqijHQa9unGylETi5VZ+cEE6nVpB2kJErVqULfTAWz8biHpDeuTVr82AMqsQf1jD2XTnOzS3ial7Ao1pIr2r/yU9LyAoDaSz9aElllG+cKk7cIy3rO0SvT23j9dUibwONDZzH6UdGeJONYnPX8UeNjMhknqDtyZ9J7l9eAJeM7Mbinl2CYzKwAws3xJXQgSVj/gaoIkGYmCgkL+cfujPPjS/SQSCUa8+j7zv/2BM/oHlchhLwzn+N7H0evsk8jPz+enTZu584q7AZg1ZTYfvzeGp0YOpiC/gO9mzOHdF9+L6lKq3M1/vo+JU6axatUaepx5EVde2p++p/eKOqwqkZ6Wxq2XX8gVf3qIgsJCzjzpWPZrncVrIz4C4NzeJzDvx0Xc9vBQEmkJ9m3Vgruuu6To9Rs3/cTnU2dwx9W/ieoSKkdBIfNve4oDX/oTSkuw9JXRbPz2R5r2D4aw574wik1zFrL64ykcOvrvWKGx9KUP2fjNAmq1a82+j1yDEglIJFjx7mes+jD1+1gtP+oIyqfyRodIagMMD5vFkHQTUJeg1jHZzJ6QdD1wvZm1kXQxQZK4Oiw/P9xeVvJYKe81HrjPzN6WVBNII2j2u8nMTgvL/AuYBLwNfENQW0oDxgP/Z2Z3Svo4fM2k8DVTgN+a2WRJzwBtzax72F+UaWbXh+X2NLOVklYCTc0sT9LBwDsETXa5YR9YPTP7QdI6M6sbvrYuUDupzBwz225/GcBxWT1Sf2hONRj91dCoQ0gZhSsXRx1Cyph6/O4/DH9HHLXozbJ+tJcrt8fxFf6+aTr6k3LfS9LJwCME379Pmdl9JY7fzNbujXSgHdDEzIq3jSbZmVF2DwJXSBoHVNbYz/7AtZKmAeOAZtsraGargKEEzYBvAxO3V5agRvS6pE+B5PHLfwH2DAdFfAWcEO4fAkyT9KKZzQRuB0aFcf0XaF7Ke9QDhodlPgFuKPtSnXOu+lRmk104sOsx4BSCvv3zwx/vW9/P7IGwL74jcAvwSVnJCCpQQ3JVx2tIAa8hbeU1pK28hlTcztaQlnTvXuHvm70+/rjM95J0NHCnmfUKt28BMLO/bqf8SwRjC8r8x77b34fknHNux2pIybenhI/LSpwuC/gxaTs73LcNSbWBk9k6AGy7Ipk6SNJjbB26vcUjZvZMFPE459zuzgorXsEysyEEXRfbU4E7uYqcDnxWXnMdRJSQzOyqKN7XOefiqrBgp1r8SsoGWiVttwQWbadsP+DlipzUm+yccy4GKvk+pInA/pLaSqpBkHSGlSwkqQFwPMFI5XL5bN/OORcDO9JkV+65gvsurwZGEgz7ftrMZki6PDy+ZR2Xs4BRZrZ+O6cqxhOSc87FQGUPqDazEcCIEvsGl9h+Fni2ouf0hOScczFQmTWkquIJyTnnYqCSBzVUCU9IzjkXA15Dcs45lxLMPCE555xLAVEuK1FRnpCccy4GCr2G5JxzLhV4k51zzrmU4KPsnHPOpQQfZeeccy4leB+Sc865lOB9SM4551LCrrA4uCck55yLAW+yc845lxIKfVCDK8u4pbOjDiElFK5cHHUIKSOxZ7OoQ0gZzZqtiTqE3YrXkJxzzqUEH9TgnHMuJXgNyTnnXErYBQbZeUJyzrk4KChMRB1CuTwhOedcDOwCq0+Q+inTOefcTjNU4UdFSDpZ0jeS5kj643bKdJc0VdIMSZ+Ud06vITnnXAwUVmInkqQ04DHgJCAbmChpmJnNTCqzB/A4cLKZLZDUtLzzeg3JOedioBBV+FEBXYA5ZjbXzDYDrwB9SpS5AHjTzBYAmFlueSf1hOScczFQyU12WcCPSdvZ4b5kBwB7SvpY0mRJvy7vpN5k55xzMVBQwb4hAEmXAZcl7RpiZkOSi5TyspKNgunAEUAPoBbwuaTxZvbt9t7XE5JzzsXAjoyyC5PPkDKKZAOtkrZbAotKKbPMzNYD6yWNAQ4DtpuQvMnOOedioHAHHhUwEdhfUltJNYB+wLASZd4BjpWULqk2cBQwq6yTeg3JOedioKLDuSt0LrN8SVcDI4E04GkzmyHp8vD4YDObJekDYBpBnnvKzL4u67yekJxzLgYqe/UJMxsBjCixb3CJ7QeAByp6Tk9IzjkXAxUczh0pT0jOORcDBVEHUAGekJxzLgYK5TUk55xzKcCXn3DOOZcSdoXZvj0hOedcDFT2KLuq4AnJOediYEemDoqKJyTnnIsBryG5SPXq2Z2HHx5EWiLB08+8zN8eeKzY8fPPP4ubb7oSgPXrNnDVNbcwbVqwnEmDBvUZ8uSDHHLIgZgZAwf+jvFfTK72a6gsYydP5/4hL1FYWMiveh7HpeecWuz4mnXr+dM/nubHxbnUzMjgrusuYf82LZmXncPv73+iqFz24qVcedFZ9O/Ts7ovoVrcfu/DjPlsAg333IO3/zO4/BfswjK7HknDm66EtATr3nqfNc++sk2ZmkccRsObroD0dApXrWbJwN8BUO/CvtQ98xQwI2/OPJbd+QBszqvuS9gh3ofkIpNIJPjnI/dwcu/zyc7OYfznI3h3+ChmzfquqMz8eT9yYo+zWbVqNSf3OoHBj99P126nA/D3hwcxcuRHnNfvMjIyMqhdu1ZUl7LTCgoKufeJFxjyl5vYq1FDzr9hEN2P6si+e2+dLX/oa8M5cJ9W/OP2a5j3Yw73PPECT937e9q2bM7rjw4qOs8vf3MDPY4+PKpLqXJn9j6JC/qewa13Pxh1KFUrkaDhH64h98o/kL9kKc3/8xgbPxlH3rwFRUVUtw4Nb7mW3KtvoWBxLok99wAgrUkj6vc7k0VnX4r9tJnG991BnV4nsP7dURFdTMXsCqPsfHLVnSRpXdQxlKbLkZ34/vv5zJu3gLy8PF577R3OOL1XsTKfj5/EqlWrARj/xZdkZTUHoF69uhzb7SiefuZlAPLy8li9ek31XkAl+vrbuezdvCktmzUlIyOdk4/rwkfjpxQrM3fBIo467GAA2rZqzqLcZSxfubpYmS++mkmr5k1p0bRxtcVe3Tp37ECD+vWiDqPK1Wh/IPnZi8hfmAP5+awf+TG1uh9TrEydU3qw8X9jKVgcrCtXuHLV1oNpaahmTUhLoFo1KVi6vBqj/3kKVfFHVDwh7QBJu0yNskVWM37M3jobfPbCHFq0aLbd8pcM6McHIz8CYJ99WrNs2XL+/dTfmThhJE8OfmCXriEtWb6SvZo0LNreq3FDcpevLFbmgLatGD0uaJKc/s1ccnKXs6REmQ/GfMEpxx1V9QG7KpfepDH5i7cuYFqQu5S0po2KlclonUWifl32GvIQzV58nDqnnhSUXbqcNS+8TtaIl2g56jVs7Xo2jU/95uxKnu27SkSakCS1kTRL0lBJMySNklQrXGGwc1imsaT54fOLJb0t6V1J8yRdLelGSVMkjZfUcDvv01TS5PD5YZJM0t7h9veSaktqLWm0pGnhf7ccf1bSw5I+Au4Pp1v/XNJESXcnvUdzSWMkTZX0taRjq/bTK5tKuSvbrPRKe/fjuzJgwPnccuu9AKSnpdGpUweefPJ5juzSi/XrN/CH319dpfFWt5Kfz6XnnMqa9Rs455o/8fLwDzlo371JS2z955GXl8/HE6bSs9uR1R2qqwqlzVpQ8p9HWho12h1A7rW3kXvVH2kw8ELS984iUa8utbt3ZeFpF5Hd6zxUK5M6vXtUS9g7o0AVf0QlFWpI+wOPmdkhwCqgbznl2xOs1d4FuAfYYGadgM+BUpfIDddyz5RUHzgWmESwTkdrINfMNgD/Ap43s0OBF4F/Jp3iAOCXZvY74BHgCTM7ElicVOYCYKSZdSRYhGpqabFIukzSJEmTCgvXl3OpP9/C7BxatWxRtN0yqzk5OUu2KdehQzueHPwAv+p7CStWBDWC7IU5ZGfnMGFi0Kz15pvv0aljhyqLtart1WhPlixdUbS9ZNkKmjTco1iZurVrcff1l/L6o4O458aBrFy9lqxmTYqOj508jXb7tqbRng2qK2xXhfJzl5LerGnRdlrTJts0uxUsWcbGcROxTZsoXLWGn76cTo0D9iXzqMPJX7iYwlWrIb+ADf8bS81DD6nuS9hhXkOqmHlmNjV8PhloU075j8xsrZktBVYD74b7p5fz2nHAMcBxwL3hf48FPg2PHw28FD5/AeiW9NrXzWzL3ITHAC8nldtiIjBA0p1ABzNbW1oQZjbEzDqbWedEok5Z17lTJk6ayn77taVNm1ZkZGRw7rl9eHd48U7XVq1a8PqrQ7l4wHV8993cov1LliwlO3sRBxywLwAnntiNWbO2u8hjyjvkgLb8sCiX7MVLycvL54MxE+h+VKdiZdas20BeXj4Ab4wcw+GHHEjdpGbK9z/x5rrdyeYZ35DeKov0Fs0gPZ06vbqz8ZNxxcps+GQcNTu1D/qJMmtSo/1B5M1bQP7iXGp0aIcyawKQ2aVTscEQqWpXSEip0CfyU9LzAoK11/PZmiwzyyhfmLRdSNnX8ylBAmpNsJLhHwgq6cO3Uz65Al+yKrNN25eZjZF0HHAq8IKkB8zs+TLiqVIFBQVcd/3tjHjvJdISCZ597lVmzvyWywb2B2DI0Be4/bYbaNRoTx59NGiqy8/P5xdH9wbguhvu4PnnHqVGjQzmzVvApb+9MapL2WnpaWncevmFXPGnhygoLOTMk45lv9ZZvDYi6DM7t/cJzPtxEbc9PJREWoJ9W7XgrusuKXr9xk0/8fnUGdxx9W+iuoRqc/Of72PilGmsWrWGHmdexJWX9qdvicEwu4WCQlbc/yhNH7sPEgnWDfuAvLk/ULfvaQCse2M4+fMWsGncJJq/OhQKC1n39vvkfT8fgA2jx9D8xSewggI2fzOHtW++F+HFVMyuMMpO2+tXqJY3l9oAw82sfbh9E1CXYH32yWb2hKTrgevNrI2ki4HOZnZ1WH5+uL2s5LHtvNcYYIyZXSRpBEHz32FmtlLSMIKa0AvhufqY2VmSng1j/L/wPMOA18zsP5KuAB4ws7ph89/CcCXF64E2ZnZ9WdefXiNrV/gbqXLrZ7wedQgpI7Hn9geexM2inpdFHUJKaf3lhzvVu/PI3hdV+PvmugX/iaQnKRWa7ErzIHCFpHFApYyxNbP54dMx4X/HAqvMbMtQqmsJmtymAf2B67ZzquuAqyRNBJI7FLoDUyVNIegHe6Qy4nbOucqwKzTZRVpDijuvIQW8hrSV15C28hpScTtbQ3pwB2pIN0VUQ0qFPiTnnHNVzOeyi4CkxwhGwiV7xMyeiSIe55xLBT6XXQTM7KqoY3DOuVRT2f0Dkk4m6CtPA54ys/tKHO9OMKJ5XrjrTTMbVNY5d7uE5JxzbluFlZiSJKUBjwEnAdnAREnDzGxmiaKfmtlpFT1vqo6yc845V4kKduBRAV2AOWY218w2A68AfXY2Rk9IzjkXAzsy7Dt5irPwUXLIYxbwY9J2drivpKMlfSXpfUnlzq/kTXbOORcDOzLKzsyGAEPKKFLa2Uq2CX4JtDazdZJ6A28TzF26XV5Dcs65GCjEKvyogGygVdJ2S2BRcgEzW2Nm68LnI4AMSWVOdOAJyTnnYsB24FEBE4H9w+V4agD9gGHJBSQ1U7jOi6QuBPmmzJUMvcnOOedioDLvQwrn7LwaGEkw7PtpM5sh6fLw+GDgbIIp4PKBjUA/K2dqIE9IzjkXAwWVfCdS2Aw3osS+wUnP/0WwzlyFeUJyzrkY8JkanHPOpYTKvDG2qnhCcs65GEj9dOQJyTnnYsGb7JxzzqWEyh7UUBU8ITnnXAx4H5JzzrmUkPrpyBOSc87FgteQnHPOpQQf1ODKlNAusMh9NZh6/INRh5AymjVbE3UIKaPFqLImm3Y7yryG5JxzLhX4KDvnnHMpwZvsnHPOpYTCsifaTgmekJxzLgZSPx15QnLOuVjwYd/OOedSgo+yc845lxLyPSE555xLBV5Dcs45lxJ82LdzzrmUYLvAsO9E1AE455yreoVYhR8VIelkSd9ImiPpj2WUO1JSgaSzyzun15Cccy4GKnPqIElpwGPASUA2MFHSMDObWUq5+4GRFTmv15Cccy4GKrmG1AWYY2ZzzWwz8ArQp5Ry1wBvALkVOaknJOeciwEzq/CjArKAH5O2s8N9RSRlAWcBgysaoyck55yLgcIdeEi6TNKkpMdlJU5X2to5JTPZP4A/mFlBRWP0PiTnnIuBHbkPycyGAGUtSJUNtErabgksKlGmM/CKgnXfGgO9JeWb2dvbO6knJOeci4FKnstuIrC/pLbAQqAfcEFyATNru+W5pGeB4WUlI/CE5JxzsVBglXdrrJnlS7qaYPRcGvC0mc2QdHl4vML9Rsk8ITnnXAxU9tRBZjYCGFFiX6mJyMwursg5PSE551wM+AJ9zjnnUkLqpyNPSM45Fwu+QJ+LVM+e3Xn4obtIpKXxzNMv88CDjxU7fn6/s7jppisBWLduPddccwvTps/igAP24cX/PFFUrm3bvblr0IM8+ui/qzX+ytSgeyda330JSiTIfflDcv711jZl6h19CK0HXYLS08hfsZZZfe9ANTM4+M2/oBoZKD3Bivc+Z+GDr0ZwBZUns+uRNLzpSkhLsO6t91nz7CvblKl5xGE0vOkKSE+ncNVqlgz8HQD1LuxL3TNPATPy5sxj2Z0PwOa86r6EanH7vQ8z5rMJNNxzD97+z8/qo08pnpAiJulWM7s36jiikEgkeOSRv9C79wVkZ+fw+bj3GD58FLNmf1dUZt78BfT45dmsWrWaXr1O4PHH/0a3Y0/n22/ncmSXXkXnmT9vEu+880FUl7LzEgna3DuQ2f3uYnPOcg4Z8TdWjZzIxu+yi4qk1a9N279exuwL72bzwmWkN2oAgP2Ux6xz/kzhhk0oPY2D376H1f+bwrovv43qanZOIkHDP1xD7pV/IH/JUpr/5zE2fjKOvHkLioqobh0a3nItuVffQsHiXBJ77gFAWpNG1O93JovOvhT7aTON77uDOr1OYP27oyK6mKp1Zu+TuKDvGdx694NRh1IpKnOUXVXZ3WdquDXqAKJy5JEd+f77+cybt4C8vDxee+0dTj+9Z7Ey48dPZtWq1QB88cWXZGU13+Y8J57Yjblzf2DBgoXVEndVqNtpPzbNz+GnBUuwvHxWvDOWPXt1KVam0VnHsWLEeDYvXAZA/vLVRccKN2wCQBlpKCN9l5jGf3tqtD+Q/OxF5C/Mgfx81o/8mFrdjylWps4pPdj4v7EULA6mHytcuWrrwbQ0VLMmpCVQrZoULF1ejdFXr84dO9Cgfr2ow6g0tgP/i0qFE5KkNpJmSRoqaYakUZJqSfpYUuewTGNJ88PnF0t6W9K7kuZJulrSjZKmSBovqWEZ73WtpJmSpkl6JdxXR9LTkiaG5+iT9D5vSvpA0neS/hbuvw+oJWmqpBfDfRdJmhDuezKciRZJ6yTdI+mrMLa9wv17SXor3P+VpK7bO0/4eFbS15KmS7phx//vqDxZLZqT/WNO0fbChYtpUUrC2WLAgH6MHPnRNvvPPecMXn3tnSqJsbrUaNaIzYu2fnFuzllORvPif36Z+7QgfY+6tPu/QbT/4AEan91968FEgvb/fYjDpz3D6jFfsX7Kd+yq0ps0Jn/x1nkuC3KXkta0UbEyGa2zSNSvy15DHqLZi49T59STgrJLl7PmhdfJGvESLUe9hq1dz6bxk6s1fvfzVfJcdlViR2tI+wOPmdkhwCqgbznl2xPcvdsFuAfYYGadgM+BX5fxuj8CnczsUODycN9twP/M7EjgBOABSXXCYx2B84AOwHmSWpnZH4GNZtbRzC6U1C4sc4yZdQQKgAvD19cBxpvZYcAYYGC4/5/AJ+H+w4EZZZynI5BlZu3NrAPwTDmfTZVSKTNNbe8P7fjjuzLg4n7cets9xfZnZGRw2mk9eeON4VURYvWpwKxbSk9Qp8O+fNP/HmZfMIis688mc58wgRcW8vVJv2PKEQOp23E/ah24d5WHXGVK/cMosZ2WRo12B5B77W3kXvVHGgy8kPS9s0jUq0vt7l1ZeNpFZPc6D9XKpE7vHtUSttt5lb0eUlXY0T6keWY2NXw+GWhTTvmPzGwtsFbSauDdcP904NAyXjcNeFHS28Db4b6ewBmSbgq3M4Et3wyjzWw1gKSZQGuKz0QL0AM4gmDdDoBabJ0SfTOw5Vt3MsEaHwAnEibOcILA1ZL6b+c87wL7SHoUeA8otWE9nKTwMoC0tD1IpNUprdhOy16YQ8tWW2tEWVnNyFm0eJtyHdq3Y/Dgv3HGGf1ZsWJVsWMnn3wCU6ZOJzd3WZXEWF025yynRouttYAazRuRt3jFNmVWrVhL4cafKNz4E2u+mEntg9uwae7WWmbBmg2s+XwGDU7oxMZvFrArys9dSnqzpkXbaU2bbNPsVrBkGRtXrcE2bcI2beKnL6dT44B9g9cvXExh2My74X9jqXnoIawfMbr6LsD9bLtCU/OO1pB+SnpeQJDQ8pPOk1lG+cKk7ULKToanEiz+dAQwWVI6we/cvmGNp6OZ7W1ms8qIqyQBzyW9/kAzuzM8lmdb/9/a3uvLPI+ZrQQOAz4GrgKeKu3FZjbEzDqbWeeqSkYAkyZ9xX77taVNm1ZkZGRw7rl9GD78v8XKtGrVgldfG8qAAdfx3XfztjnHeef24dVXd+3mOoB1U+eQ2bY5NVs1RRnpNOzTjZWjJhYrs/KDCdTr0g7SEiRq1aBupwPY+N1C0hvWJ61+bQCUWYP6xx7KpjnZpb3NLmHzjG9Ib5VFeotmkJ5OnV7d2fjJuGJlNnwyjpqd2gf9RJk1qdH+IPLmLSB/cS41OrRDmTUByOzSqdhgCJfaCiis8CMqlTHKbj5B4pgAlLtEbXkkJYBWZvaRpLEETX51CeZMukbSNWZmkjqZ2ZRyTpcnKcPM8oDRwDuS/m5muWEfVj0z+6GM148GrgD+EfY31dneeYD1wGYze0PS98CzP/9T2HkFBQVcf/0dvDf8RRJpCZ579lVmzvqWgQMvAmDo0P9w26030KjhHjz6z2AgYn5+Pkd3PRWAWrUy6dHjOK68arsrE+86CgqZf9tTHPjSn1BagqWvjGbjtz/StH8wyCP3hVFsmrOQ1R9P4dDRf8cKjaUvfcjGbxZQq11r9n3kGpRIQCLBinc/Y9WHu3C/SUEhK+5/lKaP3QeJBOuGfUDe3B+o2/c0ANa9MZz8eQvYNG4SzV8dCoWFrHv7ffK+nw/AhtFjaP7iE1hBAZu/mcPaN9+L8GKq1s1/vo+JU6axatUaepx5EVde2p++p/eKOqyfbVeYqUEVrcZJakMwW2v7cPsmgkTxCvAasA74H3CRmbWRdDHQ2cyuDsvPD7eXlTxW4n0ygI+ABgS1kf+Y2X2SahGsr9E13D/fzE4r5X2GAw+a2ceS7gfOAL4M+5HOA24hqNHlAVeZ2XhJ68ysbvj6s4HTzOzicHDDEGAfgprTFWb2eWnnATYS9BttqS3eYmbvl/WZ1qjZMvX/QqrBp426lF8oJpo1WxN1CCmjxaiyVj+In4zG+5TWG1phh+x1VIW/b2Ys+WKn3uvnqnBCcpXPE1LAE9JWnpC28oRU3M4mpHZNu1T4+2ZW7oRIEtJufWOsc865QJT3F1VUpAlJ0mPAMSV2P2JmkQ6Zds653c2u0IcUaUIys6uifH/nnIuLXWHqIG+yc865GPAmO+eccynBvIbknHMuFfjyE84551LCrnCLjyck55yLgV2hhrS7r4fknHMOKCgsrPCjIiSdLOkbSXMkbTPHmKQ+4RJCUyVNktStvHN6Dck552KgMkfZhXN7PkawMkI2weoHw8xsZlKx0cCwcO7RQwmmmDuorPN6Dck552Kgkhfo6wLMMbO5ZraZYE7TPiXeb13SKgp12HblrW14QnLOuRjYkQX6JF0WNrNteVxW4nRZFF9zLjvcV4yksyTNJlgj7pLyYvQmO+eci4EdGWVnZkMIVjrYngqswwxm9hbwlqTjgLuBX5b1vp6QnHMuBio6WKGCsoFWSdstgUXbK2xmYyTtK6mxmW13CWpvsnPOuRjYkSa7CpgI7C+praQaQD9gWHIBSftJUvj8cKAGsLysk3oNyTnnYqAyb4w1s3xJVxOs5J0GPG1mMyRdHh4fDPQFfi0pj2AB0/OsnCA8ITnnXAxU9vITZjYCGFFi3+Ck5/cD9+/IOT0hOedcDPhs384551KCL9DnnHMuJRT68hPOOedSgc/27ZxzLiXsCglJu0KQrupIuiy8Kzv2/LPYyj+LrfyzqD5+Y6wrOUdVnPlnsZV/Flv5Z1FNPCE555xLCZ6QnHPOpQRPSM7bxrfyz2Ir/yy28s+imvigBueccynBa0jOOedSgick55xzKcETknPOuZTgCSlmJLWtyD7nnKtunpDi541S9v1ftUeRIiT9TVJ9SRmSRktaJumiqOOKgqRjJNUJn18k6WFJraOOq7pJui78m5Ckf0v6UlLPqOOKA09IMSHpIEl9gQaSfpX0uBjIjDi8KPU0szXAaUA2cABwc7QhReYJYIOkw4DfAz8Az0cbUiQuCf8megJNgAHAfdGGFA8+uWp8HEjwpbsHcHrS/rXAwCgCShEZ4X97Ay+b2QpJUcYTpXwzM0l9gEfM7N+SfhN1UBHY8gfQG3jGzL5SjP8oqpMnpJgws3eAdyQdbWafRx1PChkmaTawEbhSUhNgU8QxRWWtpFuAi4DjJKWxNWHHyWRJo4C2wC2S6gGpv5jQbsBvjI0ZSQcQNM3sZWbtJR0KnGFmf4k4tGonKQH8ApgFrDGzgrAPpZ6ZLY42uuonqRlwATDRzD6VtDfQ3cxi1WwX/l10BOaa2SpJjYAsM5sWbWS7P09IMSPpE4I+kifNrFO472szax9tZNGQ9LmZHR11HC51hM1zFwL7mNmgMDE3M7MJEYe22/NBDfFTu5R/WPmRRJIaRknqG+c+AklrJa3Z3iPq+CLwOHA0cH64vRZ4LLpw4sP7kOJnmaR9AQOQdDaQE21IkboRqAMUSNpI0KFtZlY/2rCqj5nVA5A0CFgMvEDwOVwI1IswtKgcZWaHS5oCYGYrJdWIOqg48IQUP1cRzF58kKSFwDyCTuxY2vJl7ADoZWZHJW0/IekL4G9RBRSRvHBAx5YfbU3wQQ3VwpvsYsbM5prZLwnurzjIzLqZ2fyIw4pMePPjRZLuCLdbSeoSdVwRKZB0oaQ0SQlJFwIFUQcVgX8CbwFNJd0DjAXujTakePBBDTEj6cZSdq8GJpvZ1GoOJ3KSniD49XuimbWTtCcwysyOjDi0aiepDfAIcAxB7eAz4Po4/mCRdBDQg6DpcrSZzYo4pFjwJrv46Rw+3g23TwUmApdLet3M4tY84/0FQNhEdZWZ9Yk6lqhJ+gUww8weC7frSTrKzL6IOLTdnjfZxU8j4HAz+52Z/Y4gOTUBjgMujjKwiHh/AWBmBcARUceRIp4A1iVtrw/3uSrmNaT42RvYnLSdB7Q2s42SfooopiiV7C84G7gj2pAiM0XSMOB1gi9hAMzszehCioQsqS/DzAol+XdlNfAPOX5eAsZLeifcPh14OZyhYGZ0YUXDzF6UNJmt/QVnxri/oCGwHDgxaZ8BcUtIcyVdy9Za0ZXA3AjjiQ0f1BAj4c2fLYGmQDeCL+CxZjYp0sAiJOkFM+tf3j4XH5KaEtScTyRIyKMJBnfkRhpYDHhCihlJk83M+wpCkr40s8OTttOA6WZ2cIRhRUJSS+BRto6yGwtcZ2bZkQbmYsOb7OJnvKQjzWxi1IFEKZzV+lagVtL0OCLoXxsSWWDReoagSfeccPuicN9JkUUUgXBgy0CgDUnfkWZ2SVQxxYXXkGJG0kyCReh+IOi43jJVzqGRBhYRSX81s1uijiMVSJpqZh3L27e7kzQO+BSYTNKNwWZW2mrLrhJ5DSl+Tok6gBQzQVIDM1sNIGkPgiUX3o40qmhsWb795XD7fIJBDnFT28z+EHUQceT3IcWMmf1gZj8QLEhnSY+4+vOWZARgZquAP0cXTqQuAc4lmGA1h2AIfBybqYZL6h11EHHkTXYxI+kM4CGgBZALtAZmmdkhkQYWEUnTSjZXSppuZh2iiikqkjLNLK6r5RaRtJZgBvjN4SN2M8BHxZvs4uduglVSPzSzTpJOYOu6L3E0SdLDBOvdGHANQd9BHH0taQlB/8kY4LPk2mNc+Azw0fEmu/jJM7PlQEJSwsw+IliuOa6uIfgV/CrBDAWbCJboiB0z24/gx8l04DTgK0lTIw0qAj4DfHS8hhQ/qyTVJfgF/KKkXILpg2LJzNYDf4w6jlQQ3od0DHAscBgwg+BepLh5nHAGeIIWhXUENejYzQBf3Twhxc9XwAbgBoIVQRsAdSONKELhPSe/Bw4BMrfsN7MTt/ui3dcCgpnf7zWzy6MOJkI+A3xEvMkufk4ws0Izyzez58zsn8T7l9+LwGygLXAXMJ/gSzmOOgHPAxdI+lzS85IujTqoCPgM8BHxUXYxIekKgkki9wXmJB2qR9B5HctlzLdMpZQ82k7SJ2Z2fNSxRSFszu1G0Gx3EcHosjaRBlXNwpVyzwMOB54jnAHezF6LNLAY8Ca7+HgJeB/4K8X7TNaa2YpoQkoJW/rPciSdCiwimIA2diRNAmoC4wj6jo4L71mLFZ8BPjpeQ3KxJuk0gmHOrQgmFq0P3GVmwyINLAKSmpjZ0jKO/8bMnqvOmKLgM8BHx2tILrbCfoL9zWw4sBo4IeKQIlVWMgpdR9CEtbsrdpN4+HfiM+RXAx/U4GIrXLb7jKjj2IUo6gCqkqRbwlkaDpW0RtLacDsXeKecl7tK4E12LtbCZcsbENwYm7xs95eRBZWiSq4dtbvyGeCj4wnJxZqkj0rZbTG9D6lMkqaYWaeo46gOkrII5nlMXg9pTHQRxYP3IblYM7NY9xvtoM+iDqA6SLoP6AfMZOt6SEYwu4mrQl5DcrEXDvcuOVPDoOgiika4FtSv2Xal1GsjCikSkr4BDjWzn6KOJW68huRiTdJgoDbBCLunCG6CnBBpUNEZAYwnmFw1zjMTzAUyAE9I1cxrSC7WtszQkPTfusCbZtYz6tiqW1wGLZRH0hsEk8uOJikpxa2mGAWvIbm42xj+d4OkFgRLdreNMJ4ovSBpIDCc4l/EcZvJY1j4cNXME5KLu+Fh38kDwJcEnddPRRpRdDYTfA63sXVZewP2iSyiCJjZc5JqAXub2TdRxxMn3mTnXEhSTSAzjqukAkj6nmDphWVRxxIlSacDDwI1zKytpI7AIDPzm6irmNeQXOxJ6krSyDJJmNnzkQYVjRkEa2XF3Z1AF+BjADObKimuzbjVyhOSizVJLxAsyTGV4vecxDEhFQBTw5uF49yZn29mq6ViMyV5U1I18ITk4q4zcLB52zXA2+Ej7r6WdAGQJml/4FqCJTlcFfM+JBdrkl4HrjWznKhjcalBUm2CgR09CSaUHQncbWabIg0sBjwhuViS9C5BM0w9oCPBzbDJzVSx68CWNI9SmqbMLFaj7JKFS0/UMbM1UccSB95k5+LqwagDSEGdk55nAucADSOKJTKSXgIuJ+hTmww0kPSwmT0QbWS7P68huVgLR0/lbGmOCe8/2cvM5kcaWIqQNNbMukUdR3WSNNXMOkq6kGBhvj8Ak83s0IhD2+15DcnF3etA16TtgnDfkdGEEx1JydMGJQhqTPUiCidKGZIygDOBf5lZniT/5V4NPCG5uEs3s81bNsxss6QaUQYUoYfY2oeUD8wnaLaLmycJrv0rYIyk1oD3IVUDb7JzsSbpv8CjZjYs3O5DMOquR7SRVT9JmUBfii8/YXFciiOZghuS0swsP9z+jZk9F3FYuyVPSC7WJO0LvAi0CHdlA/3N7PvoooqGpA+AVQRz+m25SRgzeyiqmFKRz4pedbzJzsVamHh+ES47ITNbm3w8Zr+GW5rZyVEHsQtQ+UXcz5GIOgDnUoGZrSuZjELXVXsw0RknqUPUQewCvFmpingNybmyxenXcDfg4vAG2Z8Irt18uPM24vQ3Ua08ITlXtjj9Gj4l6gB2EZ9FHcDuygc1OFcGSVPMrFPUcbjqEy7Y+GuKjzaM46zn1c5rSM6VzX8Nx88IYDwwHSiMOJZY8RqSizX/NexK8mHd0fEakos7/zXsSnpB0kBgOMVngF8RXUjx4DUkF2v+a9iVJOkq4B6Cm4S3fEFanJfhqC6ekFysSboBWIf/GnYhSd8DR5nZsqhjiRtvsnNxtxl4gGCF0KJfw4D/Go6vGcCGqIOII09ILu5uBPbzX8MuSQEwVdJHFK81+0CXKuYJycWd/xp2Jb0dPlw18z4kF2uS3gIOAfzXsHMR8xqSi7u38V/DLkk4l982v9R9lF3V84TkYi1GS0u4iuuc9DyTYNXchhHFEiveZOdizX8Nu4qQNNbMukUdx+7Oa0gu7vzXsCtGUvKN0gmCv5F6EYUTK15Dcq4E/zUcb+Fw7y1fjPnAfOBBM/s2sqBiwhOSi7Xt/Bq+wswOiygkFzFJmUBfik+4a2Y2KLKgYsKb7FzcPcS2v4bPiSwalwreJpjH7ktgU6SRxIzXkFys+a9hV5Kkr82sfdRxxFEi6gCci9jbwOlAHsEkq+uA9VEG5CI3TlKHqIOII68huVjzX8OuJEkzgf2AeQSzd4ig1nxopIHFgPchubgbJ6mDmU2POhCXMk6JOoC48hqSizX/Nexc6vCE5GJNUuvS9pvZD9Udi3Nx5wnJOedcSvBRds4551KCJyTnnHMpwROSc865lOAJyTnnXErwhOSccy4l/H8toiQn7pTldAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e87dbd",
   "metadata": {},
   "source": [
    "### Data Pre Processing\n",
    "Lower case\n",
    "\n",
    "Tokenization\n",
    "\n",
    "Removing special characters\n",
    "\n",
    "Removing stop words and punctuation\n",
    "\n",
    "Stemming\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7199d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1fe30c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c7f43298",
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e68fe5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4c51b20f",
   "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",
    "    \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",
    "     \n",
    "            \n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eed323c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transformed_text'] = df['text'].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "94b9e68f",
   "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_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "      <th>transformed_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_characters  \\\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                                   transformed_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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dbc09f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc = WordCloud(width=500,height=500,min_font_size=10,background_color='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a7fc14ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_wc = wc.generate(df[df['target'] == 1]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "979a5237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27bb38007c0>"
      ]
     },
     "execution_count": 41,
     "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": 42,
   "id": "5e824b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_wc = wc.generate(df[df['target'] == 0]['transformed_text'].str.cat(sep=\" \"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a2d3137c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27bb3338370>"
      ]
     },
     "execution_count": 43,
     "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": 44,
   "id": "6ea8e9b4",
   "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_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "      <th>transformed_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_characters  \\\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                                   transformed_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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d6fa30e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_corpus = []\n",
    "for msg in df[df['target'] == 1]['transformed_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5190b338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9941"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "04e2dd3d",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>call</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>free</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>txt</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>text</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>u</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ur</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mobil</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>stop</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>repli</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1\n",
       "0   call  320\n",
       "1   free  191\n",
       "2      2  155\n",
       "3    txt  141\n",
       "4   text  122\n",
       "5      u  119\n",
       "6     ur  119\n",
       "7  mobil  114\n",
       "8   stop  104\n",
       "9  repli  103"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "pd.DataFrame(Counter(spam_corpus).most_common(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "221bec8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arindham Krishna\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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SjrEQO1bKMd5ix0o5xkLsWCnHWIiti98yIqZURkfEmPkBXgpcVrp9LHDsIPazcDzFjpVyjIXYsVKO8RY7VsoxFmLHSjnGQuxg4sda89FNwAxJW0laHTgIWDDKZTIzmzBWHe0ClEXE05LeD1wGTALOjIg7RrlYZmYTxphKCgARcSlw6RB3c/o4ix0r5RgLsWOlHOMtdqyUYyzEjpVyjIXYruPHVEezmZmNrrHWp2BmZqPIScHMzAoTMilIWkvSNh3EbStpT0nrNG3fuyb+gE62dRub7/tE1U9F3CRJ/1q3n5WJpG8P47626mRb6b7ndrHvtSWtUrq9iqRndV/KsUnSGp1sG6vy57HeKDzvPEkblG5PlnRmTeyRnWwblnKN5z4FSR9sdX9EfLHiMfsCJwGrR8RWkmYCn4qINzbFHQHMBe4CZgJHRsTF+b6bI2LHin0P2D4csfm+D5VurgnsA9wVEe+siL0qInav2k8p5odA7Yff/H7kxzwbOB7YNT/2WtJ794dSzHoR8YSkDWv2+7815ZkMTKM0+CEibi7d3zw0WcAewH/XlTc/7nnA14BNIuIFkrYH3hgRJzTFVX0eiyLixTX7vYY02/4m4Brg5xFxW03s9cCrI+JP+fY6wE8j4mUVsZsA/wFsGhGvy2t/vTQizijFVH5HGsrvW9O+r4iIPdttK903GZhB+r419n1NRVzb77Kk22j9fdu+Yr8CDgGeGxGfkrQF8JyIuLGmvC8DptP/O1R54iDpXOC9wApgEbA+8MWI+EJT3Kci4hOl25OAb0fEIRX7XAN4S0UZPlVThlsiYod22/L2qve4Mnaoxtzooy6tO4jHHE9aY+kqgIi4VdL0irh3Ay+OiD/l+y+QND0ivkQ6IBUkvQ54PbCZpFNLd60HPD3Y2LKIOLlpPydRP4fjF5K+AvwX8OfSPsoHi5PqnquF80gHwLfk24fk53h1KeZcUsJaRDoIlN+rAAacYUv6NPAO4Df0HTgCeFUpbHPgTuBbpf3OAvq9LxW+CXwE+AZARCzOB4QT8nNvCzwfWF/Sm0uPW4/SwbBZRLwiz6V5CbA78CNJ60REVTJcs5EQ8mP/1KKmcDZwFvDv+fb/kN7jM0oxjde8Juk9+BXp/dgeuAHYrbxDSWsCzwI2ygf6xmeyHrBpVSEkvQs4kvS+3wrsAlxH6TOR9BxSYlxL0g5N+21+ffvk33Pz7+/k34cA/1dVBuCrwDP5OT8FPAlcSHrPm8v7HWDrXNYVeXMAdbXJ7fLJyyGk0Y5Hk76zX2iK20LSsRHxmXzQPx+oTLrAxcAf836eqokpW0XS5Ih4LL+GDWk6Jks6GPgnYKumE6N1gT9QIX+PPwdsTPpMBEREdFQbGtdJISI+OYiHPR0Rf0wnIS1NavwjR8R9knYnJYYtaUoKwEPAQuCNpC9Ew5NAc1NON7GtPIuKA2zWOANtvD+i6SAbEVd38VwNG0bEp0u3T5D0pnJAROyTf9c2vVQ4ENg60iKIdWaRDlL/DnwkJ/O/dPA6nhURNzZ93uXkuw3pgLUBsG9p+5OkE4NKknYDXp5/NgAuAX5eE/5nSTs2krKkFwN/qYndKCLmSzoWirk7K8oBEbFH3s95wOGNGoqkFwAfrtjne4CjSAlgEX3f3ydIqxJXOZJ08L0+IvbIybP5/20vUjLfnJSoyvv9aFOZ789l3DUidi3ddYykX5AO+s12jogdJd2S9/FYTsRVZpEO9J02fawmaTXgTcBXIuLvkqoeeyhwTv489gB+HBGn1Oxz84iobFqucTLwS0kXkP4/DwRObIr5JbCMtFRF+QToSWBxzX4/D+wbEXd1UZbCuE4KTWfaA0TEERWbb5f0T8AkSTOAI0hvfLPfS5oZEbfmff1J0j7AmcALm57nV8CvclV8aVMZtwEeG0xs033l6vckYArV/0iQa0FN+n3hJc2PiAMrqvWNs4oB1XngSkkHAfPz7f2BHzXtdzBNG7eTDqyPtHjcM8Apks7Pvx+ms+/vo5K2Jr9GSfuT/ska+70YuFjSSyPiug7213A1Kbl/Bri0TUI7Cjhf0kP59lTgrTWxf87NdI3y7kI6+6yybbnJKiJuz82h/eTa7ZckfSAivtyinGV/jYi/SkLSGhHxazX1w0XEPGCepLdExIUd7ndtSbtFxLVQNPmsXRP799xc03gvppBqDlVuB55D6bNt4xvAfaRa1jX5ZO+Jxp1N3+Mv5fhfAFeXE3yTX0p6YV0zYrOI+LakhaSTNQFvjog7m2LuB+7PNZqHIuKvuXxrkZLxfRW7fniwCQHGf5/CnFb35y9t82OeRTrbfG3edBlwQuPNLsVtTqpV/L5iH7tGxC8qtt8NfDwi5ufbHwIOi4gB14ToJjbfv2Xp5tOkD76yuUkd9D9ImhoRy5r2W2ic2TXt90nSP3DjH3MV+pqnIiLWk3Rl1f5KMa9q3ihpFqnqfTulanddP0F+zBuAXSPio3UxOe65pMk7LyMl3HuBt0XEfU1xHfU9lOI3IPWtvIJ0Rv0McF1EfLwmfjVSrUTAryPi7zVxOwJfBl5Aej+mAPtHxICzQknfI73/3yUdON8GrBMRB7d4Pzpqd5d0Eeks+SjSQesxYLWIeH1F7H8An4+Ix/PtycCHIuJjNa/vLFIbfpAS3jurDrL5QPhWYEdgHukk5GMRcX4pptE3ti6p7+9GOvwOVTzfqo3/qUF+j+8E/oH0HXuK1idYXcnJ42WNk49cY/pFRFQ1pX2JlCB/QP/34vsdPdd4TgpDIWntiPhz+8iu9jmVdAD6K7AJqZP6Q+X25MHEDkO51gAWRMReNfc/h9TPEsBNVYmwlyTdQToTu43SmeAgm7jqnmNtYJWIeLLm/qvJfQ+NzjtJt0fEC1rs8x+BV5KakF4GPBARryzd/6qI+G/176so1P2TSlqVvgRyd4sEsibwPlJigtTf87XmE5xSfGW7e02Nuvy4V5IO4j+pqhGpusO0qmN0EnBERJyiNNpHEVFXC2o8ZltgT9J7cUXzGXAuW61W36F8YvF8+nek19W+2+rmBGsQ+741ImY2bftVRLyoIvas6mIMHJRSZVw3HzXkauXRpKu1lT/gqmz+MlJn5TqkTqQXAe+JiH8ZajnymfdPSKu7PkNa4bXyIN9N7DCo7X9Q6lD8BGkUj4AvK424qBsa90b6DkJXRcQlNXFrAv9C6vQMUnv712sOWI9GRMumwMFSao//Aun9bTRDVI3yatf30Lzf35CWbb8W+DpwaMUB85Wk93VfBgqg7sxtJ/rO5neUVHk2n9/LU/JPJ9q2u6t69FijOWQdoGr02KTcxPRU3sdawIAhqRGxQtJs4JSIeKL5/hr3kJp1Vs373iIiHijt8+q8fW3gLxHxTK71bQv8uMXr/Drp/2IP0vFgf1Itozmuk9Fg6+XXU3nCMUyWS3pjRCzIzzmbmuWzI+LQoTzRSpEUgHNIIzTeQBpmNgdYXhN7CqmDbAGkNn5Jr6iJ7YqknwK/J1X9NwfOlHR1RHykIvZyUvtnOfaaiKjqKOy2HN30P3wE2CHysNLcnv1LUt9J834/S2oqOSdvOjK3D1ddS/vbpH+SRhv2waQRJ1VzMRZJ+gzpMylXd+tGeXTjDlIz108lvTXSkNiqUQYt+x4qzIjUz1ErIo7Lf74rIla0im2oO5unYhSNpF1Jo+m2pH9zUN0AhE7a3atGj5V/V+37u8AV+Qw1gHeSmnuqdDIyDgBJHwCOAx4mvReNMlQ1x1wDvDw3XV1B6u95K2l0U5WXRcT2khZHxCclnUx1kj6b9qPBuh5xNwjvJXV4n5b3uRT453KApH+LiM9L+jIVw3/b1QjLgeP+B1iUfy8ubbu6JvaG/PuW0rZfDVM5/qfp9qrAiprYNzXdnkTqYxiOcmxZ+tkMWLVF7BWkORuN26sDP6uJXUxqgimXeXFN7ID3tO59Bq6s+PnvYXovbs6/DyQliBc3tjXFPRf4GWl45O9INYAtW+z3efm9uz3f3p7U3l0V+wCpqXBPcpNti/3e1S6mFPtr0vXMNwae3fhpEX8lqW/gMlICXkBqVuz0vawtVy7HSaQRMnu1KUNHnzWwpNXrqfmcPwD8W/771hbxjePA9aRRWWsA91TE3ZR/31LaVrlf0knPu0kDAIb83a15jnWAdWvu2zf/nkNKGP1+On2OlaWm0GhzXZbbCR8inX1XeTA3IUXurDmC9I84HPo1/0QaTnhnVWBE/EBpWOOMiDgLmEw64xqy6KANU30T/34H3CDpYtLZxWwqqtElG9DXhLB+i7hbJO0SEdfn59uZNHqjqrx7tCvvECg/x/zcd/E9YIuKuDeRxqtfSV8H+quVJrDdWhHfcv5Dk21ITUhzgTMkXQKcF3kETpNuRtH8MSJqm0gqHN9poAZO2lqFdNCrPPPO5Whbli4/6wepH3nVTJJeSirfYXnbpBbxl+TBAl8gzTsIUjNSs25Gg51Fai79ch7gcAtpUuOXOnwNtTppxoqIH+Y/7yQNCZ5OXw2ysrZZ+Vw5s4xrSkNFf06aEftl0uSZ40tvUjl2I9IQs1eTDhg/Jc1WrpwI0uHzv4/Ufv5c0gSshnVJIwTeVvGY40htvNtExPMkbQqcH/3HcPdMfv5aUTEHRGkizWdJB06R+hY+GhHfq4i9i3QwbLT/bkFKvs+QR2RIeltEfFc1M9OjYkZ6tyS9OCIWlW6vR6qlfbsp7lzS57Egv7Y3kGYrb0v6XD7fFH9TRLyk3Mla1RlYUZ7JpO/fIREx4KCVR73MpINRNLk5bxKp2aOjZrfcGTojIn6mNBJvUlR0vks6m9TJ3W/SVkQcXxG7C+n/7h9JNc1JwJ+jZrJUpx28ks4gfYd+1PT6qlYqeAVpjsYvIuJz+aB8VHTQZJJf35pR0emtLkaD5fhJpCbWPUhNPn+JiG3blaGDMv6Y3IwVES/KgxFuiYgXVsTeTTphaR640VGH98pSUzgAuDYibgf2yJ1kJwH9kkL+wP4zKqaoD9G5pLOkzwDl9vUno2ZZB2A/YAfy7MiIeEjSYGZoD0rzQT8/d0SLzu6I+J6kq0hfegFHR/1IpU4m8TTGpw/761Ye+QNsWTEqpOo1PhvYMfqWojgOuICU+BaRJgSVddUHoTRK5q2kZpabSM1ZVY5v8bKa7Zx/zyptC/rPBC+X4d2k65tvSOq32IzUSV61zEU3k7a+QrpK4vm5LP9MGppZVYaOOnizB/LP6vmnVqTlN64p3f4tqRWgUk6IHwK2iIh3S9pC0sujaeBERNycP7tORoNdQfpOX0c6SX1JRNTOvelS20mNJcsjd0gPxsqSFLaPPEYaICL+V2nafT+RRj9MkbR6tJ5s1JV8hvFHUmdqp/4WEaE8izKPnhhxSrNgv0M6UCDpUVL744Ar3qlvnZwFFdv6iYj7y81juYa2bkTcW4ppNL0MZmZ6O92O/NkCKH8n/k7qU/iLpKolC+aS+gm2lfQ70tj0ypMNSfeSOo7nk2Zj1w6Fjoirc1NBY/z5jXUHlkE0u80ljWy6IT/+HkkbN5V1MJO2iIglkiZF6lA/S1LVhFDovIO3+F50csKiNALx3xhYA6lMkKSz7kWk68JD6rg9nzQzvbzfZwEfJH0X3i1phqRtmpNHtpjUZ/UC0vHgcUnXRUTd7PVudNOMdZykb5H6vLqep7CyJIW2a4iU3EcaAbGA/qMfhtxU0aX5kr4BbJDP4N5JaqceaacDH4yIKwGUlvP4Jn1LZTSGl3a7dk7RPEb6B1yd1GcyoHks7/8wBv5DdzSuukpEHJfbwX8ceYJgG+cC1+e+FUjJ5Hs5WVf1C/2O9LquJCXUJ0gdfP2aQXLt9Kyq5pEqkg4ktXNfRd8Q4Y9ExAUVsW3bmZs8FRF/Ux52m5sgmtuPm9eSeow01Ptk6msh/6fUP3erpM+Takx1Jzl/KT1mU9L6PZVLonRzwkLfCMR9aD8CEdKyKm/NTaLk5F81Kq2j5JH38a+5nOuQalpnkfqHhmPF2A+STsa2VloWZAqpllXlUFKz52r0NR9VnQhVWlmSQts1RCR9JyLeTqrCn0LqTByx5poKU0jNE0+QDpyfoP/CciNl7UZCAIiIqypqLVVr5wRpyOlXavbbTfPYd0gjafYiHVQPYRg6/yONWX8/fctytIr9tKRLSR2FAt4bEQvz3VU1gIuBx0mv76GK+xv7XSFpD+qHBDf7d0rNDvkM+Gek70qzs2k/XLLsakkfJS1g9xpSP1i/JtZB1D4A3k76f3o/af2uaUDlhD2qO3jrTobanrCUPDsizpB0ZKS5C1crTUis8zel+RSNM++tqV7ErtPkQf6uvZxUW7ifNKy7bj2srnTTjAW8qKqvoZsnWyl+SGcz7ycNSduu4v47SUM0F5POPPr9jEJ5q4ZFVg7v7HE5LgI+ThqpMB34GPCDmthPAOvlvz+eH7tjTeyN5ddJOnOsG756S/n1k85whmtI6sdJHZDThvPzJg9F7TD2RFLyfDlpyYYdW7xvtzXdXqV5W+m+jodLlvb1btKZ7gX578phpuSlpEnj/ReSTrzWr4k9spNtFTFr1O0z39/NsObr8+/LSIMEdgB+02LfryGtX7WcVMu4D9i9Iu6XwFql7/HWje92RexHSP08tUPAB/E9e3Orn5rHfJOKY2CnPytLTYFIC0lVDv/Mvg78hFRVXVja3mpSzrBTaaSSpPIIhnWpGbLZY+8krX55Iem9uIa08mWV/SOta78b6Z/qZNJ6QTuXg/KZ1CVdNI81zngez00GvyclqOHwTtLn2zxjfaifdzeLnzXObMu1hbqmmB9Luow0dBZSzfbSmv12085MpMl236SzZsozSaNtGh3ibyfVSqpqAHNI/Q9l76jYhqSfk68/QRop1GrI6W8lfZy+ZbbfRuq7qXKCpPVJnceNEYhH1e04Ii6XdDNpSXCRkljVDOHjSMeNaZLOITV/vqNmn83Lbg+Hqj6x4impbhLaDZiT+7K6XoNppRiS2g1JX4uI943i869PmpPQzUilXpZnFqn5YTqlMc1VXyDl4ZdKs49vi4hz1eKiIKSlR15L+lJeFhGX15ThXaSktD3pwLMO8ImI+PowvL61qF5uY0idf+rR4meSPkffNREaSXqXiDi6Irbb4ZJVF7r5I+kk6YTof7GkW2PgWjv9tqlvrf/d6N9Msi5p0uaA5lCloaKNZcd3Ib13P4/cHt8UO5l0wlJ+L46P3HfYFDuPdGB/PN/eEDgpmvql1OUqvkozzG8j9YX8ljTprXJ5ibFCQ1yDacIlBetPaUzzh0kHlZZjmpUmXf2O1PfRuCbAjVG9KNdpwNkRcVOPit4RSfNJ/TaNpTkOBjaIiLohoZ3ut+N/vG46hFW9kNzimiR9AKm5ZBrpwkc7k2bF11157fOk5SLOzZsOyr+fAHaLiH1LsdeRRko1lrjelXSQfWkpZktSzXvACQ6pKbBuFd+p9C0kuAdpIcFurkNQtc8BJyc1264s3Swf/BpJ/VVN8a+iL4k9lzSK7JoYhglpndAgri455Od0UpjYJF0bEbu1jyyG5+1NqiXck/+5XxgRP62IvZO0FMT99B/ltX0ppudfeFWsJFm1rZfUwcQjDW4C5OJIwzt3IyWdk0mTCXdujs3xv4imyZGNbZJuayrPTNL6RY1Z648Bc+pqIZ1SWkjwUVJi+jmpD6RyDSmlhe0+zMClvqsWuvwVqU+gPALx6qjpcK2pQVauMKseTUjrhAYxyXSoVpo+BRu0jsc0R8T/UWrDjIhl1E/Yel0Hz90YjRQwYJG64Tpb6Xi5jR7qZOLRYCZANvbxBlKT2MWSjm9RjnUk7RwRNwBI2onUVAcDV4S9izRhb2vS0iZ/JC0FMiApqLvLP55KOhAfTOoMvlppIcjfVMSeT+oL/Fbptdbp5CpmZfNINaRT8+2DSctA9KtBqrcT0trqxUG/HdcUJjhJ3yWNab6D0pjm5rbYHpehuT14MnDycJRBHSy3MdTn6KAMV5Gady6PdHnJXYDPRenaC4Pcb8fNeTl+Fn19NpCaeQ4jDdB4Q5Tmcygt6/44adhocUCOpmuF59gldHn5R/WN5f8w6TKWVUt+LIqIF3exz+3ou4rZFdF0FbOm2I5qkJJOIb23T5FOJq4hXUxpOCakdUzpol9fJnV0B2nBxiOj6eqNw/JcTgoTW3OzwSiVoaP24EHuu7Ltv6HTzrchlqGrDuEu9ttNc175Ajfrk/73H2+x75YXGGqKHdAs1SL2ZFL7fPns++eRlqVoxGyY/zyCNGS0eW2nIQ/IUFrb6etNNcg5UXNdlaYk9pyIGI4JaR1TWmr/XPqPxDokIl4z3M/l5iO7XtJ2rc6qRkA3M9K7MhIH/Q5sTWpOK3cID/n1ddOcF/0vcNPJyqPdDLldKOm/6Ozyj9eT1iXbgr6ZvpuTRvY0NF+XoHx5WRie4eM7A/8sqV8NsjFCq1GDVA8npHVpSqTVlBvOlnRUL57IScGGNKZ5mHTbHjzefDwizs/NYq+mZn7HCOj4Ajek78U7OvxerEe6DsVrS9vqxtBvQFqZeHPSSJ5dSDWGovM4IraC+uHE7V9mRzod7bQWaRLforrRVCPkUUlvo2/+ysGkJUKGnZuPJrihjmkexnJ03B483qiL+R09LkdjOGbjn75yGGaO7cn3Ip+Jv4Q0A3mm0jWYPxkRb62I7clw4vFI0hakWfEvJX1+vyQ1Bz7Q8oGD4JrCBDdGmlc6mZE+nv1OaXb3q4HPKa3fv8oolOOqim2VZ4XdfC+67AT9a0T8VRJK13X+taRtana9TVPH75V56OlE9GlSn0e5ifUk0oz9YTUaX0yzieZA0iSzvXPn7oakdXJG2p9KP0+TmlCmD8N+zyKt4Lkp6RoNP8zbqixVWhDvB8DlSqvS1i0oeEseqQWM2nDisWL7KM3kzp3tPalpuvnIbILKNZYFEbHXEPfTdkmMmse9kjQ57idRcX2TsTCceKzodnLeULj5yGziehbDM5JnUJ2gkZa4bmVIS1+sZEZsMIZrCmYThPoviDeJNF/iUxFRd02MTvc7Yp2gE9lIDcZwUjCbIJpGFD0NPDwcwyzzjPSjmjtBR3JWvA0fNx+ZTRA9HGk2oBNUFddIt/HBo4/MbKhWyRPzgOGdkW4jzx+cmQ3Vyj4jfUJxn4KZDdnKPCN9onFSMDOzgvsUzMys4KRgZmYFJwWzYSZpb0l3S1oi6Zj2jzAbO9ynYDaM8hXO/gd4DbAUuAk42B2vNl64pmA2vHYClkTEb/Mib+cBs0e5TGYdc1IwG16bAQ+Wbi/N28zGBScFs+Glim1uo7Vxw0nBbHgtBaaVbm9O/UVkzMYcJwWz4XUTMEPSVpJWBw4iXZXMbFzw2kdmwyginpb0ftLlNycBZ0bEHaNcLLOOeUiqmZkV3HxkZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrPD/j7kdZ9Oj8GcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sns.barplot(pd.DataFrame(Counter(spam_corpus).most_common(30))[0],pd.DataFrame(Counter(spam_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4a538b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_corpus = []\n",
    "for msg in df[df['target'] == 0]['transformed_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        ham_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a46c7640",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arindham Krishna\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sns.barplot(pd.DataFrame(Counter(ham_corpus).most_common(30))[0],pd.DataFrame(Counter(ham_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a852f22",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "70a87834",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "cv = CountVectorizer()\n",
    "tfidf = TfidfVectorizer(max_features=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "80951331",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5c54c394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 3000)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d15f59f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "66020ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b54ec556",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "47a6daec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score,recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ba92e1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1af1593e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8704061895551257\n",
      "[[788 108]\n",
      " [ 26 112]]\n",
      "0.509090909090909\n",
      "0.8115942028985508\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(X_train,y_train)\n",
    "y_pred1 = gnb.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))\n",
    "print(recall_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2f79a30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.971953578336557\n",
      "[[896   0]\n",
      " [ 29 109]]\n",
      "1.0\n",
      "0.7898550724637681\n"
     ]
    }
   ],
   "source": [
    "mnb.fit(X_train,y_train)\n",
    "y_pred2 = mnb.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))\n",
    "print(recall_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ff0db9a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9835589941972921\n",
      "[[895   1]\n",
      " [ 16 122]]\n",
      "0.991869918699187\n",
      "0.8840579710144928\n"
     ]
    }
   ],
   "source": [
    "\n",
    "bnb.fit(X_train,y_train)\n",
    "y_pred3 = bnb.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))\n",
    "print(recall_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50a13139",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}