spam_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb84fd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5022aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('spam.csv',encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84e70d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>834</th>\n",
       "      <td>ham</td>\n",
       "      <td>Surely result will offer:)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>spam</td>\n",
       "      <td>Text &amp; meet someone sexy today. U can find a d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>spam</td>\n",
       "      <td>Ur balance is now å£500. Ur next question is: ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>ham</td>\n",
       "      <td>Yunny i'm walking in citylink now Ì_ faster co...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5190</th>\n",
       "      <td>spam</td>\n",
       "      <td>Our records indicate u maybe entitled to 5000 ...</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",
       "834    ham                         Surely result will offer:)        NaN   \n",
       "234   spam  Text & meet someone sexy today. U can find a d...        NaN   \n",
       "1327  spam  Ur balance is now å£500. Ur next question is: ...        NaN   \n",
       "2510   ham  Yunny i'm walking in citylink now Ì_ faster co...        NaN   \n",
       "5190  spam  Our records indicate u maybe entitled to 5000 ...        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "834         NaN        NaN  \n",
       "234         NaN        NaN  \n",
       "1327        NaN        NaN  \n",
       "2510        NaN        NaN  \n",
       "5190        NaN        NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "363a37e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "707d2c92",
   "metadata": {},
   "source": [
    "###### Steps "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ce08447",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Data cleaning\n",
    "# 2. EDA\n",
    "# 3. Text Preprocessing\n",
    "# 4. Model building\n",
    "# 5. Evaluation\n",
    "# 6. Improvement\n",
    "# 7. Website\n",
    "# 8. Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d608385",
   "metadata": {},
   "source": [
    "#### 1. Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e3f27cb",
   "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": "code",
   "execution_count": 7,
   "id": "4ac7e209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop last 3 cols\n",
    "df.drop(columns=['Unnamed: 2','Unnamed: 3','Unnamed: 4'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5dddcbb",
   "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>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     v1                                                 v2\n",
       "0   ham  Go until jurong point, crazy.. Available only ...\n",
       "1   ham                      Ok lar... Joking wif u oni...\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3   ham  U dun say so early hor... U c already then say...\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16099a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  target                                               text\n",
       "0    ham  Go until jurong point, crazy.. Available only ...\n",
       "1    ham                      Ok lar... Joking wif u oni...\n",
       "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3    ham  U dun say so early hor... U c already then say...\n",
       "4    ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming the cols\n",
    "df.rename(columns={'v1':'target','v2':'text'},inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c98b45a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55e76e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = encoder.fit_transform(df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9e0faa5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>target</th>\n",
       "      <th>text</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",
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       "    <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": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67f7f24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d308b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicate values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "12f447aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove duplicates\n",
    "df = df.drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "021d4462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicate values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a78e08f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbd9ccd",
   "metadata": {},
   "source": [
    "##### # 2. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "089ffde1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b96ae302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "31da98ac",
   "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": 21,
   "id": "31dbf8c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "812b6259",
   "metadata": {},
   "outputs": [],
   "source": [
    "#natural tool_kit\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1cf7e57b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\nabar\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5089f1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_characters'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9248bb13",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b508320",
   "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": 27,
   "id": "e3735536",
   "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",
       "    </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",
       "      <td>13</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",
       "    </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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bab64c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_sentences'] = df['text'].apply(lambda x:len(nltk.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "928b454b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\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>target</th>\n",
       "      <th>text</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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9da78368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\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>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.977945</td>\n",
       "      <td>18.455407</td>\n",
       "      <td>1.961308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.236293</td>\n",
       "      <td>13.322448</td>\n",
       "      <td>1.432583</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        78.977945    18.455407       1.961308\n",
       "std         58.236293    13.322448       1.432583\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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fe3996f9",
   "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>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "      <td>4516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.459256</td>\n",
       "      <td>17.123339</td>\n",
       "      <td>1.815545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.358207</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        70.459256    17.123339       1.815545\n",
       "std         56.358207    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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ham\n",
    "df[df['target'] == 0][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "86c90db5",
   "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>137.891271</td>\n",
       "      <td>27.667688</td>\n",
       "      <td>2.969372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.137753</td>\n",
       "      <td>7.008418</td>\n",
       "      <td>1.488910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters   num_words  num_sentences\n",
       "count      653.000000  653.000000     653.000000\n",
       "mean       137.891271   27.667688       2.969372\n",
       "std         30.137753    7.008418       1.488910\n",
       "min         13.000000    2.000000       1.000000\n",
       "25%        132.000000   25.000000       2.000000\n",
       "50%        149.000000   29.000000       3.000000\n",
       "75%        157.000000   32.000000       4.000000\n",
       "max        224.000000   46.000000       9.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#spam\n",
    "df[df['target'] == 1][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f74daf13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "615a40d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_characters', ylabel='Count'>"
      ]
     },
     "execution_count": 34,
     "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'])#ham\n",
    "sns.histplot(df[df['target'] == 1]['num_characters'],color='red')#spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2cc4d264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2309e6ff880>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 582.375x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0e81dac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1302663b",
   "metadata": {},
   "source": [
    "### 3. Data Preprocessing\n",
    "- Lower case\n",
    "- Tokenization\n",
    "- Removing special characters\n",
    "- Removing stop words and punctuation\n",
    "- Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "50cc105b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps=PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eab6c6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_text(text):\n",
    "    #Lower case\n",
    "    text = text.lower()\n",
    "    #Tokenization\n",
    "    text = nltk.word_tokenize(text)\n",
    "    #Removing special characters\n",
    "    y = []\n",
    "    for i in text:\n",
    "        if i.isalnum():\n",
    "            y.append(i)\n",
    "    \n",
    "    text = y.copy()\n",
    "    y.clear()\n",
    "    #Removing stop words and punctuation\n",
    "    for i in text:\n",
    "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
    "            y.append(i)\n",
    "    #Stemming\n",
    "    text = y.copy()\n",
    "    y.clear()\n",
    "    \n",
    "    for i in text:\n",
    "        y.append(PorterStemmer().stem(i))\n",
    "        \n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70b1beab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transformed_text'] = df['text'].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9cf89c94",
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49f8177",
   "metadata": {},
   "source": [
    "#### Word Cloud\n",
    "- an image composed of words used in a particular text or subject, in which the size of each word indicates its frequency or importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "612d1539",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4ecc6b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "wc = WordCloud(width=500,height=500,min_font_size=10,background_color='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bebc65db",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_wc = wc.generate(df[df['target'] == 1]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f65685c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x230a1c76220>"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "id": "c0623e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "hpam_wc = wc.generate(df[df['target'] == 0]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "db3d9b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x230a1ee0f40>"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "id": "317f331f",
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f5b9dd2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9939"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8572f247",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\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": [
    "#counter will give the frequency of each word\n",
    "#we plot the barplot of the top 30 occuring words\n",
    "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": 71,
   "id": "8b392f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35402"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "len(ham_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8eb077f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\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": [
    "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": "afc66f51",
   "metadata": {},
   "source": [
    "### 4.Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c150e88f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "b8241d64",
   "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": 152,
   "id": "58fc6f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "52af515f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 3000)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "0a1f66a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c2aba31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "0c18bda4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "7e9b7773",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8d73ca7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "268f840c",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d03668bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8694390715667312\n",
      "[[788 108]\n",
      " [ 27 111]]\n",
      "0.5068493150684932\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c9000796",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9709864603481625\n",
      "[[896   0]\n",
      " [ 30 108]]\n",
      "1.0\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0b756499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9835589941972921\n",
      "[[895   1]\n",
      " [ 16 122]]\n",
      "0.991869918699187\n"
     ]
    }
   ],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ee1150c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tfidf --> MNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "bef31935",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "64d97e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1a8bcea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : svc,\n",
    "    'KN' : knc, \n",
    "    'NB': mnb, \n",
    "    'DT': dtc, \n",
    "    'LR': lrc, \n",
    "    'RF': rfc, \n",
    "    'AdaBoost': abc, \n",
    "    'BgC': bc, \n",
    "    'ETC': etc,\n",
    "    'GBDT':gbdt,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f3947747",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    precision = precision_score(y_test,y_pred)\n",
    "    \n",
    "    return accuracy,precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "62425b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For  SVC\n",
      "Accuracy -  0.9758220502901354\n",
      "Precision -  0.9747899159663865\n",
      "For  KN\n",
      "Accuracy -  0.9052224371373307\n",
      "Precision -  1.0\n",
      "For  NB\n",
      "Accuracy -  0.9709864603481625\n",
      "Precision -  1.0\n",
      "For  DT\n",
      "Accuracy -  0.9294003868471954\n",
      "Precision -  0.8282828282828283\n",
      "For  LR\n",
      "Accuracy -  0.9584139264990329\n",
      "Precision -  0.9702970297029703\n",
      "For  RF\n",
      "Accuracy -  0.9758220502901354\n",
      "Precision -  0.9829059829059829\n",
      "For  AdaBoost\n",
      "Accuracy -  0.960348162475822\n",
      "Precision -  0.9292035398230089\n",
      "For  BgC\n",
      "Accuracy -  0.9584139264990329\n",
      "Precision -  0.8682170542635659\n",
      "For  ETC\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9745762711864406\n",
      "For  GBDT\n",
      "Accuracy -  0.9468085106382979\n",
      "Precision -  0.9191919191919192\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, X_train,y_train,X_test,y_test)\n",
    "    \n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "88279bd9",
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.919192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.868217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.929400</td>\n",
       "      <td>0.828283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Algorithm  Accuracy  Precision\n",
       "1        KN  0.905222   1.000000\n",
       "2        NB  0.970986   1.000000\n",
       "5        RF  0.975822   0.982906\n",
       "0       SVC  0.975822   0.974790\n",
       "8       ETC  0.974855   0.974576\n",
       "4        LR  0.958414   0.970297\n",
       "6  AdaBoost  0.960348   0.929204\n",
       "9      GBDT  0.946809   0.919192\n",
       "7       BgC  0.958414   0.868217\n",
       "3        DT  0.929400   0.828283"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Precision',ascending=False)\n",
    "performance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "fde6e1f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Voting Classifier\n",
    "svc = SVC(kernel='sigmoid', gamma=1.0,probability=True)\n",
    "mnb = MultinomialNB()\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "937c77d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('et', etc)],voting='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "cb5ec513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svm',\n",
       "                              SVC(gamma=1.0, kernel='sigmoid',\n",
       "                                  probability=True)),\n",
       "                             ('nb', MultinomialNB()),\n",
       "                             ('et',\n",
       "                              ExtraTreesClassifier(n_estimators=50,\n",
       "                                                   random_state=2))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "dbf5c855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9816247582205029\n",
      "Precision 0.9917355371900827\n"
     ]
    }
   ],
   "source": [
    "y_pred = voting.predict(X_test)\n",
    "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
    "print(\"Precision\",precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "a920449c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
    "pickle.dump(mnb,open('model.pkl','wb'))"
   ]
  }
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