sms_spam_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3a3aff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f440716d",
   "metadata": {},
   "outputs": [],
   "source": [
    "file= 'cleaned_sms_spam.csv'\n",
    "df = pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ab1d1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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": [
       "   Unnamed: 0  target                                               text\n",
       "0           0       0  Go until jurong point, crazy.. Available only ...\n",
       "1           1       0                      Ok lar... Joking wif u oni...\n",
       "2           2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3           3       0  U dun say so early hor... U c already then say...\n",
       "4           4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e3c10e",
   "metadata": {},
   "source": [
    "## Steps to perform:\n",
    "####  1. EDA / Exploratory Data Analysis\n",
    "####  2. Text Preprocessing\n",
    "####  3. Model building\n",
    "####  4. Evaluation\n",
    "####  5. Improvement\n",
    "####  6. Website\n",
    "####  7. Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86dfe139",
   "metadata": {},
   "source": [
    "## Step-1. EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de1352b",
   "metadata": {},
   "source": [
    "### •\tLets check how much percent of data is spam and how much is legitimate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "876e78db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Unnamed: 0</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>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  target                                               text\n",
       "0           0       0  Go until jurong point, crazy.. Available only ...\n",
       "1           1       0                      Ok lar... Joking wif u oni...\n",
       "2           2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3           3       0  U dun say so early hor... U c already then say...\n",
       "4           4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "146bd66b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30140bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.pie(df['target'].value_counts(), labels=['Legitimate','SPAM'],autopct=\"%0.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf50d867",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "daba5e59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\mesan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !pip install nltk\n",
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7dce316",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93a7e3f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_characters'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1098e35d",
   "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>Unnamed: 0</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>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>1</td>\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>2</td>\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>3</td>\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>4</td>\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": [
       "   Unnamed: 0  target                                               text  \\\n",
       "0           0       0  Go until jurong point, crazy.. Available only ...   \n",
       "1           1       0                      Ok lar... Joking wif u oni...   \n",
       "2           2       1  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3           3       0  U dun say so early hor... U c already then say...   \n",
       "4           4       0  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "   num_characters  \n",
       "0             111  \n",
       "1              29  \n",
       "2             155  \n",
       "3              49  \n",
       "4              61  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "614e4a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7ce2ccb",
   "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": 14,
   "id": "c71fdf43",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b06ebed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_sentences'] = df['text'].apply(lambda x:len(nltk.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "982d265d",
   "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",
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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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6515f39c",
   "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",
       "    }\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>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>18.455794</td>\n",
       "      <td>1.965564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.236293</td>\n",
       "      <td>13.324758</td>\n",
       "      <td>1.448541</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    18.455794       1.965564\n",
       "std         58.236293    13.324758       1.448541\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         36.000000     9.000000       1.000000\n",
       "50%         60.000000    15.000000       1.000000\n",
       "75%        117.000000    26.000000       2.000000\n",
       "max        910.000000   220.000000      38.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f497cc9",
   "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>17.123782</td>\n",
       "      <td>1.820195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.358207</td>\n",
       "      <td>13.493970</td>\n",
       "      <td>1.383657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters    num_words  num_sentences\n",
       "count     4516.000000  4516.000000    4516.000000\n",
       "mean        70.459256    17.123782       1.820195\n",
       "std         56.358207    13.493970       1.383657\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         34.000000     8.000000       1.000000\n",
       "50%         52.000000    13.000000       1.000000\n",
       "75%         90.000000    22.000000       2.000000\n",
       "max        910.000000   220.000000      38.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legit sms\n",
    "df[df['target'] == 0][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60d756ba",
   "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>2.970904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.137753</td>\n",
       "      <td>7.008418</td>\n",
       "      <td>1.488425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters   num_words  num_sentences\n",
       "count      653.000000  653.000000     653.000000\n",
       "mean       137.891271   27.667688       2.970904\n",
       "std         30.137753    7.008418       1.488425\n",
       "min         13.000000    2.000000       1.000000\n",
       "25%        132.000000   25.000000       2.000000\n",
       "50%        149.000000   29.000000       3.000000\n",
       "75%        157.000000   32.000000       4.000000\n",
       "max        224.000000   46.000000       9.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#spam\n",
    "df[df['target'] == 1][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e5d28a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00012856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_characters', ylabel='Count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 22,
   "id": "ab1da46e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 23,
   "id": "7eb17af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f01bd1ec88>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0c8d5ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ba190a",
   "metadata": {},
   "source": [
    "## Step 2- Preprocessing of Text Data\n",
    "### Lower case\n",
    "### Tokenization: breaked in words\n",
    "### Removing special characters: We only take alphanumeric values as they only give the meaning\n",
    "### Removing stop words and punctuation\n",
    "### Stemming / Lemmitisation : Converting same type of words into one. Like, Dancing,Dance is ovearall giving same meaning that is Dance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac7ea0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\mesan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see Stopwords in English\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d4a9b548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see punctuations\n",
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f53297f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Stemming we will use nltk PrterStemer\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "ps.stem('loving')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6ef7040f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now We will create a function which will perform all the tasks of step 2 \n",
    "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",
    "    \n",
    "    text = y[:]  # Here we used cloning as list is a mutable data type\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": 29,
   "id": "dd845996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gon na home soon want talk stuff anymor tonight k cri enough today'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_text(\"I'm gonna be home soon and i don't want to talk about this stuff anymore tonight, k? I've cried enough today.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8c6a3c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transformed_text'] = df['text'].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ea275ea",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "085bd937",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94afd804",
   "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": 34,
   "id": "d954fe99",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_wc = wc.generate(df[df['target'] == 1]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "151b8bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f01ca96d08>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "45b2ce6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Most used words in Spam SMS\n",
    "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": 37,
   "id": "174639b0",
   "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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>claim</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>prize</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>get</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>new</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>servic</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tone</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>send</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>urgent</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>nokia</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>contact</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>award</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>phone</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cash</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pleas</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>week</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>win</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>c</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>collect</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>min</td>\n",
       "      <td>45</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\n",
       "10    claim   98\n",
       "11        4   97\n",
       "12    prize   82\n",
       "13      get   74\n",
       "14      new   64\n",
       "15   servic   64\n",
       "16     tone   63\n",
       "17     send   60\n",
       "18   urgent   57\n",
       "19    nokia   57\n",
       "20  contact   56\n",
       "21    award   55\n",
       "22    phone   52\n",
       "23     cash   51\n",
       "24    pleas   51\n",
       "25     week   49\n",
       "26      win   48\n",
       "27        c   45\n",
       "28  collect   45\n",
       "29      min   45"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 30 words\n",
    "from collections import Counter\n",
    "pd.DataFrame(Counter(spam_corpus).most_common(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f96383b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=(pd.DataFrame(Counter(spam_corpus).most_common(30))[0]), y= (pd.DataFrame(Counter(spam_corpus).most_common(30))[1]))\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e24b460d",
   "metadata": {},
   "outputs": [],
   "source": [
    "legit_wc = wc.generate(df[df['target'] == 0]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8e00f310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f01d019948>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(legit_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "31b56240",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Most used words in Ligitimate SMS\n",
    "legit_corpus = []\n",
    "for msg in df[df['target'] == 0]['transformed_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        legit_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fc578917",
   "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>u</td>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>go</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>get</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gt</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lt</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>come</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>got</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>know</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>like</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>call</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>time</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ok</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>love</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>good</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>want</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ur</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>day</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>need</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>one</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>lor</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>home</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>think</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>see</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>take</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>still</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>da</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>tell</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>make</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1\n",
       "0       u  883\n",
       "1      go  404\n",
       "2     get  349\n",
       "3      gt  288\n",
       "4      lt  287\n",
       "5       2  284\n",
       "6    come  275\n",
       "7     got  236\n",
       "8    know  236\n",
       "9    like  234\n",
       "10   call  233\n",
       "11   time  219\n",
       "12     ok  217\n",
       "13   love  216\n",
       "14   good  213\n",
       "15   want  208\n",
       "16     ur  197\n",
       "17    day  190\n",
       "18   need  170\n",
       "19    one  165\n",
       "20    lor  159\n",
       "21      4  156\n",
       "22   home  152\n",
       "23  think  149\n",
       "24    see  147\n",
       "25   take  143\n",
       "26  still  143\n",
       "27     da  142\n",
       "28   tell  133\n",
       "29   make  129"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 30 words\n",
    "from collections import Counter\n",
    "pd.DataFrame(Counter(legit_corpus).most_common(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e3bed665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=(pd.DataFrame(Counter(legit_corpus).most_common(30))[0]), y= (pd.DataFrame(Counter(legit_corpus).most_common(30))[1]))\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4824dd84",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883d6edb",
   "metadata": {},
   "source": [
    "### Vectorization of Input file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "70ff27a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bagofwords/ count Vectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fc0af197",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To transform sparse array into a dense array\n",
    "X = cv.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bd50d1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 6708)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5570e803",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "119c66f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "13f2dcc8",
   "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": 50,
   "id": "9a943af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we don't know the data distribution type so we will use all type of Naive Bayse\n",
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
    "\n",
    "# Spam detection is a high precision model so we will check some metrics of our model to select the best\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c2181fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ca4df124",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8800773694390716\n",
      "[[792 104]\n",
      " [ 20 118]]\n",
      "0.5315315315315315\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": 53,
   "id": "cb7b509b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9642166344294004\n",
      "[[871  25]\n",
      " [ 12 126]]\n",
      "0.8344370860927153\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": 54,
   "id": "e2bd07b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9700193423597679\n",
      "[[893   3]\n",
      " [ 28 110]]\n",
      "0.9734513274336283\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": "markdown",
   "id": "210bbd23",
   "metadata": {},
   "source": [
    "### So, we can see that here out of three Naive Bayse models, Bernoulli Naive Bayse performs well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f877da",
   "metadata": {},
   "source": [
    "### Again we can train our model after using another vectorizer called Tfidf and see if there any improvements in model performence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0090a29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c2af4364",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a5f42089",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new X value with new vectorizer but other steps will be same like above\n",
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5fbb0ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 6708)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "05591acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "921d28eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2b7266cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score\n",
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ec78a021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8762088974854932\n",
      "[[793 103]\n",
      " [ 25 113]]\n",
      "0.5231481481481481\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": 63,
   "id": "6ac57473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9593810444874274\n",
      "[[896   0]\n",
      " [ 42  96]]\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": 64,
   "id": "ba5ddfd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9700193423597679\n",
      "[[893   3]\n",
      " [ 28 110]]\n",
      "0.9734513274336283\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": "markdown",
   "id": "9869a429",
   "metadata": {},
   "source": [
    "### As we kow from our EDA step that the data is imbalanced so here Precision score is more important than Accurecy Score.\n",
    "### So, we will go with Tfidf vectorizer and Multinomial Naive Bayes model as it gives 100% precision with good accurecy also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f4cf7167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using all other classification algorithms\n",
    "\n",
    "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\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "274dd40c",
   "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)\n",
    "xgb = XGBClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d6c25d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of all agorithm\n",
    "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",
    "    'xgb':xgb\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4568c814",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function which will calculate accurecy and precision as per algorithm\n",
    "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": 69,
   "id": "ba7ca953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9729206963249516, 0.9741379310344828)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_classifier(svc,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "256c05fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For  SVC\n",
      "Accuracy -  0.9729206963249516\n",
      "Precision -  0.9741379310344828\n",
      "For  KN\n",
      "Accuracy -  0.9003868471953579\n",
      "Precision -  1.0\n",
      "For  NB\n",
      "Accuracy -  0.9593810444874274\n",
      "Precision -  1.0\n",
      "For  DT\n",
      "Accuracy -  0.9352030947775629\n",
      "Precision -  0.8380952380952381\n",
      "For  LR\n",
      "Accuracy -  0.9516441005802708\n",
      "Precision -  0.94\n",
      "For  RF\n",
      "Accuracy -  0.971953578336557\n",
      "Precision -  1.0\n",
      "For  AdaBoost\n",
      "Accuracy -  0.9613152804642167\n",
      "Precision -  0.9454545454545454\n",
      "For  BgC\n",
      "Accuracy -  0.9584139264990329\n",
      "Precision -  0.8625954198473282\n",
      "For  ETC\n",
      "Accuracy -  0.9729206963249516\n",
      "Precision -  0.9824561403508771\n",
      "For  GBDT\n",
      "Accuracy -  0.9526112185686654\n",
      "Precision -  0.9238095238095239\n",
      "For  xgb\n",
      "Accuracy -  0.9700193423597679\n",
      "Precision -  0.9349593495934959\n"
     ]
    }
   ],
   "source": [
    "# Lets create a loop that will run for each algorithm and will calculate accurecy and precision\n",
    "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": 71,
   "id": "0d514a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving all the above values in a DataFrame\n",
    "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Precision',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0f2c5c63",
   "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>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.982456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.934959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision\n",
       "1         KN  0.900387   1.000000\n",
       "2         NB  0.959381   1.000000\n",
       "5         RF  0.971954   1.000000\n",
       "8        ETC  0.972921   0.982456\n",
       "0        SVC  0.972921   0.974138\n",
       "6   AdaBoost  0.961315   0.945455\n",
       "4         LR  0.951644   0.940000\n",
       "10       xgb  0.970019   0.934959\n",
       "9       GBDT  0.952611   0.923810\n",
       "7        BgC  0.958414   0.862595\n",
       "3         DT  0.935203   0.838095"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3267739f",
   "metadata": {},
   "source": [
    "## Till now, we can see that Naive Bayes is the best model with respect to Precision and Accurecy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f4c076cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets make another dataframe to plot the performences of each model\n",
    "performance_df1 = pd.melt(performance_df, id_vars = \"Algorithm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ce2a4be1",
   "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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.900387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.959381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.971954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.972921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.972921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.961315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.951644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.970019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.952611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.958414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.935203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KN</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NB</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF</td>\n",
       "      <td>Precision</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.982456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SVC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.974138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LR</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>xgb</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.934959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.923810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BgC</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.862595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DT</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.838095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm   variable     value\n",
       "0         KN   Accuracy  0.900387\n",
       "1         NB   Accuracy  0.959381\n",
       "2         RF   Accuracy  0.971954\n",
       "3        ETC   Accuracy  0.972921\n",
       "4        SVC   Accuracy  0.972921\n",
       "5   AdaBoost   Accuracy  0.961315\n",
       "6         LR   Accuracy  0.951644\n",
       "7        xgb   Accuracy  0.970019\n",
       "8       GBDT   Accuracy  0.952611\n",
       "9        BgC   Accuracy  0.958414\n",
       "10        DT   Accuracy  0.935203\n",
       "11        KN  Precision  1.000000\n",
       "12        NB  Precision  1.000000\n",
       "13        RF  Precision  1.000000\n",
       "14       ETC  Precision  0.982456\n",
       "15       SVC  Precision  0.974138\n",
       "16  AdaBoost  Precision  0.945455\n",
       "17        LR  Precision  0.940000\n",
       "18       xgb  Precision  0.934959\n",
       "19      GBDT  Precision  0.923810\n",
       "20       BgC  Precision  0.862595\n",
       "21        DT  Precision  0.838095"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e5f4f10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 613.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Algorithm', y='value', hue = 'variable',data=performance_df1, kind='bar',height=5)\n",
    "plt.ylim(0.5,1.0)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9436016f",
   "metadata": {},
   "source": [
    "### Model Improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f0765b",
   "metadata": {},
   "source": [
    "### Step-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "550be526",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the max_features parameter of TfIdf vectorizer by that we only take a specified amount of special words. Not all.\n",
    "# we have taken only 3000 features \n",
    "\n",
    "tfidf = TfidfVectorizer(max_features=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3e53b615",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9f420633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 3000)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "277be2ce",
   "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": 80,
   "id": "2c406c04",
   "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": 81,
   "id": "f6b47536",
   "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 -  0.9323017408123792\n",
      "Precision -  0.8333333333333334\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",
      "For  xgb\n",
      "Accuracy -  0.9671179883945842\n",
      "Precision -  0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Now lets check accurecy and precision for all algorithm\n",
    "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": 82,
   "id": "fb2bef6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_max_ft_3000':accuracy_scores,'Precision_max_ft_3000':precision_scores}).sort_values('Precision_max_ft_3000',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e5a77baa",
   "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_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</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>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.933333</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>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy_max_ft_3000  Precision_max_ft_3000\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",
       "10       xgb              0.967118               0.933333\n",
       "6   AdaBoost              0.960348               0.929204\n",
       "9       GBDT              0.946809               0.919192\n",
       "7        BgC              0.958414               0.868217\n",
       "3         DT              0.932302               0.833333"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "13b9fffb",
   "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",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.919192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.868217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  Precision_max_ft_3000\n",
       "0         KN  0.900387   1.000000              0.905222               1.000000\n",
       "1         NB  0.959381   1.000000              0.970986               1.000000\n",
       "2         RF  0.971954   1.000000              0.975822               0.982906\n",
       "3        ETC  0.972921   0.982456              0.974855               0.974576\n",
       "4        SVC  0.972921   0.974138              0.975822               0.974790\n",
       "5   AdaBoost  0.961315   0.945455              0.960348               0.929204\n",
       "6         LR  0.951644   0.940000              0.958414               0.970297\n",
       "7        xgb  0.970019   0.934959              0.967118               0.933333\n",
       "8       GBDT  0.952611   0.923810              0.946809               0.919192\n",
       "9        BgC  0.958414   0.862595              0.958414               0.868217\n",
       "10        DT  0.935203   0.838095              0.932302               0.833333"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df= performance_df.merge(temp_df,on='Algorithm')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2553a2a7",
   "metadata": {},
   "source": [
    "### So we can see that our Naive Bayes model's Accuracy is increased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba773e62",
   "metadata": {},
   "source": [
    "### Step-2\n",
    "#### Again we can check that if scaling of data with Tfidf vectorizer, improves our accuracy or not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "acc62cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "58263b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we use MinMaxScaler in place of StandardScaler \n",
    "# As StandardScaler gives negative values which will be not accepted by Naive Bayse\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "17a188cd",
   "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": 88,
   "id": "52753eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For  SVC\n",
      "Accuracy -  0.9690522243713733\n",
      "Precision -  0.9274193548387096\n",
      "For  KN\n",
      "Accuracy -  0.9052224371373307\n",
      "Precision -  0.9761904761904762\n",
      "For  NB\n",
      "Accuracy -  0.9787234042553191\n",
      "Precision -  0.9461538461538461\n",
      "For  DT\n",
      "Accuracy -  0.9303675048355899\n",
      "Precision -  0.8173076923076923\n",
      "For  LR\n",
      "Accuracy -  0.9671179883945842\n",
      "Precision -  0.9642857142857143\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",
      "For  xgb\n",
      "Accuracy -  0.9671179883945842\n",
      "Precision -  0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Again check accurecy and precision for all algorithm\n",
    "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": 89,
   "id": "3858592e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Algorithm</th>\n",
       "      <th>Scaled_Accuracy_max_ft_3000</th>\n",
       "      <th>Scaled_Precision_max_ft_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>0.976190</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>0.967118</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.946154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.933333</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>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.927419</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>0.930368</td>\n",
       "      <td>0.817308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Scaled_Accuracy_max_ft_3000  Scaled_Precision_max_ft_3000\n",
       "5         RF                     0.975822                      0.982906\n",
       "1         KN                     0.905222                      0.976190\n",
       "8        ETC                     0.974855                      0.974576\n",
       "4         LR                     0.967118                      0.964286\n",
       "2         NB                     0.978723                      0.946154\n",
       "10       xgb                     0.967118                      0.933333\n",
       "6   AdaBoost                     0.960348                      0.929204\n",
       "0        SVC                     0.969052                      0.927419\n",
       "9       GBDT                     0.946809                      0.919192\n",
       "7        BgC                     0.958414                      0.868217\n",
       "3         DT                     0.930368                      0.817308"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df = pd.DataFrame({'Algorithm':clfs.keys(),'Scaled_Accuracy_max_ft_3000':accuracy_scores,'Scaled_Precision_max_ft_3000':precision_scores}).sort_values('Scaled_Precision_max_ft_3000',ascending=False)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "544b01ec",
   "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",
       "      <th>Accuracy_max_ft_3000</th>\n",
       "      <th>Precision_max_ft_3000</th>\n",
       "      <th>Scaled_Accuracy_max_ft_3000</th>\n",
       "      <th>Scaled_Precision_max_ft_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.900387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>0.976190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.946154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.971954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972921</td>\n",
       "      <td>0.974138</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.969052</td>\n",
       "      <td>0.927419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.952611</td>\n",
       "      <td>0.923810</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.919192</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.919192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.862595</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.868217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.935203</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.932302</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.930368</td>\n",
       "      <td>0.817308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision  Accuracy_max_ft_3000  \\\n",
       "0         KN  0.900387   1.000000              0.905222   \n",
       "1         NB  0.959381   1.000000              0.970986   \n",
       "2         RF  0.971954   1.000000              0.975822   \n",
       "3        ETC  0.972921   0.982456              0.974855   \n",
       "4        SVC  0.972921   0.974138              0.975822   \n",
       "5   AdaBoost  0.961315   0.945455              0.960348   \n",
       "6         LR  0.951644   0.940000              0.958414   \n",
       "7        xgb  0.970019   0.934959              0.967118   \n",
       "8       GBDT  0.952611   0.923810              0.946809   \n",
       "9        BgC  0.958414   0.862595              0.958414   \n",
       "10        DT  0.935203   0.838095              0.932302   \n",
       "\n",
       "    Precision_max_ft_3000  Scaled_Accuracy_max_ft_3000  \\\n",
       "0                1.000000                     0.905222   \n",
       "1                1.000000                     0.978723   \n",
       "2                0.982906                     0.975822   \n",
       "3                0.974576                     0.974855   \n",
       "4                0.974790                     0.969052   \n",
       "5                0.929204                     0.960348   \n",
       "6                0.970297                     0.967118   \n",
       "7                0.933333                     0.967118   \n",
       "8                0.919192                     0.946809   \n",
       "9                0.868217                     0.958414   \n",
       "10               0.833333                     0.930368   \n",
       "\n",
       "    Scaled_Precision_max_ft_3000  \n",
       "0                       0.976190  \n",
       "1                       0.946154  \n",
       "2                       0.982906  \n",
       "3                       0.974576  \n",
       "4                       0.927419  \n",
       "5                       0.929204  \n",
       "6                       0.964286  \n",
       "7                       0.933333  \n",
       "8                       0.919192  \n",
       "9                       0.868217  \n",
       "10                      0.817308  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_scaled = new_df.merge(scaled_df,on='Algorithm')\n",
    "new_df_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb581a8",
   "metadata": {},
   "source": [
    "### So, as our focus is on Naive Bayes , we can see that after scaling Accuracy improved but Precision decresed. So no need to scale the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f596919",
   "metadata": {},
   "source": [
    "### Step-3\n",
    "#### We will create a Voting Classifier and check if it can give better accuracy and precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4405ba3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(max_features=3000)\n",
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c3b43bf4",
   "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": 93,
   "id": "9e023c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Voting Classifier is a collection of best performing algorithms with same weitage\n",
    "# Here all the algorithms work together and sometime gives better result\n",
    "\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": 94,
   "id": "103cfdc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('et', etc)],voting='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b0aaae09",
   "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": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a47e46b7",
   "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": "markdown",
   "id": "968ff845",
   "metadata": {},
   "source": [
    "#### Here we can see that the result can't outperform the result of Step 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efaa66d",
   "metadata": {},
   "source": [
    "### Step-4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a2e8fd2",
   "metadata": {},
   "source": [
    "#### Lets try Staking, which is simmiler to Voting CLassifier but here weigtage is given by a final estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b263e352",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying stacking\n",
    "estimators=[('svm', svc), ('nb', mnb), ('et', etc)]\n",
    "final_estimator=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "bdfff753",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2870cdb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b24b75b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9806576402321083\n",
      "Precision 0.946969696969697\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
    "print(\"Precision\",precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40456b80",
   "metadata": {},
   "source": [
    "### Here we can see that the result can't outperform the result of Step 1, so upto now we have the best model and that is Mulinomial Naibe Bayes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b9f7b3",
   "metadata": {},
   "source": [
    "### Now we will create a pipeline which will perform all the tasks to detect Spam SMS. Tasks are:\n",
    "#### 1. After receiving the SMS, pre-process it by our function \"transform_text\". So,we need the Function \"transform_text\"\n",
    "#### 2. Vectorize the text by Vectorizer Tfidf. So we need the vectorizer file\n",
    "#### 3. Apply the best Model. So we need the model file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "fbea38fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As Mulinomial Naibe Bayes is the best algorithm so we will fit the data with it again, by that we can save the model\n",
    "\n",
    "tfidf = TfidfVectorizer(max_features=3000)\n",
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "333bc151",
   "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": 103,
   "id": "3c597950",
   "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": 106,
   "id": "f5a30ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the Vectorizer and model file by pickle\n",
    "import pickle\n",
    "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
    "pickle.dump(mnb,open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "80609bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from platform import python_version\n",
    "# print(python_version())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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