Spam-detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e4a60599",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "76f40660",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"spam.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d3414f30",
   "metadata": {},
   "outputs": [
    {
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       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>ham</td>\n",
       "      <td>I dont want to hear philosophy. Just say what ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>ham</td>\n",
       "      <td>Yup i thk they r e teacher said that will make...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4506</th>\n",
       "      <td>ham</td>\n",
       "      <td>He neva grumble but i sad lor... Hee... Buy tm...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4643</th>\n",
       "      <td>ham</td>\n",
       "      <td>Did u got that persons story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4699</th>\n",
       "      <td>spam</td>\n",
       "      <td>WIN: We have a winner! Mr. T. Foley won an iPo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        v1                                                 v2 Unnamed: 2  \\\n",
       "1848   ham  I dont want to hear philosophy. Just say what ...        NaN   \n",
       "627    ham  Yup i thk they r e teacher said that will make...        NaN   \n",
       "4506   ham  He neva grumble but i sad lor... Hee... Buy tm...        NaN   \n",
       "4643   ham                       Did u got that persons story        NaN   \n",
       "4699  spam  WIN: We have a winner! Mr. T. Foley won an iPo...        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "1848        NaN        NaN  \n",
       "627         NaN        NaN  \n",
       "4506        NaN        NaN  \n",
       "4643        NaN        NaN  \n",
       "4699        NaN        NaN  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a56af8c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc546d2e",
   "metadata": {},
   "source": [
    "## Steps:\n",
    "1. Data Cleaning\n",
    "2. EDA - Exploratory Data Analysis\n",
    "3. Text Preprocessing - vectorization, stepping, remove stopwords\n",
    "4. Model Building\n",
    "5. Evaluation\n",
    "6. Improvements (depending on the evaluation)\n",
    "7. Website\n",
    "8. Deploy on Heroku"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1285694",
   "metadata": {},
   "source": [
    "## 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64b0cce",
   "metadata": {},
   "source": [
    "Check if there are any columns that we can remove."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e91c362e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   v1          5572 non-null   object\n",
      " 1   v2          5572 non-null   object\n",
      " 2   Unnamed: 2  50 non-null     object\n",
      " 3   Unnamed: 3  12 non-null     object\n",
      " 4   Unnamed: 4  6 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 217.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "56145bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# By the above observation, we will drop last three columns\n",
    "df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "64f17675",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4575</th>\n",
       "      <td>spam</td>\n",
       "      <td>Congratulations ur awarded 500 of CD vouchers ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>ham</td>\n",
       "      <td>Beautiful Truth against Gravity.. Read careful...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3529</th>\n",
       "      <td>ham</td>\n",
       "      <td>Tyler (getting an 8th) has to leave not long a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5146</th>\n",
       "      <td>ham</td>\n",
       "      <td>Oh unintentionally not bad timing. Great. Fing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3400</th>\n",
       "      <td>ham</td>\n",
       "      <td>Thanks honey. Have a great day.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2\n",
       "4575  spam  Congratulations ur awarded 500 of CD vouchers ...\n",
       "3406   ham  Beautiful Truth against Gravity.. Read careful...\n",
       "3529   ham  Tyler (getting an 8th) has to leave not long a...\n",
       "5146   ham  Oh unintentionally not bad timing. Great. Fing...\n",
       "3400   ham                    Thanks honey. Have a great day."
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "680027a0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4495</th>\n",
       "      <td>ham</td>\n",
       "      <td>In case you wake up wondering where I am, I fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>ham</td>\n",
       "      <td>Are you in castor? You need to see something</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>ham</td>\n",
       "      <td>U guys never invite me anywhere :(</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4873</th>\n",
       "      <td>ham</td>\n",
       "      <td>Wat happened to the cruise thing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5023</th>\n",
       "      <td>ham</td>\n",
       "      <td>Where you. What happen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     target                                               text\n",
       "4495    ham  In case you wake up wondering where I am, I fo...\n",
       "876     ham       Are you in castor? You need to see something\n",
       "1739    ham                 U guys never invite me anywhere :(\n",
       "4873    ham                   Wat happened to the cruise thing\n",
       "5023    ham                             Where you. What happen"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming the columns to something that is more descriptive to their role in the data\n",
    "df.rename(columns={'v1':'target', 'v2':'text'}, inplace = True)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7db7dbb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode 0 to ham and 1 to spam in the target column\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder() #object of label encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "23e3eace",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = encoder.fit_transform(df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "58e18fd9",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   target                                               text\n",
       "0       0  Go until jurong point, crazy.. Available only ...\n",
       "1       0                      Ok lar... Joking wif u oni...\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       0  U dun say so early hor... U c already then say...\n",
       "4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
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     "execution_count": 68,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "00c0c4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0\n",
       "text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0aae8572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicate values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fba0fbc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove duplicates\n",
    "df = df.drop_duplicates(keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b306ec95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
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     "execution_count": 72,
     "metadata": {},
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   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "96242477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 2)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2553007f",
   "metadata": {},
   "source": [
    "## 2. EDA\n",
    "- EDA Stands for Exploratory data analysis..\n",
    "- This is done to get the basic understanding of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ba83ef4b",
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </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",
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      "text/plain": [
       "   target                                               text\n",
       "0       0  Go until jurong point, crazy.. Available only ...\n",
       "1       0                      Ok lar... Joking wif u oni...\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       0  U dun say so early hor... U c already then say...\n",
       "4       0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3317e0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b381fc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To represent it better, we will use pie chart.\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(df['target'].value_counts(), labels=['ham', 'spam'], autopct=\"%0.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9c866b",
   "metadata": {},
   "source": [
    "By the above representation, we can clearly see that data is imbalanced"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01aa25d5",
   "metadata": {},
   "source": [
    "### Now, for deeper understanding, we will make 3 new columns:\n",
    "1. No. of characters in the SMS\n",
    "2. No. of words in the SMS\n",
    "3. No. of sentences in the SMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "17ab33f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in d:\\software\\anaconda\\lib\\site-packages (3.6.1)\n",
      "Requirement already satisfied: tqdm in d:\\software\\anaconda\\lib\\site-packages (from nltk) (4.59.0)\n",
      "Requirement already satisfied: regex in d:\\software\\anaconda\\lib\\site-packages (from nltk) (2021.4.4)\n",
      "Requirement already satisfied: click in d:\\software\\anaconda\\lib\\site-packages (from nltk) (7.1.2)\n",
      "Requirement already satisfied: joblib in d:\\software\\anaconda\\lib\\site-packages (from nltk) (1.0.1)\n"
     ]
    }
   ],
   "source": [
    "#nltk - natural language toolkit\n",
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bb5d2f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "65cb917c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to C:\\Users\\ANCHIT\n",
      "[nltk_data]     SINHA\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is done to download some Nltk Dependencies\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8d4f5651",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# add new column for num of characters\n",
    "df['num_characters'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "042ac082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_characters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target                                               text  num_characters\n",
       "0       0  Go until jurong point, crazy.. Available only ...             111\n",
       "1       0                      Ok lar... Joking wif u oni...              29\n",
       "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155\n",
       "3       0  U dun say so early hor... U c already then say...              49\n",
       "4       0  Nah I don't think he goes to usf, he lives aro...              61"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bd7a2157",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add new column for num of words\n",
    "df['num_words'] = df['text'].apply(lambda x: len(nltk.word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1c712ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <th>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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b621f3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add new column for num of sentences\n",
    "df['num_sentences'] = df['text'].apply(lambda x: len(nltk.sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "62dbbef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</th>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "da2e9e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\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>78.923776</td>\n",
       "      <td>18.456375</td>\n",
       "      <td>1.962275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.174846</td>\n",
       "      <td>13.323322</td>\n",
       "      <td>1.433892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters    num_words  num_sentences\n",
       "count     5169.000000  5169.000000    5169.000000\n",
       "mean        78.923776    18.456375       1.962275\n",
       "std         58.174846    13.323322       1.433892\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         36.000000     9.000000       1.000000\n",
       "50%         60.000000    15.000000       1.000000\n",
       "75%        117.000000    26.000000       2.000000\n",
       "max        910.000000   220.000000      38.000000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the summary of the last 3 columns\n",
    "df[['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c6ea5b0",
   "metadata": {},
   "source": [
    "### Analize Ham and Spam Seperately for better observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ce278354",
   "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>70.456820</td>\n",
       "      <td>17.123339</td>\n",
       "      <td>1.815545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.356802</td>\n",
       "      <td>13.491315</td>\n",
       "      <td>1.364098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters    num_words  num_sentences\n",
       "count     4516.000000  4516.000000    4516.000000\n",
       "mean        70.456820    17.123339       1.815545\n",
       "std         56.356802    13.491315       1.364098\n",
       "min          2.000000     1.000000       1.000000\n",
       "25%         34.000000     8.000000       1.000000\n",
       "50%         52.000000    13.000000       1.000000\n",
       "75%         90.000000    22.000000       2.000000\n",
       "max        910.000000   220.000000      38.000000"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ham\n",
    "df[df['target'] == 0][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6ca3fe2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_characters</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.479326</td>\n",
       "      <td>27.675345</td>\n",
       "      <td>2.977029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.014336</td>\n",
       "      <td>7.011513</td>\n",
       "      <td>1.493676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>131.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>148.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>223.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_characters   num_words  num_sentences\n",
       "count      653.000000  653.000000     653.000000\n",
       "mean       137.479326   27.675345       2.977029\n",
       "std         30.014336    7.011513       1.493676\n",
       "min         13.000000    2.000000       1.000000\n",
       "25%        131.000000   25.000000       2.000000\n",
       "50%        148.000000   29.000000       3.000000\n",
       "75%        157.000000   32.000000       4.000000\n",
       "max        223.000000   46.000000       9.000000"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spam\n",
    "df[df['target'] == 1][['num_characters','num_words','num_sentences']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "68634e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4c803932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_characters', ylabel='Count'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Histogram to differentiate between spam and ham on the basis of number of characters\n",
    "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": 56,
   "id": "efb96b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Histogram to differentiate between spam and ham on the basis of number of words\n",
    "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": "markdown",
   "id": "ecd95b6c",
   "metadata": {},
   "source": [
    "### From the above observation, we can clearly see that spam messages are generally bigger in terms of number of characters, words or sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0a0b6630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1fb4d833c70>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 582.375x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the relationship among last 3 columns\n",
    "sns.pairplot(df, hue = 'target')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f479a1",
   "metadata": {},
   "source": [
    "### We can see from the above graph that there are outliers in the data that can degrade the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "933f09fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To get the correlation\n",
    "sns.heatmap(df.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5215b2b5",
   "metadata": {},
   "source": [
    "### By the above heatmap, we can conclude that the relation between target and num_characters will give the best result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c59231e",
   "metadata": {},
   "source": [
    "## 3. Data Preprocessing\n",
    "- Lower case\n",
    "- Tokenization (break into words and convert it into list)\n",
    "- Removing special characters\n",
    "- Removing stop words and punctuation: Stopwords are those words which have no contribution in the meaning of the sentence but are used to in the formation of the sentences.\n",
    "- Stemming: To bring words to their root form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0278c265",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to C:\\Users\\ANCHIT\n",
      "[nltk_data]     SINHA\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c6d61dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For removing stopwords\n",
    "from nltk.corpus import stopwords\n",
    "stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "866f6049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For removing Punctuation\n",
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b6fddfc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'danc'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Stemming\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "ps.stem('dancing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "07af7036",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that will preform all the data processing\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",
    "    text = y[:]\n",
    "    y.clear()\n",
    "    \n",
    "    for i in text:\n",
    "        if i not in stopwords.words('english') and i not in string.punctuation:\n",
    "            y.append(i)\n",
    "            \n",
    "    text = y[:]\n",
    "    y.clear()\n",
    "    \n",
    "    for i in text:\n",
    "        y.append(ps.stem(i))\n",
    "    \n",
    "            \n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "934e143f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transformed_text'] = df['text'].apply(transform_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ca506bdf",
   "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": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35fb96d5",
   "metadata": {},
   "source": [
    "#### Now, we will generate the word cloud for all the words. It will display the words based on the occurence in data. Therefore, by doing this, we will get to know which words are used the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5628e03f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wordcloud\n",
      "  Downloading wordcloud-1.8.2.2-cp38-cp38-win_amd64.whl (152 kB)\n",
      "Requirement already satisfied: numpy>=1.6.1 in d:\\software\\anaconda\\lib\\site-packages (from wordcloud) (1.20.1)\n",
      "Requirement already satisfied: matplotlib in d:\\software\\anaconda\\lib\\site-packages (from wordcloud) (3.3.4)\n",
      "Requirement already satisfied: pillow in d:\\software\\anaconda\\lib\\site-packages (from wordcloud) (8.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in d:\\software\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\software\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in d:\\software\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\software\\anaconda\\lib\\site-packages (from matplotlib->wordcloud) (0.10.0)\n",
      "Requirement already satisfied: six in d:\\software\\anaconda\\lib\\site-packages (from cycler>=0.10->matplotlib->wordcloud) (1.15.0)\n",
      "Installing collected packages: wordcloud\n",
      "Successfully installed wordcloud-1.8.2.2\n"
     ]
    }
   ],
   "source": [
    "!pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "72eb1454",
   "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": 118,
   "id": "ee245410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word cloud for Spam Messages\n",
    "spam_wc = wc.generate(df[df['target'] == 1]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "4f6f3eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1fb4e98cc40>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "7dfde12d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word cloud for Ham Messages\n",
    "ham_wc = wc.generate(df[df['target'] == 0]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "5ea2cb9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1fb4e965fa0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.imshow(ham_wc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9117de",
   "metadata": {},
   "source": [
    "#### Our next task is to find out the top words used in both Spam and Ham messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b4bb73f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For spam messages\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": 124,
   "id": "6fd1d247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9941"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "75c5eaa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sns.barplot(pd.DataFrame(Counter(spam_corpus).most_common(30))[0],pd.DataFrame(Counter(spam_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "34d64740",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Ham messages\n",
    "ham_corpus = []\n",
    "for msg in df[df['target'] == 0]['transformed_text'].tolist():\n",
    "    for word in msg.split():\n",
    "        ham_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6a9d1f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35303"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4547ff97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sns.barplot(pd.DataFrame(Counter(ham_corpus).most_common(30))[0],pd.DataFrame(Counter(ham_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d7526dc",
   "metadata": {},
   "source": [
    "## 4. Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70207f8c",
   "metadata": {},
   "source": [
    "The first model that we will make will be **Naive Bayes Classification**. <br>\n",
    "This is because it is a well known fact that Naive Bayes gives *best performance* on textual based data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28b28db",
   "metadata": {},
   "source": [
    "Every Machine Learning algorithm requires numerical input instead of alphabertical.<br>\n",
    "Therefore, we will have to convert the input to numbers or basically vectorize the input."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2299f044",
   "metadata": {},
   "source": [
    "We will perform three steps:\n",
    "1. Vectorize the data using **bag of words**.\n",
    "2. Give it as input in the Naive Bayes Algorithm.\n",
    "3. Check the performance of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1fab620f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "469e07cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cv.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "14585ab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 6677)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "e47f6b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "18fa7d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ed6799b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "eb715f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying train-test split\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d7fa8722",
   "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": "markdown",
   "id": "8ec45b06",
   "metadata": {},
   "source": [
    "#### Import all the Naives Bayes Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d128c5e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "67e37839",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb2088f",
   "metadata": {},
   "source": [
    "**Accuracy Score:** The ratio of correct predictions (true positives + true negatives) to the total number of predictions. <br>\n",
    "**Precision:** The fraction of positive cases correctly identified (the number of true positives divided by the number of true positives plus false positives).\n",
    "**Confusion Matrix:** It is a tabulation of the predicted and actual value counts for each possible class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "a30cdd24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c95788c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8781431334622823\n",
      "[[790 106]\n",
      " [ 20 118]]\n",
      "0.5267857142857143\n"
     ]
    }
   ],
   "source": [
    "# 1.GaussianNB\n",
    "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": 146,
   "id": "fecb49ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.965183752417795\n",
      "[[872  24]\n",
      " [ 12 126]]\n",
      "0.84\n"
     ]
    }
   ],
   "source": [
    "# 2.MultinomialNB\n",
    "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": 148,
   "id": "d564cff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.971953578336557\n",
      "[[894   2]\n",
      " [ 27 111]]\n",
      "0.9823008849557522\n"
     ]
    }
   ],
   "source": [
    "# 3.BernoulliNB\n",
    "bnb.fit(X_train,y_train)\n",
    "y_pred3 = bnb.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred3))\n",
    "print(confusion_matrix(y_test,y_pred3))\n",
    "print(precision_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19060914",
   "metadata": {},
   "source": [
    "From the above results, we can see that **Bernoulli Naive Bayes** is giving the best results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7696c59",
   "metadata": {},
   "source": [
    "Now, we will check the performance of the model with **TfidfVectorizer** instead of CountVectorizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "45368fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "8e42315e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tfidf.fit_transform(df['transformed_text']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "e2c7118c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "da3b6fb2",
   "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": 282,
   "id": "67882aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.874274661508704\n",
      "[[791 105]\n",
      " [ 25 113]]\n",
      "0.518348623853211\n"
     ]
    }
   ],
   "source": [
    "# 1.GaussianNB\n",
    "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": 283,
   "id": "7fe85ab8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9593810444874274\n",
      "[[896   0]\n",
      " [ 42  96]]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# 2.MultinomialNB\n",
    "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": 284,
   "id": "48585f33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.971953578336557\n",
      "[[894   2]\n",
      " [ 27 111]]\n",
      "0.9823008849557522\n"
     ]
    }
   ],
   "source": [
    "# 3.BernoulliNB\n",
    "bnb.fit(X_train,y_train)\n",
    "y_pred3 = bnb.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred3))\n",
    "print(confusion_matrix(y_test,y_pred3))\n",
    "print(precision_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bdb10ea",
   "metadata": {},
   "source": [
    "As data is imbalanced, **precision score** matters more than accuracy score in this model.\n",
    "\n",
    "In **MultinomialNB** with **TfidfVectorizer**, the *precision_score is 1*, therefore, we will chose this model.\n",
    "This is because we need a model which will not make any mistake. It will not label any email as spam when the email is not spam."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51342a92",
   "metadata": {},
   "source": [
    "Now, we will compare our results with all the other models and check which one is the best performing one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "0c3d25cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "90544a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "7caaaeaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dictionary to access each model easily.\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",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "d97bc131",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to test models\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": 286,
   "id": "f1bb721a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9729206963249516, 0.9741379310344828)"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_classifier(svc,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "6b51ab74",
   "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 -  0.9342359767891683\n",
      "Precision -  0.8301886792452831\n",
      "For  LR\n",
      "Accuracy -  0.9516441005802708\n",
      "Precision -  0.94\n",
      "For  RF\n",
      "Accuracy -  0.9700193423597679\n",
      "Precision -  0.9908256880733946\n",
      "For  AdaBoost\n",
      "Accuracy -  0.9622823984526112\n",
      "Precision -  0.9541284403669725\n",
      "For  BgC\n",
      "Accuracy -  0.9574468085106383\n",
      "Precision -  0.8615384615384616\n",
      "For  ETC\n",
      "Accuracy -  0.9777562862669246\n",
      "Precision -  0.9914529914529915\n",
      "For  GBDT\n",
      "Accuracy -  0.9516441005802708\n",
      "Precision -  0.9313725490196079\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, X_train,y_train,X_test,y_test)\n",
    "    \n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "e278d087",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Precision',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "0edacbf1",
   "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>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.977756</td>\n",
       "      <td>0.991453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.990826</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>0.962282</td>\n",
       "      <td>0.954128</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>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.931373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.861538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.934236</td>\n",
       "      <td>0.830189</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",
       "8       ETC  0.977756   0.991453\n",
       "5        RF  0.970019   0.990826\n",
       "0       SVC  0.972921   0.974138\n",
       "6  AdaBoost  0.962282   0.954128\n",
       "4        LR  0.951644   0.940000\n",
       "9      GBDT  0.951644   0.931373\n",
       "7       BgC  0.957447   0.861538\n",
       "3        DT  0.934236   0.830189"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef23a363",
   "metadata": {},
   "source": [
    "After observing the above results, we are still inclined to use **Naive Bayes Algorithm** as it is giving the best precision score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd323e8d",
   "metadata": {},
   "source": [
    "## Model Improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2cc440",
   "metadata": {},
   "source": [
    "1. Change the max_features parameter of TfIdf to 3000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "4a69054b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(max_features = 3000)\n",
    "\n",
    "X = tfidf.fit_transform(df['transformed_text']).toarray()\n",
    "y = df['target'].values\n",
    "\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": 292,
   "id": "fd014aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8704061895551257\n",
      "[[788 108]\n",
      " [ 26 112]]\n",
      "0.509090909090909\n"
     ]
    }
   ],
   "source": [
    "# 1.GaussianNB\n",
    "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": 310,
   "id": "34e47f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.971953578336557\n",
      "[[896   0]\n",
      " [ 29 109]]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# 2.MultinomialNB\n",
    "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": 294,
   "id": "18df1007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9835589941972921\n",
      "[[895   1]\n",
      " [ 16 122]]\n",
      "0.991869918699187\n"
     ]
    }
   ],
   "source": [
    "# 3.BernoulliNB\n",
    "bnb.fit(X_train,y_train)\n",
    "y_pred3 = bnb.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred3))\n",
    "print(confusion_matrix(y_test,y_pred3))\n",
    "print(precision_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "c48c814e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For  SVC\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9745762711864406\n",
      "For  KN\n",
      "Accuracy -  0.9052224371373307\n",
      "Precision -  1.0\n",
      "For  NB\n",
      "Accuracy -  0.971953578336557\n",
      "Precision -  1.0\n",
      "For  DT\n",
      "Accuracy -  0.9332688588007737\n",
      "Precision -  0.8415841584158416\n",
      "For  LR\n",
      "Accuracy -  0.9564796905222437\n",
      "Precision -  0.9696969696969697\n",
      "For  RF\n",
      "Accuracy -  0.9758220502901354\n",
      "Precision -  0.9829059829059829\n",
      "For  AdaBoost\n",
      "Accuracy -  0.9613152804642167\n",
      "Precision -  0.9454545454545454\n",
      "For  BgC\n",
      "Accuracy -  0.9593810444874274\n",
      "Precision -  0.8692307692307693\n",
      "For  ETC\n",
      "Accuracy -  0.9796905222437138\n",
      "Precision -  0.975609756097561\n",
      "For  GBDT\n",
      "Accuracy -  0.9468085106382979\n",
      "Precision -  0.9278350515463918\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, X_train,y_train,X_test,y_test)\n",
    "    \n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "760b6dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df1 = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Precision',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "948f1b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.971954</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>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.979691</td>\n",
       "      <td>0.975610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</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>0.956480</td>\n",
       "      <td>0.969697</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>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.927835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>0.869231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.933269</td>\n",
       "      <td>0.841584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Algorithm  Accuracy  Precision\n",
       "1        KN  0.905222   1.000000\n",
       "2        NB  0.971954   1.000000\n",
       "5        RF  0.975822   0.982906\n",
       "8       ETC  0.979691   0.975610\n",
       "0       SVC  0.974855   0.974576\n",
       "4        LR  0.956480   0.969697\n",
       "6  AdaBoost  0.961315   0.945455\n",
       "9      GBDT  0.946809   0.927835\n",
       "7       BgC  0.959381   0.869231\n",
       "3        DT  0.933269   0.841584"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "f2d19895",
   "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": 300,
   "id": "eb07ca1e",
   "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>0.971954</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.977756</td>\n",
       "      <td>0.991453</td>\n",
       "      <td>0.979691</td>\n",
       "      <td>0.975610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.970019</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</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>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.962282</td>\n",
       "      <td>0.954128</td>\n",
       "      <td>0.961315</td>\n",
       "      <td>0.945455</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>0.956480</td>\n",
       "      <td>0.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.951644</td>\n",
       "      <td>0.931373</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.927835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.861538</td>\n",
       "      <td>0.959381</td>\n",
       "      <td>0.869231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.934236</td>\n",
       "      <td>0.830189</td>\n",
       "      <td>0.933269</td>\n",
       "      <td>0.841584</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              0.971954               1.000000\n",
       "2       ETC  0.977756   0.991453              0.979691               0.975610\n",
       "3        RF  0.970019   0.990826              0.975822               0.982906\n",
       "4       SVC  0.972921   0.974138              0.974855               0.974576\n",
       "5  AdaBoost  0.962282   0.954128              0.961315               0.945455\n",
       "6        LR  0.951644   0.940000              0.956480               0.969697\n",
       "7      GBDT  0.951644   0.931373              0.946809               0.927835\n",
       "8       BgC  0.957447   0.861538              0.959381               0.869231\n",
       "9        DT  0.934236   0.830189              0.933269               0.841584"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df.merge(temp_df,on='Algorithm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b682545",
   "metadata": {},
   "source": [
    "Next, we will try to improve the results of the model by using **Voting Classifier**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "138ee689",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Voting Classifier\n",
    "svc = SVC(kernel='sigmoid', gamma=1.0,probability=True)\n",
    "mnb = MultinomialNB()\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "4604634e",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('et', etc)],voting='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "8bc46516",
   "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": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "9524ca44",
   "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": "b91ce639",
   "metadata": {},
   "source": [
    "As **Precision** is more important to us, we will not implement this model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6263eb",
   "metadata": {},
   "source": [
    "Next, we will use **Stacking**.<br>\n",
    "Stacking is very similar to voting but in this we give weightage to models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "c99c133f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying stacking\n",
    "estimators=[('svm', svc), ('nb', mnb), ('et', etc)]\n",
    "final_estimator=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "d6d191d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "a6ceafd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "1506d5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9787234042553191\n",
      "Precision 0.9393939393939394\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": "0a309c6c",
   "metadata": {},
   "source": [
    "As it can be clearly observed from the above results, Stacking is not the way to go."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547c0c89",
   "metadata": {},
   "source": [
    "Therefore, after trying multiple methods, the best result is given by **Multinomial Naive Bayes**.<br>\n",
    "Hence, we will use this model going forward."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84176381",
   "metadata": {},
   "source": [
    "## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "97c02389",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(tfidf,open('vectorizer.pkl','wb'))\n",
    "pickle.dump(mnb,open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68252379",
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  }
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