spam_classifier_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af0095aa",
   "metadata": {},
   "source": [
    "## Objective \n",
    "Detecting wheter the E-Mail is Spam or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0cbd97c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# ignoring the feature warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0625336",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aff35295",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset/dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c45f086",
   "metadata": {},
   "source": [
    "### Analyzing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "418a2d11",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_freq_make</th>\n",
       "      <th>word_freq_address</th>\n",
       "      <th>word_freq_all</th>\n",
       "      <th>word_freq_3d</th>\n",
       "      <th>word_freq_our</th>\n",
       "      <th>word_freq_over</th>\n",
       "      <th>word_freq_remove</th>\n",
       "      <th>word_freq_internet</th>\n",
       "      <th>word_freq_order</th>\n",
       "      <th>word_freq_mail</th>\n",
       "      <th>...</th>\n",
       "      <th>char_freq_%3B</th>\n",
       "      <th>char_freq_%28</th>\n",
       "      <th>char_freq_%5B</th>\n",
       "      <th>char_freq_%21</th>\n",
       "      <th>char_freq_%24</th>\n",
       "      <th>char_freq_%23</th>\n",
       "      <th>capital_run_length_average</th>\n",
       "      <th>capital_run_length_longest</th>\n",
       "      <th>capital_run_length_total</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>3.756</td>\n",
       "      <td>61</td>\n",
       "      <td>278</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.180</td>\n",
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       "      <td>5.114</td>\n",
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       "      <td>1028</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.06</td>\n",
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       "      <td>0.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.010</td>\n",
       "      <td>9.821</td>\n",
       "      <td>485</td>\n",
       "      <td>2259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.537</td>\n",
       "      <td>40</td>\n",
       "      <td>191</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.537</td>\n",
       "      <td>40</td>\n",
       "      <td>191</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
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       "   word_freq_make  word_freq_address  word_freq_all  word_freq_3d  \\\n",
       "0            0.00               0.64           0.64           0.0   \n",
       "1            0.21               0.28           0.50           0.0   \n",
       "2            0.06               0.00           0.71           0.0   \n",
       "3            0.00               0.00           0.00           0.0   \n",
       "4            0.00               0.00           0.00           0.0   \n",
       "\n",
       "   word_freq_our  word_freq_over  word_freq_remove  word_freq_internet  \\\n",
       "0           0.32            0.00              0.00                0.00   \n",
       "1           0.14            0.28              0.21                0.07   \n",
       "2           1.23            0.19              0.19                0.12   \n",
       "3           0.63            0.00              0.31                0.63   \n",
       "4           0.63            0.00              0.31                0.63   \n",
       "\n",
       "   word_freq_order  word_freq_mail  ...  char_freq_%3B  char_freq_%28  \\\n",
       "0             0.00            0.00  ...           0.00          0.000   \n",
       "1             0.00            0.94  ...           0.00          0.132   \n",
       "2             0.64            0.25  ...           0.01          0.143   \n",
       "3             0.31            0.63  ...           0.00          0.137   \n",
       "4             0.31            0.63  ...           0.00          0.135   \n",
       "\n",
       "   char_freq_%5B  char_freq_%21  char_freq_%24  char_freq_%23  \\\n",
       "0            0.0          0.778          0.000          0.000   \n",
       "1            0.0          0.372          0.180          0.048   \n",
       "2            0.0          0.276          0.184          0.010   \n",
       "3            0.0          0.137          0.000          0.000   \n",
       "4            0.0          0.135          0.000          0.000   \n",
       "\n",
       "   capital_run_length_average  capital_run_length_longest  \\\n",
       "0                       3.756                          61   \n",
       "1                       5.114                         101   \n",
       "2                       9.821                         485   \n",
       "3                       3.537                          40   \n",
       "4                       3.537                          40   \n",
       "\n",
       "   capital_run_length_total  class  \n",
       "0                       278      1  \n",
       "1                      1028      1  \n",
       "2                      2259      1  \n",
       "3                       191      1  \n",
       "4                       191      1  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ee69410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4601, 58)"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.shape"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "929c9f2b",
   "metadata": {
    "scrolled": true
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_freq_make</th>\n",
       "      <th>word_freq_address</th>\n",
       "      <th>word_freq_all</th>\n",
       "      <th>word_freq_3d</th>\n",
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       "      <th>word_freq_mail</th>\n",
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       "      <th>count</th>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
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       "      <td>4601.000000</td>\n",
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       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.104553</td>\n",
       "      <td>0.213015</td>\n",
       "      <td>0.280656</td>\n",
       "      <td>0.065425</td>\n",
       "      <td>0.312223</td>\n",
       "      <td>0.095901</td>\n",
       "      <td>0.114208</td>\n",
       "      <td>0.105295</td>\n",
       "      <td>0.090067</td>\n",
       "      <td>0.239413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038575</td>\n",
       "      <td>0.139030</td>\n",
       "      <td>0.016976</td>\n",
       "      <td>0.269071</td>\n",
       "      <td>0.075811</td>\n",
       "      <td>0.044238</td>\n",
       "      <td>5.191515</td>\n",
       "      <td>52.172789</td>\n",
       "      <td>283.289285</td>\n",
       "      <td>0.394045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.305358</td>\n",
       "      <td>1.290575</td>\n",
       "      <td>0.504143</td>\n",
       "      <td>1.395151</td>\n",
       "      <td>0.672513</td>\n",
       "      <td>0.273824</td>\n",
       "      <td>0.391441</td>\n",
       "      <td>0.401071</td>\n",
       "      <td>0.278616</td>\n",
       "      <td>0.644755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243471</td>\n",
       "      <td>0.270355</td>\n",
       "      <td>0.109394</td>\n",
       "      <td>0.815672</td>\n",
       "      <td>0.245882</td>\n",
       "      <td>0.429342</td>\n",
       "      <td>31.729449</td>\n",
       "      <td>194.891310</td>\n",
       "      <td>606.347851</td>\n",
       "      <td>0.488698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.588000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.276000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.315000</td>\n",
       "      <td>0.052000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.706000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.540000</td>\n",
       "      <td>14.280000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>42.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>7.270000</td>\n",
       "      <td>11.110000</td>\n",
       "      <td>5.260000</td>\n",
       "      <td>18.180000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.385000</td>\n",
       "      <td>9.752000</td>\n",
       "      <td>4.081000</td>\n",
       "      <td>32.478000</td>\n",
       "      <td>6.003000</td>\n",
       "      <td>19.829000</td>\n",
       "      <td>1102.500000</td>\n",
       "      <td>9989.000000</td>\n",
       "      <td>15841.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       word_freq_make  word_freq_address  word_freq_all  word_freq_3d  \\\n",
       "count     4601.000000        4601.000000    4601.000000   4601.000000   \n",
       "mean         0.104553           0.213015       0.280656      0.065425   \n",
       "std          0.305358           1.290575       0.504143      1.395151   \n",
       "min          0.000000           0.000000       0.000000      0.000000   \n",
       "25%          0.000000           0.000000       0.000000      0.000000   \n",
       "50%          0.000000           0.000000       0.000000      0.000000   \n",
       "75%          0.000000           0.000000       0.420000      0.000000   \n",
       "max          4.540000          14.280000       5.100000     42.810000   \n",
       "\n",
       "       word_freq_our  word_freq_over  word_freq_remove  word_freq_internet  \\\n",
       "count    4601.000000     4601.000000       4601.000000         4601.000000   \n",
       "mean        0.312223        0.095901          0.114208            0.105295   \n",
       "std         0.672513        0.273824          0.391441            0.401071   \n",
       "min         0.000000        0.000000          0.000000            0.000000   \n",
       "25%         0.000000        0.000000          0.000000            0.000000   \n",
       "50%         0.000000        0.000000          0.000000            0.000000   \n",
       "75%         0.380000        0.000000          0.000000            0.000000   \n",
       "max        10.000000        5.880000          7.270000           11.110000   \n",
       "\n",
       "       word_freq_order  word_freq_mail  ...  char_freq_%3B  char_freq_%28  \\\n",
       "count      4601.000000     4601.000000  ...    4601.000000    4601.000000   \n",
       "mean          0.090067        0.239413  ...       0.038575       0.139030   \n",
       "std           0.278616        0.644755  ...       0.243471       0.270355   \n",
       "min           0.000000        0.000000  ...       0.000000       0.000000   \n",
       "25%           0.000000        0.000000  ...       0.000000       0.000000   \n",
       "50%           0.000000        0.000000  ...       0.000000       0.065000   \n",
       "75%           0.000000        0.160000  ...       0.000000       0.188000   \n",
       "max           5.260000       18.180000  ...       4.385000       9.752000   \n",
       "\n",
       "       char_freq_%5B  char_freq_%21  char_freq_%24  char_freq_%23  \\\n",
       "count    4601.000000    4601.000000    4601.000000    4601.000000   \n",
       "mean        0.016976       0.269071       0.075811       0.044238   \n",
       "std         0.109394       0.815672       0.245882       0.429342   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.315000       0.052000       0.000000   \n",
       "max         4.081000      32.478000       6.003000      19.829000   \n",
       "\n",
       "       capital_run_length_average  capital_run_length_longest  \\\n",
       "count                 4601.000000                 4601.000000   \n",
       "mean                     5.191515                   52.172789   \n",
       "std                     31.729449                  194.891310   \n",
       "min                      1.000000                    1.000000   \n",
       "25%                      1.588000                    6.000000   \n",
       "50%                      2.276000                   15.000000   \n",
       "75%                      3.706000                   43.000000   \n",
       "max                   1102.500000                 9989.000000   \n",
       "\n",
       "       capital_run_length_total        class  \n",
       "count               4601.000000  4601.000000  \n",
       "mean                 283.289285     0.394045  \n",
       "std                  606.347851     0.488698  \n",
       "min                    1.000000     0.000000  \n",
       "25%                   35.000000     0.000000  \n",
       "50%                   95.000000     0.000000  \n",
       "75%                  266.000000     1.000000  \n",
       "max                15841.000000     1.000000  \n",
       "\n",
       "[8 rows x 58 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8f23454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4601 entries, 0 to 4600\n",
      "Data columns (total 58 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   word_freq_make              4601 non-null   float64\n",
      " 1   word_freq_address           4601 non-null   float64\n",
      " 2   word_freq_all               4601 non-null   float64\n",
      " 3   word_freq_3d                4601 non-null   float64\n",
      " 4   word_freq_our               4601 non-null   float64\n",
      " 5   word_freq_over              4601 non-null   float64\n",
      " 6   word_freq_remove            4601 non-null   float64\n",
      " 7   word_freq_internet          4601 non-null   float64\n",
      " 8   word_freq_order             4601 non-null   float64\n",
      " 9   word_freq_mail              4601 non-null   float64\n",
      " 10  word_freq_receive           4601 non-null   float64\n",
      " 11  word_freq_will              4601 non-null   float64\n",
      " 12  word_freq_people            4601 non-null   float64\n",
      " 13  word_freq_report            4601 non-null   float64\n",
      " 14  word_freq_addresses         4601 non-null   float64\n",
      " 15  word_freq_free              4601 non-null   float64\n",
      " 16  word_freq_business          4601 non-null   float64\n",
      " 17  word_freq_email             4601 non-null   float64\n",
      " 18  word_freq_you               4601 non-null   float64\n",
      " 19  word_freq_credit            4601 non-null   float64\n",
      " 20  word_freq_your              4601 non-null   float64\n",
      " 21  word_freq_font              4601 non-null   float64\n",
      " 22  word_freq_000               4601 non-null   float64\n",
      " 23  word_freq_money             4601 non-null   float64\n",
      " 24  word_freq_hp                4601 non-null   float64\n",
      " 25  word_freq_hpl               4601 non-null   float64\n",
      " 26  word_freq_george            4601 non-null   float64\n",
      " 27  word_freq_650               4601 non-null   float64\n",
      " 28  word_freq_lab               4601 non-null   float64\n",
      " 29  word_freq_labs              4601 non-null   float64\n",
      " 30  word_freq_telnet            4601 non-null   float64\n",
      " 31  word_freq_857               4601 non-null   float64\n",
      " 32  word_freq_data              4601 non-null   float64\n",
      " 33  word_freq_415               4601 non-null   float64\n",
      " 34  word_freq_85                4601 non-null   float64\n",
      " 35  word_freq_technology        4601 non-null   float64\n",
      " 36  word_freq_1999              4601 non-null   float64\n",
      " 37  word_freq_parts             4601 non-null   float64\n",
      " 38  word_freq_pm                4601 non-null   float64\n",
      " 39  word_freq_direct            4601 non-null   float64\n",
      " 40  word_freq_cs                4601 non-null   float64\n",
      " 41  word_freq_meeting           4601 non-null   float64\n",
      " 42  word_freq_original          4601 non-null   float64\n",
      " 43  word_freq_project           4601 non-null   float64\n",
      " 44  word_freq_re                4601 non-null   float64\n",
      " 45  word_freq_edu               4601 non-null   float64\n",
      " 46  word_freq_table             4601 non-null   float64\n",
      " 47  word_freq_conference        4601 non-null   float64\n",
      " 48  char_freq_%3B               4601 non-null   float64\n",
      " 49  char_freq_%28               4601 non-null   float64\n",
      " 50  char_freq_%5B               4601 non-null   float64\n",
      " 51  char_freq_%21               4601 non-null   float64\n",
      " 52  char_freq_%24               4601 non-null   float64\n",
      " 53  char_freq_%23               4601 non-null   float64\n",
      " 54  capital_run_length_average  4601 non-null   float64\n",
      " 55  capital_run_length_longest  4601 non-null   int64  \n",
      " 56  capital_run_length_total    4601 non-null   int64  \n",
      " 57  class                       4601 non-null   int64  \n",
      "dtypes: float64(55), int64(3)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34e4def3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "68bab76d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spam : 39.4% \n",
      "Not_Spam : 60.6% \n",
      "Total : 100.0%\n"
     ]
    }
   ],
   "source": [
    "spam = data['class'].value_counts()[1] / data['class'].value_counts().sum() *  100\n",
    "not_spam = data['class'].value_counts()[0] / data['class'].value_counts().sum() *  100\n",
    "\n",
    "print(f'Spam : {round(spam, 2)}% \\nNot_Spam : {round(not_spam, 2)}% \\nTotal : {round(spam + not_spam, 2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67fee560",
   "metadata": {},
   "source": [
    "## Exploratory Data Analyis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "770f77ef",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f63a433aac0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='word_freq_email', y='capital_run_length_average', data=data, hue='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72c276da",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f63a433aa30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='capital_run_length_total', y='capital_run_length_average', data=data, hue='class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82e31214",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f63668b6430>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='word_freq_credit', y='word_freq_receive', data=data, \n",
    "            hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd219de",
   "metadata": {},
   "source": [
    "#### Not much insights from dataset :("
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fc2c7b",
   "metadata": {},
   "source": [
    "### Scaling and Splitting the dataset into Train-Test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08de5593",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scale = StandardScaler()\n",
    "\n",
    "X = data.drop('class', axis=1)\n",
    "y = data['class']\n",
    "\n",
    "x = scale.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "923e0c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.42433707e-01,  3.30884903e-01,  7.12858774e-01, ...,\n",
       "        -4.52472762e-02,  4.52979198e-02, -8.72413388e-03],\n",
       "       [ 3.45359395e-01,  5.19091945e-02,  4.35129540e-01, ...,\n",
       "        -2.44326749e-03,  2.50562832e-01,  1.22832407e+00],\n",
       "       [-1.45921392e-01, -1.65071912e-01,  8.51723390e-01, ...,\n",
       "         1.45920848e-01,  2.22110599e+00,  3.25873251e+00],\n",
       "       ...,\n",
       "       [ 6.40127868e-01, -1.65071912e-01,  3.83734930e-02, ...,\n",
       "        -1.19382054e-01, -2.36941335e-01, -2.72627750e-01],\n",
       "       [ 2.80176333e+00, -1.65071912e-01, -5.56760578e-01, ...,\n",
       "        -1.27482666e-01, -2.42072958e-01, -3.38603654e-01],\n",
       "       [-3.42433707e-01, -1.65071912e-01,  7.32696576e-01, ...,\n",
       "        -1.24236117e-01, -2.42072958e-01, -4.01280763e-01]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Scaled Dataset \n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da433e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25\n",
    "                                                    , random_state=42) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27c9207",
   "metadata": {},
   "source": [
    "## Creating a classifier using Support Vector Machines (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "214f08c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM classifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07b6224c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Creating A model\n",
    "\n",
    "model = SVC()\n",
    "\n",
    "## Training the model\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "466d5e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Making Predictions\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad5429b",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cba17754",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c1aaf883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f6354a76910>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "46410417",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95       676\n",
      "           1       0.95      0.89      0.92       475\n",
      "\n",
      "    accuracy                           0.94      1151\n",
      "   macro avg       0.94      0.93      0.93      1151\n",
      "weighted avg       0.94      0.94      0.94      1151\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  confusion_matrix,classification_report,precision_recall_curve\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18804948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Positive  > 655\n",
      "False Positive > 21\n",
      "False Negetive > 52\n",
      "True Negetive  > 423\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "true_positive = cm[0, 0]\n",
    "true_negetive = cm[1, 1]\n",
    "\n",
    "print ('True Positive  >', cm[0, 0])\n",
    "print ('False Positive >', cm[0, 1])\n",
    "print ('False Negetive >', cm[1, 0])\n",
    "print ('True Negetive  >', cm[1, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b4bb75",
   "metadata": {},
   "source": [
    "### We have tested many classification algorithms but, SVC performed better than all the other classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "76785d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dumping the model\n",
    "import pickle\n",
    "\n",
    "with open(\"spam_classifier_model.pkl\", \"wb\") as f:\n",
    "    pickle.dump(model, f)"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
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