Spam detection linear classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "email_rec=pd.read_csv('Spam.txt', sep=',', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     0     1     2    3     4     5     6     7     8     9   ...    48  \\\n",
       "0  0.00  0.64  0.64  0.0  0.32  0.00  0.00  0.00  0.00  0.00  ...  0.00   \n",
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       "\n",
       "      49   50     51     52     53     54   55    56  57  \n",
       "0  0.000  0.0  0.778  0.000  0.000  3.756   61   278   1  \n",
       "1  0.132  0.0  0.372  0.180  0.048  5.114  101  1028   1  \n",
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       "3  0.137  0.0  0.137  0.000  0.000  3.537   40   191   1  \n",
       "4  0.135  0.0  0.135  0.000  0.000  3.537   40   191   1  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_rec.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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_;  char_freq_(  \\\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_[  char_freq_!  char_freq_$  char_freq_hash  \\\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  spam  \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]\n"
     ]
    }
   ],
   "source": [
    "email_rec.columns  = [\"word_freq_make\", \"word_freq_address\", \"word_freq_all\", \"word_freq_3d\", \n",
    "                      \"word_freq_our\", \"word_freq_over\", \"word_freq_remove\", \"word_freq_internet\", \n",
    "                      \"word_freq_order\", \"word_freq_mail\", \"word_freq_receive\", \"word_freq_will\", \n",
    "                      \"word_freq_people\", \"word_freq_report\", \"word_freq_addresses\", \"word_freq_free\", \n",
    "                      \"word_freq_business\", \"word_freq_email\", \"word_freq_you\", \"word_freq_credit\", \n",
    "                      \"word_freq_your\", \"word_freq_font\", \"word_freq_000\", \"word_freq_money\", \"word_freq_hp\", \n",
    "                      \"word_freq_hpl\", \"word_freq_george\", \"word_freq_650\", \"word_freq_lab\", \"word_freq_labs\", \n",
    "                      \"word_freq_telnet\", \"word_freq_857\", \"word_freq_data\", \"word_freq_415\", \"word_freq_85\", \n",
    "                      \"word_freq_technology\", \"word_freq_1999\", \"word_freq_parts\", \"word_freq_pm\", \"word_freq_direct\",\n",
    "                      \"word_freq_cs\", \"word_freq_meeting\", \"word_freq_original\", \"word_freq_project\", \"word_freq_re\", \n",
    "                      \"word_freq_edu\", \"word_freq_table\", \"word_freq_conference\", \"char_freq_;\", \"char_freq_(\", \n",
    "                      \"char_freq_[\", \"char_freq_!\", \"char_freq_$\", \"char_freq_hash\", \"capital_run_length_average\", \n",
    "                      \"capital_run_length_longest\", \"capital_run_length_total\", \"spam\"]\n",
    "print(email_rec.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4601, 58)\n"
     ]
    }
   ],
   "source": [
    "print(email_rec.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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_;                 4601 non-null   float64\n",
      " 49  char_freq_(                 4601 non-null   float64\n",
      " 50  char_freq_[                 4601 non-null   float64\n",
      " 51  char_freq_!                 4601 non-null   float64\n",
      " 52  char_freq_$                 4601 non-null   float64\n",
      " 53  char_freq_hash              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  spam                        4601 non-null   int64  \n",
      "dtypes: float64(55), int64(3)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "email_rec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "word_freq_make                0\n",
       "word_freq_address             0\n",
       "word_freq_all                 0\n",
       "word_freq_3d                  0\n",
       "word_freq_our                 0\n",
       "word_freq_over                0\n",
       "word_freq_remove              0\n",
       "word_freq_internet            0\n",
       "word_freq_order               0\n",
       "word_freq_mail                0\n",
       "word_freq_receive             0\n",
       "word_freq_will                0\n",
       "word_freq_people              0\n",
       "word_freq_report              0\n",
       "word_freq_addresses           0\n",
       "word_freq_free                0\n",
       "word_freq_business            0\n",
       "word_freq_email               0\n",
       "word_freq_you                 0\n",
       "word_freq_credit              0\n",
       "word_freq_your                0\n",
       "word_freq_font                0\n",
       "word_freq_000                 0\n",
       "word_freq_money               0\n",
       "word_freq_hp                  0\n",
       "word_freq_hpl                 0\n",
       "word_freq_george              0\n",
       "word_freq_650                 0\n",
       "word_freq_lab                 0\n",
       "word_freq_labs                0\n",
       "word_freq_telnet              0\n",
       "word_freq_857                 0\n",
       "word_freq_data                0\n",
       "word_freq_415                 0\n",
       "word_freq_85                  0\n",
       "word_freq_technology          0\n",
       "word_freq_1999                0\n",
       "word_freq_parts               0\n",
       "word_freq_pm                  0\n",
       "word_freq_direct              0\n",
       "word_freq_cs                  0\n",
       "word_freq_meeting             0\n",
       "word_freq_original            0\n",
       "word_freq_project             0\n",
       "word_freq_re                  0\n",
       "word_freq_edu                 0\n",
       "word_freq_table               0\n",
       "word_freq_conference          0\n",
       "char_freq_;                   0\n",
       "char_freq_(                   0\n",
       "char_freq_[                   0\n",
       "char_freq_!                   0\n",
       "char_freq_$                   0\n",
       "char_freq_hash                0\n",
       "capital_run_length_average    0\n",
       "capital_run_length_longest    0\n",
       "capital_run_length_total      0\n",
       "spam                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_rec.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4601.000000\n",
       "mean        0.394045\n",
       "std         0.488698\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         1.000000\n",
       "max         1.000000\n",
       "Name: spam, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_rec['spam'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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",
       "      <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_;</th>\n",
       "      <th>char_freq_(</th>\n",
       "      <th>char_freq_[</th>\n",
       "      <th>char_freq_!</th>\n",
       "      <th>char_freq_$</th>\n",
       "      <th>char_freq_hash</th>\n",
       "      <th>capital_run_length_average</th>\n",
       "      <th>capital_run_length_longest</th>\n",
       "      <th>capital_run_length_total</th>\n",
       "      <th>spam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <td>4601.000000</td>\n",
       "      <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_;  char_freq_(  \\\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_[  char_freq_!  char_freq_$  char_freq_hash  \\\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         spam  \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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_rec.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=email_rec.drop('spam', axis=1)\n",
    "y=email_rec.spam.values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "X=scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3978260869565217\n",
      "0.38522809558291093\n"
     ]
    }
   ],
   "source": [
    "print(y_train.mean())\n",
    "print(y_test.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class SVC in module sklearn.svm._classes:\n",
      "\n",
      "class SVC(sklearn.svm._base.BaseSVC)\n",
      " |  SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)\n",
      " |  \n",
      " |  C-Support Vector Classification.\n",
      " |  \n",
      " |  The implementation is based on libsvm. The fit time scales at least\n",
      " |  quadratically with the number of samples and may be impractical\n",
      " |  beyond tens of thousands of samples. For large datasets\n",
      " |  consider using :class:`sklearn.svm.LinearSVC` or\n",
      " |  :class:`sklearn.linear_model.SGDClassifier` instead, possibly after a\n",
      " |  :class:`sklearn.kernel_approximation.Nystroem` transformer.\n",
      " |  \n",
      " |  The multiclass support is handled according to a one-vs-one scheme.\n",
      " |  \n",
      " |  For details on the precise mathematical formulation of the provided\n",
      " |  kernel functions and how `gamma`, `coef0` and `degree` affect each\n",
      " |  other, see the corresponding section in the narrative documentation:\n",
      " |  :ref:`svm_kernels`.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <svm_classification>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  C : float, default=1.0\n",
      " |      Regularization parameter. The strength of the regularization is\n",
      " |      inversely proportional to C. Must be strictly positive. The penalty\n",
      " |      is a squared l2 penalty.\n",
      " |  \n",
      " |  kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'\n",
      " |      Specifies the kernel type to be used in the algorithm.\n",
      " |      It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or\n",
      " |      a callable.\n",
      " |      If none is given, 'rbf' will be used. If a callable is given it is\n",
      " |      used to pre-compute the kernel matrix from data matrices; that matrix\n",
      " |      should be an array of shape ``(n_samples, n_samples)``.\n",
      " |  \n",
      " |  degree : int, default=3\n",
      " |      Degree of the polynomial kernel function ('poly').\n",
      " |      Ignored by all other kernels.\n",
      " |  \n",
      " |  gamma : {'scale', 'auto'} or float, default='scale'\n",
      " |      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.\n",
      " |  \n",
      " |      - if ``gamma='scale'`` (default) is passed then it uses\n",
      " |        1 / (n_features * X.var()) as value of gamma,\n",
      " |      - if 'auto', uses 1 / n_features.\n",
      " |  \n",
      " |      .. versionchanged:: 0.22\n",
      " |         The default value of ``gamma`` changed from 'auto' to 'scale'.\n",
      " |  \n",
      " |  coef0 : float, default=0.0\n",
      " |      Independent term in kernel function.\n",
      " |      It is only significant in 'poly' and 'sigmoid'.\n",
      " |  \n",
      " |  shrinking : bool, default=True\n",
      " |      Whether to use the shrinking heuristic.\n",
      " |      See the :ref:`User Guide <shrinking_svm>`.\n",
      " |  \n",
      " |  probability : bool, default=False\n",
      " |      Whether to enable probability estimates. This must be enabled prior\n",
      " |      to calling `fit`, will slow down that method as it internally uses\n",
      " |      5-fold cross-validation, and `predict_proba` may be inconsistent with\n",
      " |      `predict`. Read more in the :ref:`User Guide <scores_probabilities>`.\n",
      " |  \n",
      " |  tol : float, default=1e-3\n",
      " |      Tolerance for stopping criterion.\n",
      " |  \n",
      " |  cache_size : float, default=200\n",
      " |      Specify the size of the kernel cache (in MB).\n",
      " |  \n",
      " |  class_weight : dict or 'balanced', default=None\n",
      " |      Set the parameter C of class i to class_weight[i]*C for\n",
      " |      SVC. If not given, all classes are supposed to have\n",
      " |      weight one.\n",
      " |      The \"balanced\" mode uses the values of y to automatically adjust\n",
      " |      weights inversely proportional to class frequencies in the input data\n",
      " |      as ``n_samples / (n_classes * np.bincount(y))``\n",
      " |  \n",
      " |  verbose : bool, default=False\n",
      " |      Enable verbose output. Note that this setting takes advantage of a\n",
      " |      per-process runtime setting in libsvm that, if enabled, may not work\n",
      " |      properly in a multithreaded context.\n",
      " |  \n",
      " |  max_iter : int, default=-1\n",
      " |      Hard limit on iterations within solver, or -1 for no limit.\n",
      " |  \n",
      " |  decision_function_shape : {'ovo', 'ovr'}, default='ovr'\n",
      " |      Whether to return a one-vs-rest ('ovr') decision function of shape\n",
      " |      (n_samples, n_classes) as all other classifiers, or the original\n",
      " |      one-vs-one ('ovo') decision function of libsvm which has shape\n",
      " |      (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one\n",
      " |      ('ovo') is always used as multi-class strategy. The parameter is\n",
      " |      ignored for binary classification.\n",
      " |  \n",
      " |      .. versionchanged:: 0.19\n",
      " |          decision_function_shape is 'ovr' by default.\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *decision_function_shape='ovr'* is recommended.\n",
      " |  \n",
      " |      .. versionchanged:: 0.17\n",
      " |         Deprecated *decision_function_shape='ovo' and None*.\n",
      " |  \n",
      " |  break_ties : bool, default=False\n",
      " |      If true, ``decision_function_shape='ovr'``, and number of classes > 2,\n",
      " |      :term:`predict` will break ties according to the confidence values of\n",
      " |      :term:`decision_function`; otherwise the first class among the tied\n",
      " |      classes is returned. Please note that breaking ties comes at a\n",
      " |      relatively high computational cost compared to a simple predict.\n",
      " |  \n",
      " |      .. versionadded:: 0.22\n",
      " |  \n",
      " |  random_state : int or RandomState instance, default=None\n",
      " |      Controls the pseudo random number generation for shuffling the data for\n",
      " |      probability estimates. Ignored when `probability` is False.\n",
      " |      Pass an int for reproducible output across multiple function calls.\n",
      " |      See :term:`Glossary <random_state>`.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  support_ : ndarray of shape (n_SV,)\n",
      " |      Indices of support vectors.\n",
      " |  \n",
      " |  support_vectors_ : ndarray of shape (n_SV, n_features)\n",
      " |      Support vectors.\n",
      " |  \n",
      " |  n_support_ : ndarray of shape (n_class,), dtype=int32\n",
      " |      Number of support vectors for each class.\n",
      " |  \n",
      " |  dual_coef_ : ndarray of shape (n_class-1, n_SV)\n",
      " |      Dual coefficients of the support vector in the decision\n",
      " |      function (see :ref:`sgd_mathematical_formulation`), multiplied by\n",
      " |      their targets.\n",
      " |      For multiclass, coefficient for all 1-vs-1 classifiers.\n",
      " |      The layout of the coefficients in the multiclass case is somewhat\n",
      " |      non-trivial. See the :ref:`multi-class section of the User Guide\n",
      " |      <svm_multi_class>` for details.\n",
      " |  \n",
      " |  coef_ : ndarray of shape (n_class * (n_class-1) / 2, n_features)\n",
      " |      Weights assigned to the features (coefficients in the primal\n",
      " |      problem). This is only available in the case of a linear kernel.\n",
      " |  \n",
      " |      `coef_` is a readonly property derived from `dual_coef_` and\n",
      " |      `support_vectors_`.\n",
      " |  \n",
      " |  intercept_ : ndarray of shape (n_class * (n_class-1) / 2,)\n",
      " |      Constants in decision function.\n",
      " |  \n",
      " |  fit_status_ : int\n",
      " |      0 if correctly fitted, 1 otherwise (will raise warning)\n",
      " |  \n",
      " |  classes_ : ndarray of shape (n_classes,)\n",
      " |      The classes labels.\n",
      " |  \n",
      " |  probA_ : ndarray of shape (n_class * (n_class-1) / 2)\n",
      " |  probB_ : ndarray of shape (n_class * (n_class-1) / 2)\n",
      " |      If `probability=True`, it corresponds to the parameters learned in\n",
      " |      Platt scaling to produce probability estimates from decision values.\n",
      " |      If `probability=False`, it's an empty array. Platt scaling uses the\n",
      " |      logistic function\n",
      " |      ``1 / (1 + exp(decision_value * probA_ + probB_))``\n",
      " |      where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For\n",
      " |      more information on the multiclass case and training procedure see\n",
      " |      section 8 of [1]_.\n",
      " |  \n",
      " |  class_weight_ : ndarray of shape (n_class,)\n",
      " |      Multipliers of parameter C for each class.\n",
      " |      Computed based on the ``class_weight`` parameter.\n",
      " |  \n",
      " |  shape_fit_ : tuple of int of shape (n_dimensions_of_X,)\n",
      " |      Array dimensions of training vector ``X``.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> import numpy as np\n",
      " |  >>> from sklearn.pipeline import make_pipeline\n",
      " |  >>> from sklearn.preprocessing import StandardScaler\n",
      " |  >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])\n",
      " |  >>> y = np.array([1, 1, 2, 2])\n",
      " |  >>> from sklearn.svm import SVC\n",
      " |  >>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))\n",
      " |  >>> clf.fit(X, y)\n",
      " |  Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      " |                  ('svc', SVC(gamma='auto'))])\n",
      " |  \n",
      " |  >>> print(clf.predict([[-0.8, -1]]))\n",
      " |  [1]\n",
      " |  \n",
      " |  See also\n",
      " |  --------\n",
      " |  SVR\n",
      " |      Support Vector Machine for Regression implemented using libsvm.\n",
      " |  \n",
      " |  LinearSVC\n",
      " |      Scalable Linear Support Vector Machine for classification\n",
      " |      implemented using liblinear. Check the See also section of\n",
      " |      LinearSVC for more comparison element.\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  .. [1] `LIBSVM: A Library for Support Vector Machines\n",
      " |      <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_\n",
      " |  \n",
      " |  .. [2] `Platt, John (1999). \"Probabilistic outputs for support vector\n",
      " |      machines and comparison to regularizedlikelihood methods.\"\n",
      " |      <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      SVC\n",
      " |      sklearn.svm._base.BaseSVC\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      sklearn.svm._base.BaseLibSVM\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, *, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.svm._base.BaseSVC:\n",
      " |  \n",
      " |  decision_function(self, X)\n",
      " |      Evaluates the decision function for the samples in X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2)\n",
      " |          Returns the decision function of the sample for each class\n",
      " |          in the model.\n",
      " |          If decision_function_shape='ovr', the shape is (n_samples,\n",
      " |          n_classes).\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If decision_function_shape='ovo', the function values are proportional\n",
      " |      to the distance of the samples X to the separating hyperplane. If the\n",
      " |      exact distances are required, divide the function values by the norm of\n",
      " |      the weight vector (``coef_``). See also `this question\n",
      " |      <https://stats.stackexchange.com/questions/14876/\n",
      " |      interpreting-distance-from-hyperplane-in-svm>`_ for further details.\n",
      " |      If decision_function_shape='ovr', the decision function is a monotonic\n",
      " |      transformation of ovo decision function.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Perform classification on samples in X.\n",
      " |      \n",
      " |      For an one-class model, +1 or -1 is returned.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features) or                 (n_samples_test, n_samples_train)\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples_test, n_samples_train).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_pred : ndarray of shape (n_samples,)\n",
      " |          Class labels for samples in X.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from sklearn.svm._base.BaseSVC:\n",
      " |  \n",
      " |  predict_log_proba\n",
      " |      Compute log probabilities of possible outcomes for samples in X.\n",
      " |      \n",
      " |      The model need to have probability information computed at training\n",
      " |      time: fit with attribute `probability` set to True.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features) or                 (n_samples_test, n_samples_train)\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples_test, n_samples_train).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      T : ndarray of shape (n_samples, n_classes)\n",
      " |          Returns the log-probabilities of the sample for each class in\n",
      " |          the model. The columns correspond to the classes in sorted\n",
      " |          order, as they appear in the attribute :term:`classes_`.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The probability model is created using cross validation, so\n",
      " |      the results can be slightly different than those obtained by\n",
      " |      predict. Also, it will produce meaningless results on very small\n",
      " |      datasets.\n",
      " |  \n",
      " |  predict_proba\n",
      " |      Compute probabilities of possible outcomes for samples in X.\n",
      " |      \n",
      " |      The model need to have probability information computed at training\n",
      " |      time: fit with attribute `probability` set to True.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          [n_samples_test, n_samples_train]\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      T : ndarray of shape (n_samples, n_classes)\n",
      " |          Returns the probability of the sample for each class in\n",
      " |          the model. The columns correspond to the classes in sorted\n",
      " |          order, as they appear in the attribute :term:`classes_`.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The probability model is created using cross validation, so\n",
      " |      the results can be slightly different than those obtained by\n",
      " |      predict. Also, it will produce meaningless results on very small\n",
      " |      datasets.\n",
      " |  \n",
      " |  probA_\n",
      " |  \n",
      " |  probB_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for X.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of self.predict(X) wrt. y.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.svm._base.BaseLibSVM:\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None)\n",
      " |      Fit the SVM model according to the given training data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)                 or (n_samples, n_samples)\n",
      " |          Training vectors, where n_samples is the number of samples\n",
      " |          and n_features is the number of features.\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples, n_samples).\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,)\n",
      " |          Target values (class labels in classification, real numbers in\n",
      " |          regression)\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Per-sample weights. Rescale C per sample. Higher weights\n",
      " |          force the classifier to put more emphasis on these points.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If X and y are not C-ordered and contiguous arrays of np.float64 and\n",
      " |      X is not a scipy.sparse.csr_matrix, X and/or y may be copied.\n",
      " |      \n",
      " |      If X is a dense array, then the other methods will not support sparse\n",
      " |      matrices as input.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from sklearn.svm._base.BaseLibSVM:\n",
      " |  \n",
      " |  coef_\n",
      " |  \n",
      " |  n_support_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(SVC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=SVC(C=1)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[811,  38],\n",
       "       [ 61, 471]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.confusion_matrix(y_true=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.9283128167994207\n",
      "precision 0.925343811394892\n",
      "recall 0.8853383458646616\n"
     ]
    }
   ],
   "source": [
    "print('accuracy',metrics.accuracy_score(y_test, y_pred))\n",
    "print('precision', metrics.precision_score(y_test, y_pred))\n",
    "print('recall', metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "specificity 0.9552414605418139\n"
     ]
    }
   ],
   "source": [
    "print('specificity', 811/(811+38))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function confusion_matrix in module sklearn.metrics._classification:\n",
      "\n",
      "confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None, normalize=None)\n",
      "    Compute confusion matrix to evaluate the accuracy of a classification.\n",
      "    \n",
      "    By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\n",
      "    is equal to the number of observations known to be in group :math:`i` and\n",
      "    predicted to be in group :math:`j`.\n",
      "    \n",
      "    Thus in binary classification, the count of true negatives is\n",
      "    :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is\n",
      "    :math:`C_{1,1}` and false positives is :math:`C_{0,1}`.\n",
      "    \n",
      "    Read more in the :ref:`User Guide <confusion_matrix>`.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    y_true : array-like of shape (n_samples,)\n",
      "        Ground truth (correct) target values.\n",
      "    \n",
      "    y_pred : array-like of shape (n_samples,)\n",
      "        Estimated targets as returned by a classifier.\n",
      "    \n",
      "    labels : array-like of shape (n_classes), default=None\n",
      "        List of labels to index the matrix. This may be used to reorder\n",
      "        or select a subset of labels.\n",
      "        If ``None`` is given, those that appear at least once\n",
      "        in ``y_true`` or ``y_pred`` are used in sorted order.\n",
      "    \n",
      "    sample_weight : array-like of shape (n_samples,), default=None\n",
      "        Sample weights.\n",
      "    \n",
      "        .. versionadded:: 0.18\n",
      "    \n",
      "    normalize : {'true', 'pred', 'all'}, default=None\n",
      "        Normalizes confusion matrix over the true (rows), predicted (columns)\n",
      "        conditions or all the population. If None, confusion matrix will not be\n",
      "        normalized.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    C : ndarray of shape (n_classes, n_classes)\n",
      "        Confusion matrix whose i-th row and j-th\n",
      "        column entry indicates the number of\n",
      "        samples with true label being i-th class\n",
      "        and prediced label being j-th class.\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] `Wikipedia entry for the Confusion matrix\n",
      "           <https://en.wikipedia.org/wiki/Confusion_matrix>`_\n",
      "           (Wikipedia and other references may use a different\n",
      "           convention for axes)\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> from sklearn.metrics import confusion_matrix\n",
      "    >>> y_true = [2, 0, 2, 2, 0, 1]\n",
      "    >>> y_pred = [0, 0, 2, 2, 0, 2]\n",
      "    >>> confusion_matrix(y_true, y_pred)\n",
      "    array([[2, 0, 0],\n",
      "           [0, 0, 1],\n",
      "           [1, 0, 2]])\n",
      "    \n",
      "    >>> y_true = [\"cat\", \"ant\", \"cat\", \"cat\", \"ant\", \"bird\"]\n",
      "    >>> y_pred = [\"ant\", \"ant\", \"cat\", \"cat\", \"ant\", \"cat\"]\n",
      "    >>> confusion_matrix(y_true, y_pred, labels=[\"ant\", \"bird\", \"cat\"])\n",
      "    array([[2, 0, 0],\n",
      "           [0, 0, 1],\n",
      "           [1, 0, 2]])\n",
      "    \n",
      "    In the binary case, we can extract true positives, etc as follows:\n",
      "    \n",
      "    >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()\n",
      "    >>> (tn, fp, fn, tp)\n",
      "    (0, 2, 1, 1)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(metrics.confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds=KFold(n_splits=5, shuffle=True, random_state=4)\n",
    "model=SVC(C=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results = cross_val_score(model, X_train, y_train, cv = folds, scoring = 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.91770186 0.94099379 0.92080745 0.93012422 0.93944099]\n",
      "mean accuracy =0.9298136645962731\n"
     ]
    }
   ],
   "source": [
    "print(cv_results)\n",
    "print('mean accuracy ={}'.format(cv_results.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "params= {'C':[0.1,1,10,100,1000]}\n",
    "model=SVC()\n",
    "model_cv=GridSearchCV(estimator=model,param_grid=params,scoring='accuracy',cv=folds,verbose=1,return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:   13.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=4, shuffle=True),\n",
       "             estimator=SVC(), param_grid={'C': [0.1, 1, 10, 100, 1000]},\n",
       "             return_train_score=True, scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results=pd.DataFrame(model_cv.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.401228</td>\n",
       "      <td>0.033990</td>\n",
       "      <td>0.072639</td>\n",
       "      <td>0.002853</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'C': 0.1}</td>\n",
       "      <td>0.895963</td>\n",
       "      <td>0.902174</td>\n",
       "      <td>0.906832</td>\n",
       "      <td>0.902174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.905280</td>\n",
       "      <td>0.007795</td>\n",
       "      <td>5</td>\n",
       "      <td>0.912655</td>\n",
       "      <td>0.911879</td>\n",
       "      <td>0.912267</td>\n",
       "      <td>0.911102</td>\n",
       "      <td>0.906056</td>\n",
       "      <td>0.910792</td>\n",
       "      <td>0.002423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.300072</td>\n",
       "      <td>0.058289</td>\n",
       "      <td>0.045540</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 1}</td>\n",
       "      <td>0.917702</td>\n",
       "      <td>0.940994</td>\n",
       "      <td>0.920807</td>\n",
       "      <td>0.930124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.929814</td>\n",
       "      <td>0.009440</td>\n",
       "      <td>2</td>\n",
       "      <td>0.951475</td>\n",
       "      <td>0.946040</td>\n",
       "      <td>0.949922</td>\n",
       "      <td>0.946040</td>\n",
       "      <td>0.945652</td>\n",
       "      <td>0.947826</td>\n",
       "      <td>0.002401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.248382</td>\n",
       "      <td>0.029393</td>\n",
       "      <td>0.043407</td>\n",
       "      <td>0.008588</td>\n",
       "      <td>10</td>\n",
       "      <td>{'C': 10}</td>\n",
       "      <td>0.908385</td>\n",
       "      <td>0.944099</td>\n",
       "      <td>0.933230</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.930745</td>\n",
       "      <td>0.012368</td>\n",
       "      <td>1</td>\n",
       "      <td>0.975543</td>\n",
       "      <td>0.970885</td>\n",
       "      <td>0.973991</td>\n",
       "      <td>0.968944</td>\n",
       "      <td>0.971273</td>\n",
       "      <td>0.972127</td>\n",
       "      <td>0.002347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.283571</td>\n",
       "      <td>0.034267</td>\n",
       "      <td>0.031622</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 100}</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.927019</td>\n",
       "      <td>0.936335</td>\n",
       "      <td>0.930124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.008562</td>\n",
       "      <td>3</td>\n",
       "      <td>0.989519</td>\n",
       "      <td>0.989907</td>\n",
       "      <td>0.989907</td>\n",
       "      <td>0.988354</td>\n",
       "      <td>0.987966</td>\n",
       "      <td>0.989130</td>\n",
       "      <td>0.000814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.385790</td>\n",
       "      <td>0.064097</td>\n",
       "      <td>0.037008</td>\n",
       "      <td>0.017623</td>\n",
       "      <td>1000</td>\n",
       "      <td>{'C': 1000}</td>\n",
       "      <td>0.908385</td>\n",
       "      <td>0.931677</td>\n",
       "      <td>0.922360</td>\n",
       "      <td>0.919255</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920497</td>\n",
       "      <td>0.007440</td>\n",
       "      <td>4</td>\n",
       "      <td>0.996118</td>\n",
       "      <td>0.993789</td>\n",
       "      <td>0.994177</td>\n",
       "      <td>0.993789</td>\n",
       "      <td>0.994177</td>\n",
       "      <td>0.994410</td>\n",
       "      <td>0.000872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       0.401228      0.033990         0.072639        0.002853     0.1   \n",
       "1       0.300072      0.058289         0.045540        0.000758       1   \n",
       "2       0.248382      0.029393         0.043407        0.008588      10   \n",
       "3       0.283571      0.034267         0.031622        0.001851     100   \n",
       "4       0.385790      0.064097         0.037008        0.017623    1000   \n",
       "\n",
       "        params  split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0   {'C': 0.1}           0.895963           0.902174           0.906832   \n",
       "1     {'C': 1}           0.917702           0.940994           0.920807   \n",
       "2    {'C': 10}           0.908385           0.944099           0.933230   \n",
       "3   {'C': 100}           0.913043           0.927019           0.936335   \n",
       "4  {'C': 1000}           0.908385           0.931677           0.922360   \n",
       "\n",
       "   split3_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0           0.902174  ...         0.905280        0.007795                5   \n",
       "1           0.930124  ...         0.929814        0.009440                2   \n",
       "2           0.928571  ...         0.930745        0.012368                1   \n",
       "3           0.930124  ...         0.928571        0.008562                3   \n",
       "4           0.919255  ...         0.920497        0.007440                4   \n",
       "\n",
       "   split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0            0.912655            0.911879            0.912267   \n",
       "1            0.951475            0.946040            0.949922   \n",
       "2            0.975543            0.970885            0.973991   \n",
       "3            0.989519            0.989907            0.989907   \n",
       "4            0.996118            0.993789            0.994177   \n",
       "\n",
       "   split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0            0.911102            0.906056          0.910792         0.002423  \n",
       "1            0.946040            0.945652          0.947826         0.002401  \n",
       "2            0.968944            0.971273          0.972127         0.002347  \n",
       "3            0.988354            0.987966          0.989130         0.000814  \n",
       "4            0.993789            0.994177          0.994410         0.000872  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(cv_results['param_C'], cv_results['mean_test_score'])\n",
    "plt.plot(cv_results['param_C'], cv_results['mean_train_score'])\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend(['test accuracy','train accuracy'], loc='upper left')\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highest test accuracy is 0.9307453416149067, when C=10\n"
     ]
    }
   ],
   "source": [
    "best_score=model_cv.best_score_\n",
    "best_C=model_cv.best_params_['C']\n",
    "print('highest test accuracy is {0}, when C={1}'.format(best_score,best_C))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=SVC(C=best_C)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.9304851556842868\n",
      "precision 0.9241245136186771\n",
      "recall 0.8928571428571429\n"
     ]
    }
   ],
   "source": [
    "print('accuracy',metrics.accuracy_score(y_test, y_pred))\n",
    "print('precision', metrics.precision_score(y_test, y_pred))\n",
    "print('recall', metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Tuning hyper-parameters for accuracy\n",
      " The highest accuracy score is 0.9307453416149067 at C = {'C': 10}\n",
      "\n",
      "\n",
      "#Tuning hyper-parameters for precision\n",
      " The highest precision score is 0.9373311086391073 at C = {'C': 0.1}\n",
      "\n",
      "\n",
      "#Tuning hyper-parameters for recall\n",
      " The highest recall score is 0.8987270122310326 at C = {'C': 10}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params= {'C':[0.1,1,10,100,1000]}\n",
    "scores=['accuracy','precision', 'recall']\n",
    "for score in scores:\n",
    "    print('#Tuning hyper-parameters for {}'.format(score))\n",
    "    clf=GridSearchCV(SVC(),params,cv=folds,scoring=score,return_train_score=True)\n",
    "    clf.fit(X_train, y_train)\n",
    "    print(\" The highest {0} score is {1} at C = {2}\".format(score, clf.best_score_, clf.best_params_))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}