SVM Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Necessary Libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visulaizing the Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 5)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean diagnosis\n",
       "0        17.99         10.38          122.80     1001.0         M\n",
       "1        20.57         17.77          132.90     1326.0         M\n",
       "2        19.69         21.25          130.00     1203.0         M\n",
       "3        11.42         20.38           77.58      386.1         M\n",
       "4        20.29         14.34          135.10     1297.0         M"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the features and the Target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('diagnosis', axis = 1)\n",
    "Y = df['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.990</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.570</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.690</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.420</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.290</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.450</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.250</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.710</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.000</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.460</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.020</td>\n",
       "      <td>23.24</td>\n",
       "      <td>102.70</td>\n",
       "      <td>797.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.780</td>\n",
       "      <td>17.89</td>\n",
       "      <td>103.60</td>\n",
       "      <td>781.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.170</td>\n",
       "      <td>24.80</td>\n",
       "      <td>132.40</td>\n",
       "      <td>1123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.850</td>\n",
       "      <td>23.95</td>\n",
       "      <td>103.70</td>\n",
       "      <td>782.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.730</td>\n",
       "      <td>22.61</td>\n",
       "      <td>93.60</td>\n",
       "      <td>578.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.540</td>\n",
       "      <td>27.54</td>\n",
       "      <td>96.73</td>\n",
       "      <td>658.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.680</td>\n",
       "      <td>20.13</td>\n",
       "      <td>94.74</td>\n",
       "      <td>684.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.130</td>\n",
       "      <td>20.68</td>\n",
       "      <td>108.10</td>\n",
       "      <td>798.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.810</td>\n",
       "      <td>22.15</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.540</td>\n",
       "      <td>14.36</td>\n",
       "      <td>87.46</td>\n",
       "      <td>566.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13.080</td>\n",
       "      <td>15.71</td>\n",
       "      <td>85.63</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9.504</td>\n",
       "      <td>12.44</td>\n",
       "      <td>60.34</td>\n",
       "      <td>273.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.340</td>\n",
       "      <td>14.26</td>\n",
       "      <td>102.50</td>\n",
       "      <td>704.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21.160</td>\n",
       "      <td>23.04</td>\n",
       "      <td>137.20</td>\n",
       "      <td>1404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.650</td>\n",
       "      <td>21.38</td>\n",
       "      <td>110.00</td>\n",
       "      <td>904.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.140</td>\n",
       "      <td>16.40</td>\n",
       "      <td>116.00</td>\n",
       "      <td>912.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14.580</td>\n",
       "      <td>21.53</td>\n",
       "      <td>97.41</td>\n",
       "      <td>644.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18.610</td>\n",
       "      <td>20.25</td>\n",
       "      <td>122.10</td>\n",
       "      <td>1094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15.300</td>\n",
       "      <td>25.27</td>\n",
       "      <td>102.40</td>\n",
       "      <td>732.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17.570</td>\n",
       "      <td>15.05</td>\n",
       "      <td>115.00</td>\n",
       "      <td>955.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>7.691</td>\n",
       "      <td>25.44</td>\n",
       "      <td>48.34</td>\n",
       "      <td>170.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>11.540</td>\n",
       "      <td>14.44</td>\n",
       "      <td>74.65</td>\n",
       "      <td>402.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>14.470</td>\n",
       "      <td>24.99</td>\n",
       "      <td>95.81</td>\n",
       "      <td>656.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>14.740</td>\n",
       "      <td>25.42</td>\n",
       "      <td>94.70</td>\n",
       "      <td>668.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>13.210</td>\n",
       "      <td>28.06</td>\n",
       "      <td>84.88</td>\n",
       "      <td>538.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>13.870</td>\n",
       "      <td>20.70</td>\n",
       "      <td>89.77</td>\n",
       "      <td>584.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>13.620</td>\n",
       "      <td>23.23</td>\n",
       "      <td>87.19</td>\n",
       "      <td>573.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>10.320</td>\n",
       "      <td>16.35</td>\n",
       "      <td>65.31</td>\n",
       "      <td>324.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>10.260</td>\n",
       "      <td>16.58</td>\n",
       "      <td>65.85</td>\n",
       "      <td>320.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>9.683</td>\n",
       "      <td>19.34</td>\n",
       "      <td>61.05</td>\n",
       "      <td>285.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>10.820</td>\n",
       "      <td>24.21</td>\n",
       "      <td>68.89</td>\n",
       "      <td>361.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>10.860</td>\n",
       "      <td>21.48</td>\n",
       "      <td>68.51</td>\n",
       "      <td>360.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>11.130</td>\n",
       "      <td>22.44</td>\n",
       "      <td>71.49</td>\n",
       "      <td>378.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>12.770</td>\n",
       "      <td>29.43</td>\n",
       "      <td>81.35</td>\n",
       "      <td>507.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>9.333</td>\n",
       "      <td>21.94</td>\n",
       "      <td>59.01</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>12.880</td>\n",
       "      <td>28.92</td>\n",
       "      <td>82.50</td>\n",
       "      <td>514.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>10.290</td>\n",
       "      <td>27.61</td>\n",
       "      <td>65.67</td>\n",
       "      <td>321.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>10.160</td>\n",
       "      <td>19.59</td>\n",
       "      <td>64.73</td>\n",
       "      <td>311.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>9.423</td>\n",
       "      <td>27.88</td>\n",
       "      <td>59.26</td>\n",
       "      <td>271.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>14.590</td>\n",
       "      <td>22.68</td>\n",
       "      <td>96.39</td>\n",
       "      <td>657.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11.510</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>14.050</td>\n",
       "      <td>27.15</td>\n",
       "      <td>91.38</td>\n",
       "      <td>600.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>11.200</td>\n",
       "      <td>29.37</td>\n",
       "      <td>70.67</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>15.220</td>\n",
       "      <td>30.62</td>\n",
       "      <td>103.40</td>\n",
       "      <td>716.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>20.920</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.560</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.130</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.600</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.600</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.760</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radius_mean  texture_mean  perimeter_mean  area_mean\n",
       "0         17.990         10.38          122.80     1001.0\n",
       "1         20.570         17.77          132.90     1326.0\n",
       "2         19.690         21.25          130.00     1203.0\n",
       "3         11.420         20.38           77.58      386.1\n",
       "4         20.290         14.34          135.10     1297.0\n",
       "5         12.450         15.70           82.57      477.1\n",
       "6         18.250         19.98          119.60     1040.0\n",
       "7         13.710         20.83           90.20      577.9\n",
       "8         13.000         21.82           87.50      519.8\n",
       "9         12.460         24.04           83.97      475.9\n",
       "10        16.020         23.24          102.70      797.8\n",
       "11        15.780         17.89          103.60      781.0\n",
       "12        19.170         24.80          132.40     1123.0\n",
       "13        15.850         23.95          103.70      782.7\n",
       "14        13.730         22.61           93.60      578.3\n",
       "15        14.540         27.54           96.73      658.8\n",
       "16        14.680         20.13           94.74      684.5\n",
       "17        16.130         20.68          108.10      798.8\n",
       "18        19.810         22.15          130.00     1260.0\n",
       "19        13.540         14.36           87.46      566.3\n",
       "20        13.080         15.71           85.63      520.0\n",
       "21         9.504         12.44           60.34      273.9\n",
       "22        15.340         14.26          102.50      704.4\n",
       "23        21.160         23.04          137.20     1404.0\n",
       "24        16.650         21.38          110.00      904.6\n",
       "25        17.140         16.40          116.00      912.7\n",
       "26        14.580         21.53           97.41      644.8\n",
       "27        18.610         20.25          122.10     1094.0\n",
       "28        15.300         25.27          102.40      732.4\n",
       "29        17.570         15.05          115.00      955.1\n",
       "..           ...           ...             ...        ...\n",
       "539        7.691         25.44           48.34      170.4\n",
       "540       11.540         14.44           74.65      402.9\n",
       "541       14.470         24.99           95.81      656.4\n",
       "542       14.740         25.42           94.70      668.6\n",
       "543       13.210         28.06           84.88      538.4\n",
       "544       13.870         20.70           89.77      584.8\n",
       "545       13.620         23.23           87.19      573.2\n",
       "546       10.320         16.35           65.31      324.9\n",
       "547       10.260         16.58           65.85      320.8\n",
       "548        9.683         19.34           61.05      285.7\n",
       "549       10.820         24.21           68.89      361.6\n",
       "550       10.860         21.48           68.51      360.5\n",
       "551       11.130         22.44           71.49      378.4\n",
       "552       12.770         29.43           81.35      507.9\n",
       "553        9.333         21.94           59.01      264.0\n",
       "554       12.880         28.92           82.50      514.3\n",
       "555       10.290         27.61           65.67      321.4\n",
       "556       10.160         19.59           64.73      311.7\n",
       "557        9.423         27.88           59.26      271.3\n",
       "558       14.590         22.68           96.39      657.1\n",
       "559       11.510         23.93           74.52      403.5\n",
       "560       14.050         27.15           91.38      600.4\n",
       "561       11.200         29.37           70.67      386.0\n",
       "562       15.220         30.62          103.40      716.9\n",
       "563       20.920         25.09          143.00     1347.0\n",
       "564       21.560         22.39          142.00     1479.0\n",
       "565       20.130         28.25          131.20     1261.0\n",
       "566       16.600         28.08          108.30      858.1\n",
       "567       20.600         29.33          140.10     1265.0\n",
       "568        7.760         24.54           47.92      181.0\n",
       "\n",
       "[569 rows x 4 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      M\n",
       "1      M\n",
       "2      M\n",
       "3      M\n",
       "4      M\n",
       "5      M\n",
       "6      M\n",
       "7      M\n",
       "8      M\n",
       "9      M\n",
       "10     M\n",
       "11     M\n",
       "12     M\n",
       "13     M\n",
       "14     M\n",
       "15     M\n",
       "16     M\n",
       "17     M\n",
       "18     M\n",
       "19     B\n",
       "20     B\n",
       "21     B\n",
       "22     M\n",
       "23     M\n",
       "24     M\n",
       "25     M\n",
       "26     M\n",
       "27     M\n",
       "28     M\n",
       "29     M\n",
       "      ..\n",
       "539    B\n",
       "540    B\n",
       "541    B\n",
       "542    B\n",
       "543    B\n",
       "544    B\n",
       "545    B\n",
       "546    B\n",
       "547    B\n",
       "548    B\n",
       "549    B\n",
       "550    B\n",
       "551    B\n",
       "552    B\n",
       "553    B\n",
       "554    B\n",
       "555    B\n",
       "556    B\n",
       "557    B\n",
       "558    B\n",
       "559    B\n",
       "560    B\n",
       "561    B\n",
       "562    M\n",
       "563    M\n",
       "564    M\n",
       "565    M\n",
       "566    M\n",
       "567    M\n",
       "568    B\n",
       "Name: diagnosis, Length: 569, dtype: object"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into train data and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC(kernel = 'linear')\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'M', 'B', 'B', 'M', 'M',\n",
       "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'M', 'B', 'B', 'M',\n",
       "       'M', 'B', 'B', 'M', 'M', 'B', 'M', 'M', 'M', 'M', 'B', 'M', 'B',\n",
       "       'M', 'B', 'M', 'M', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B',\n",
       "       'M', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M',\n",
       "       'M', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B',\n",
       "       'M', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M'], dtype=object)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Predicting Values Using SVM algorithm Model\"\"\"\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evalutaion from SVM model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64  2]\n",
      " [ 9 39]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.88      0.97      0.92        66\n",
      "          M       0.95      0.81      0.88        48\n",
      "\n",
      "avg / total       0.91      0.90      0.90       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(Y_test, y_pred))\n",
    "print(classification_report(Y_test, y_pred))\n",
    "print(accuracy_score(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing Kernel SVM with Sklearn SVM module:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial Kernel: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=8, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model1 = SVC(kernel='poly', degree=8)\n",
    "model1.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M'], dtype=object)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1 = model1.predict(X_test)\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = SVC(kernel='rbf')\n",
    "model2.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B'], dtype=object)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred2 = model2.predict(X_test)\n",
    "y_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0]\n",
      " [46  2]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.59      1.00      0.74        66\n",
      "          M       1.00      0.04      0.08        48\n",
      "\n",
      "avg / total       0.76      0.60      0.46       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_test, y_pred2))\n",
    "print(classification_report(Y_test, y_pred2))\n",
    "print(accuracy_score(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sigmoid Kernel: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='sigmoid',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = SVC(kernel='sigmoid')\n",
    "model3.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B'], dtype=object)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred3 = model3.predict(X_test)\n",
    "y_pred3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0]\n",
      " [48  0]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.58      1.00      0.73        66\n",
      "          M       0.00      0.00      0.00        48\n",
      "\n",
      "avg / total       0.34      0.58      0.42       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Geekquad\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_test, y_pred3 ))\n",
    "print(classification_report(Y_test, y_pred3))\n",
    "print(accuracy_score(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing with parameters in SVM: \n",
    "There are main three paramteres in SVM:\n",
    "- Kernel\n",
    "- Gamma(Γ) \n",
    "- C (Regularization parameter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Already played with three different types of Kenrel.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C (Regularization Parameter):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0]\n",
      " [46  2]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.59      1.00      0.74        66\n",
      "          M       1.00      0.04      0.08        48\n",
      "\n",
      "avg / total       0.76      0.60      0.46       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    }
   ],
   "source": [
    "\"\"\" c = 1\"\"\"\n",
    "model2 = svm.SVC(kernel='rbf', C=1)\n",
    "model2.fit(X_train, Y_train)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "print(confusion_matrix(Y_test, y_pred2))\n",
    "print(classification_report(Y_test, y_pred2))\n",
    "print(accuracy_score(Y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0]\n",
      " [45  3]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.59      1.00      0.75        66\n",
      "          M       1.00      0.06      0.12        48\n",
      "\n",
      "avg / total       0.77      0.61      0.48       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    }
   ],
   "source": [
    "\"\"\" c = 100\"\"\"\n",
    "model2 = svm.SVC(kernel='rbf', C=100)\n",
    "model2.fit(X_train, Y_train)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "print(confusion_matrix(Y_test, y_pred2))\n",
    "print(classification_report(Y_test, y_pred2))\n",
    "print(accuracy_score(Y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0]\n",
      " [45  3]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.59      1.00      0.75        66\n",
      "          M       1.00      0.06      0.12        48\n",
      "\n",
      "avg / total       0.77      0.61      0.48       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    }
   ],
   "source": [
    "\"\"\" c = 10000\"\"\"\n",
    "model2 = svm.SVC(kernel='rbf', C=10000)\n",
    "model2.fit(X_train, Y_train)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "print(confusion_matrix(Y_test, y_pred2))\n",
    "print(classification_report(Y_test, y_pred2))\n",
    "print(accuracy_score(Y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gamma (Γ):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0]\n",
      " [13 35]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.84      1.00      0.91        66\n",
      "          M       1.00      0.73      0.84        48\n",
      "\n",
      "avg / total       0.90      0.89      0.88       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Γ = 0.001\"\"\"\n",
    "model2 = svm.SVC(kernel='rbf', C=100, gamma=0.001)\n",
    "model2.fit(X_train, Y_train)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "print(confusion_matrix(Y_test, y_pred2))\n",
    "print(classification_report(Y_test, y_pred2))\n",
    "print(accuracy_score(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[65  1]\n",
      " [14 34]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.82      0.98      0.90        66\n",
      "          M       0.97      0.71      0.82        48\n",
      "\n",
      "avg / total       0.89      0.87      0.86       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Γ = 0.01\"\"\"\n",
    "model2 = svm.SVC(kernel='rbf', C=1, gamma=0.01)\n",
    "model2.fit(X_train, Y_train)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "print(confusion_matrix(Y_test, y_pred2))\n",
    "print(classification_report(Y_test, y_pred2))\n",
    "print(accuracy_score(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[66  0]\n",
      " [48  0]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          B       0.58      1.00      0.73        66\n",
      "          M       0.00      0.00      0.00        48\n",
      "\n",
      "avg / total       0.34      0.58      0.42       114\n",
      "\n",
      "0.9035087719298246\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Geekquad\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Γ = 10\"\"\"\n",
    "model2 = svm.SVC(kernel='rbf', C=1, gamma=10)\n",
    "model2.fit(X_train, Y_train)\n",
    "y_pred2 = model2.predict(X_test)\n",
    "print(confusion_matrix(Y_test, y_pred2))\n",
    "print(classification_report(Y_test, y_pred2))\n",
    "print(accuracy_score(Y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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