Lung cancer prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('lung_cancer_examples.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Surname</th>\n",
       "      <th>Age</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>AreaQ</th>\n",
       "      <th>Alkhol</th>\n",
       "      <th>Result</th>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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       "      <th>1</th>\n",
       "      <td>John</td>\n",
       "      <td>Constantine</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <td>Camela</td>\n",
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       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alex</td>\n",
       "      <td>Telles</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Diego</td>\n",
       "      <td>Maradona</td>\n",
       "      <td>68</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>Cristiano</td>\n",
       "      <td>Ronaldo</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mihail</td>\n",
       "      <td>Tal</td>\n",
       "      <td>58</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kathy</td>\n",
       "      <td>Bates</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nicole</td>\n",
       "      <td>Kidman</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ray</td>\n",
       "      <td>Milland</td>\n",
       "      <td>52</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fredric</td>\n",
       "      <td>March</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Yul</td>\n",
       "      <td>Brynner</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Joan</td>\n",
       "      <td>Crawford</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Wyman</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Anna</td>\n",
       "      <td>Magnani</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Katharine</td>\n",
       "      <td>Hepburn</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Katharine</td>\n",
       "      <td>Hepburn</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Barbra</td>\n",
       "      <td>Streisand</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Maggie</td>\n",
       "      <td>Smith</td>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Glenda</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>73</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Fonda</td>\n",
       "      <td>55</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Maximilian</td>\n",
       "      <td>Schell</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Gregory</td>\n",
       "      <td>Peck</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sidney</td>\n",
       "      <td>Poitier</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Rex</td>\n",
       "      <td>Harrison</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Lee</td>\n",
       "      <td>Marvin</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Paul</td>\n",
       "      <td>Scofield</td>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rod</td>\n",
       "      <td>Steiger</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>John</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>55</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Gene</td>\n",
       "      <td>Hackman</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Marlon</td>\n",
       "      <td>Brando</td>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Jack</td>\n",
       "      <td>Lemmon</td>\n",
       "      <td>56</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Jack</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>47</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Finch</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Richard</td>\n",
       "      <td>Dreyfuss</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Dustin</td>\n",
       "      <td>Hoffman</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Henry</td>\n",
       "      <td>Henry</td>\n",
       "      <td>59</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Robert</td>\n",
       "      <td>Duvall</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Ellen</td>\n",
       "      <td>Burstyn</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Faye</td>\n",
       "      <td>Dunaway</td>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Diane</td>\n",
       "      <td>Keaton</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Fonda</td>\n",
       "      <td>47</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Sally</td>\n",
       "      <td>Field</td>\n",
       "      <td>69</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Sissy</td>\n",
       "      <td>Spacek</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Lange</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Gwyneth</td>\n",
       "      <td>Paltrow</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Halle</td>\n",
       "      <td>Berry</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Nicole</td>\n",
       "      <td>Kidman</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Charlize</td>\n",
       "      <td>Theron</td>\n",
       "      <td>53</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Katharine</td>\n",
       "      <td>Hepburn</td>\n",
       "      <td>62</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Katharine</td>\n",
       "      <td>Hepburn</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Barbra</td>\n",
       "      <td>Streisand</td>\n",
       "      <td>44</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Maggie</td>\n",
       "      <td>Smith</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Glenda</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ernest</td>\n",
       "      <td>Borgnine</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Alec</td>\n",
       "      <td>Guinness</td>\n",
       "      <td>77</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Charlton</td>\n",
       "      <td>Heston</td>\n",
       "      <td>75</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Gregory</td>\n",
       "      <td>Peck</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Sidney</td>\n",
       "      <td>Poitier</td>\n",
       "      <td>51</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name      Surname  Age  Smokes  AreaQ  Alkhol  Result\n",
       "0          John         Wick   35       3      5       4       1\n",
       "1          John  Constantine   27      20      2       5       1\n",
       "2        Camela     Anderson   30       0      5       2       0\n",
       "3          Alex       Telles   28       0      8       1       0\n",
       "4         Diego     Maradona   68       4      5       6       1\n",
       "5     Cristiano      Ronaldo   34       0     10       0       0\n",
       "6        Mihail          Tal   58      15     10       0       0\n",
       "7         Kathy        Bates   22      12      5       2       0\n",
       "8        Nicole       Kidman   45       2      6       0       0\n",
       "9           Ray      Milland   52      18      4       5       1\n",
       "10      Fredric        March   33       4      8       0       0\n",
       "11          Yul      Brynner   18      10      6       3       0\n",
       "12         Joan     Crawford   25       2      5       1       0\n",
       "13         Jane        Wyman   28      20      2       8       1\n",
       "14         Anna      Magnani   34      25      4       8       1\n",
       "15   Katharine       Hepburn   39      18      8       1       0\n",
       "16   Katharine       Hepburn   42      22      3       5       1\n",
       "17       Barbra    Streisand   19      12      8       0       0\n",
       "18      Maggie         Smith   62       5      4       3       1\n",
       "19      Glenda       Jackson   73      10      7       6       1\n",
       "20        Jane         Fonda   55      15      1       3       1\n",
       "21  Maximilian        Schell   33       8      8       1       0\n",
       "22     Gregory          Peck   22      20      6       2       0\n",
       "23      Sidney       Poitier   44       5      8       1       0\n",
       "24         Rex      Harrison   77       3      2       6       1\n",
       "25         Lee        Marvin   21      20      5       3       0\n",
       "26        Paul      Scofield   37      15      6       2       0\n",
       "27         Rod       Steiger   34      12      8       0       0\n",
       "28        John         Wayne   55      20      1       4       1\n",
       "29        Gene       Hackman   40      20      2       7       1\n",
       "30      Marlon        Brando   36      13      5       2       0\n",
       "31        Jack        Lemmon   56      20      3       3       1\n",
       "32        Jack     Nicholson   47      15      1       8       1\n",
       "33       Peter        Finch    62      25      3       4       1\n",
       "34     Richard      Dreyfuss   26      10      7       2       0\n",
       "35      Dustin       Hoffman   25      20      8       2       0\n",
       "36       Henry        Henry    59      20      3       4       1\n",
       "37      Robert        Duvall   62      15      5       5       1\n",
       "38       Ellen       Burstyn   33      25      8       2       0\n",
       "39        Faye       Dunaway   37      10      5       3       0\n",
       "40       Diane        Keaton   50      20      2       4       1\n",
       "41        Jane         Fonda   47      12      8       0       0\n",
       "42       Sally         Field   69      20      5       4       1\n",
       "43       Sissy        Spacek   63      20      4       5       1\n",
       "44     Jessica         Lange   39      15      7       2       0\n",
       "45     Gwyneth       Paltrow   21      20      8       3       0\n",
       "46       Halle         Berry   31      20      9       4       0\n",
       "47      Nicole        Kidman   28      10      4       1       0\n",
       "48    Charlize        Theron   53      20      6       3       1\n",
       "49   Katharine       Hepburn   62      20      5       6       1\n",
       "50   Katharine       Hepburn   42      12      6       2       0\n",
       "51      Barbra     Streisand   44      30      1       6       1\n",
       "52      Maggie         Smith   26      34      1       8       1\n",
       "53      Glenda       Jackson   35      20      5       1       0\n",
       "54      Ernest      Borgnine   26      13      6       1       0\n",
       "55        Alec      Guinness   77      20      5       4       1\n",
       "56    Charlton        Heston   75      15      3       5       1\n",
       "57     Gregory          Peck   43      30      3       8       1\n",
       "58      Sidney       Poitier   51      25      9       0       0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>AreaQ</th>\n",
       "      <th>Alkhol</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053665</td>\n",
       "      <td>-0.268957</td>\n",
       "      <td>0.318621</td>\n",
       "      <td>0.637726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smokes</th>\n",
       "      <td>0.053665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.353295</td>\n",
       "      <td>0.469915</td>\n",
       "      <td>0.373444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AreaQ</th>\n",
       "      <td>-0.268957</td>\n",
       "      <td>-0.353295</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.714412</td>\n",
       "      <td>-0.746621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alkhol</th>\n",
       "      <td>0.318621</td>\n",
       "      <td>0.469915</td>\n",
       "      <td>-0.714412</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.810439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Result</th>\n",
       "      <td>0.637726</td>\n",
       "      <td>0.373444</td>\n",
       "      <td>-0.746621</td>\n",
       "      <td>0.810439</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age    Smokes     AreaQ    Alkhol    Result\n",
       "Age     1.000000  0.053665 -0.268957  0.318621  0.637726\n",
       "Smokes  0.053665  1.000000 -0.353295  0.469915  0.373444\n",
       "AreaQ  -0.268957 -0.353295  1.000000 -0.714412 -0.746621\n",
       "Alkhol  0.318621  0.469915 -0.714412  1.000000  0.810439\n",
       "Result  0.637726  0.373444 -0.746621  0.810439  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22632da0f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dataset.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000022632D48B00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000226331614E0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000022633189940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000226331B1E80>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000226331E2438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002263320A9B0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=dataset.iloc[:,-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alkhol</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Alkhol  Result\n",
       "0        4       1\n",
       "1        5       1\n",
       "2        2       0\n",
       "3        1       0\n",
       "4        6       1\n",
       "5        0       0\n",
       "6        0       0\n",
       "7        2       0\n",
       "8        0       0\n",
       "9        5       1\n",
       "10       0       0\n",
       "11       3       0\n",
       "12       1       0\n",
       "13       8       1\n",
       "14       8       1\n",
       "15       1       0\n",
       "16       5       1\n",
       "17       0       0\n",
       "18       3       1\n",
       "19       6       1\n",
       "20       3       1\n",
       "21       1       0\n",
       "22       2       0\n",
       "23       1       0\n",
       "24       6       1\n",
       "25       3       0\n",
       "26       2       0\n",
       "27       0       0\n",
       "28       4       1\n",
       "29       7       1\n",
       "30       2       0\n",
       "31       3       1\n",
       "32       8       1\n",
       "33       4       1\n",
       "34       2       0\n",
       "35       2       0\n",
       "36       4       1\n",
       "37       5       1\n",
       "38       2       0\n",
       "39       3       0\n",
       "40       4       1\n",
       "41       0       0\n",
       "42       4       1\n",
       "43       5       1\n",
       "44       2       0\n",
       "45       3       0\n",
       "46       4       0\n",
       "47       1       0\n",
       "48       3       1\n",
       "49       6       1\n",
       "50       2       0\n",
       "51       6       1\n",
       "52       8       1\n",
       "53       1       0\n",
       "54       1       0\n",
       "55       4       1\n",
       "56       5       1\n",
       "57       8       1\n",
       "58       0       0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x1.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4],\n",
       "       [5],\n",
       "       [2],\n",
       "       [1],\n",
       "       [6],\n",
       "       [0],\n",
       "       [0],\n",
       "       [2],\n",
       "       [0],\n",
       "       [5],\n",
       "       [0],\n",
       "       [3],\n",
       "       [1],\n",
       "       [8],\n",
       "       [8],\n",
       "       [1],\n",
       "       [5],\n",
       "       [0],\n",
       "       [3],\n",
       "       [6],\n",
       "       [3],\n",
       "       [1],\n",
       "       [2],\n",
       "       [1],\n",
       "       [6],\n",
       "       [3],\n",
       "       [2],\n",
       "       [0],\n",
       "       [4],\n",
       "       [7],\n",
       "       [2],\n",
       "       [3],\n",
       "       [8],\n",
       "       [4],\n",
       "       [2],\n",
       "       [2],\n",
       "       [4],\n",
       "       [5],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [0],\n",
       "       [4],\n",
       "       [5],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [1],\n",
       "       [3],\n",
       "       [6],\n",
       "       [2],\n",
       "       [6],\n",
       "       [8],\n",
       "       [1],\n",
       "       [1],\n",
       "       [4],\n",
       "       [5],\n",
       "       [8],\n",
       "       [0]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=dataset.iloc[:,-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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=0.8,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2],\n",
       "       [2],\n",
       "       [5],\n",
       "       [4],\n",
       "       [3],\n",
       "       [2],\n",
       "       [2],\n",
       "       [4],\n",
       "       [4],\n",
       "       [2],\n",
       "       [6],\n",
       "       [0],\n",
       "       [2],\n",
       "       [0],\n",
       "       [4],\n",
       "       [4],\n",
       "       [3],\n",
       "       [2],\n",
       "       [8],\n",
       "       [8],\n",
       "       [6],\n",
       "       [7],\n",
       "       [5],\n",
       "       [5],\n",
       "       [3],\n",
       "       [4],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [3],\n",
       "       [5],\n",
       "       [2],\n",
       "       [3],\n",
       "       [6],\n",
       "       [0],\n",
       "       [8],\n",
       "       [3],\n",
       "       [0],\n",
       "       [0],\n",
       "       [8],\n",
       "       [8],\n",
       "       [2],\n",
       "       [5],\n",
       "       [1],\n",
       "       [3],\n",
       "       [6],\n",
       "       [0],\n",
       "       [1]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\Users\\admin_1\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "g:\\Users\\admin_1\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "g:\\Users\\admin_1\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.76948376],\n",
       "       [-0.92338052],\n",
       "       [ 1.89805995],\n",
       "       [ 1.33377186],\n",
       "       [ 0.20519567],\n",
       "       [-0.92338052],\n",
       "       [-0.92338052],\n",
       "       [ 0.76948376],\n",
       "       [-0.92338052],\n",
       "       [-0.92338052],\n",
       "       [-0.35909242]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.35909242],\n",
       "       [-0.35909242],\n",
       "       [ 1.33377186],\n",
       "       [ 0.76948376],\n",
       "       [ 0.20519567],\n",
       "       [-0.35909242],\n",
       "       [-0.35909242],\n",
       "       [ 0.76948376],\n",
       "       [ 0.76948376],\n",
       "       [-0.35909242],\n",
       "       [ 1.89805995],\n",
       "       [-1.48766861],\n",
       "       [-0.35909242],\n",
       "       [-1.48766861],\n",
       "       [ 0.76948376],\n",
       "       [ 0.76948376],\n",
       "       [ 0.20519567],\n",
       "       [-0.35909242],\n",
       "       [ 3.02663614],\n",
       "       [ 3.02663614],\n",
       "       [ 1.89805995],\n",
       "       [ 2.46234805],\n",
       "       [ 1.33377186],\n",
       "       [ 1.33377186],\n",
       "       [ 0.20519567],\n",
       "       [ 0.76948376],\n",
       "       [-1.48766861],\n",
       "       [-0.92338052],\n",
       "       [-1.48766861],\n",
       "       [ 0.20519567],\n",
       "       [ 1.33377186],\n",
       "       [-0.35909242],\n",
       "       [ 0.20519567],\n",
       "       [ 1.89805995],\n",
       "       [-1.48766861],\n",
       "       [ 3.02663614],\n",
       "       [ 0.20519567],\n",
       "       [-1.48766861],\n",
       "       [-1.48766861],\n",
       "       [ 3.02663614],\n",
       "       [ 3.02663614],\n",
       "       [-0.35909242],\n",
       "       [ 1.33377186],\n",
       "       [-0.92338052],\n",
       "       [ 0.20519567],\n",
       "       [ 1.89805995],\n",
       "       [-1.48766861],\n",
       "       [-0.92338052]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0]], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\Users\\admin_1\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "g:\\Users\\admin_1\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23,  1],\n",
       "       [ 4, 20]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics as metrics\n",
    "fpr,tpr,threshold=metrics.roc_curve(y_test,y_pred)\n",
    "roc_auc=metrics.auc(fpr,tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lr.predict([[-0.35909242]])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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