DM_5(naive_bayes).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>564</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>565</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>566</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>567</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>568</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     worst fractal dimension  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer = datasets.load_breast_cancer()\n",
    "df = pd.DataFrame(data = cancer.data,columns = cancer.feature_names)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>293</td>\n",
       "      <td>11.850</td>\n",
       "      <td>17.46</td>\n",
       "      <td>75.54</td>\n",
       "      <td>432.7</td>\n",
       "      <td>0.08372</td>\n",
       "      <td>0.05642</td>\n",
       "      <td>0.026880</td>\n",
       "      <td>0.022800</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.05715</td>\n",
       "      <td>...</td>\n",
       "      <td>13.060</td>\n",
       "      <td>25.75</td>\n",
       "      <td>84.35</td>\n",
       "      <td>517.8</td>\n",
       "      <td>0.13690</td>\n",
       "      <td>0.17580</td>\n",
       "      <td>0.13160</td>\n",
       "      <td>0.09140</td>\n",
       "      <td>0.3101</td>\n",
       "      <td>0.07007</td>\n",
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       "    <tr>\n",
       "      <td>332</td>\n",
       "      <td>11.220</td>\n",
       "      <td>19.86</td>\n",
       "      <td>71.94</td>\n",
       "      <td>387.3</td>\n",
       "      <td>0.10540</td>\n",
       "      <td>0.06779</td>\n",
       "      <td>0.005006</td>\n",
       "      <td>0.007583</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>0.06028</td>\n",
       "      <td>...</td>\n",
       "      <td>11.980</td>\n",
       "      <td>25.78</td>\n",
       "      <td>76.91</td>\n",
       "      <td>436.1</td>\n",
       "      <td>0.14240</td>\n",
       "      <td>0.09669</td>\n",
       "      <td>0.01335</td>\n",
       "      <td>0.02022</td>\n",
       "      <td>0.3292</td>\n",
       "      <td>0.06522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>565</td>\n",
       "      <td>20.130</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.097910</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.32150</td>\n",
       "      <td>0.16280</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>278</td>\n",
       "      <td>13.590</td>\n",
       "      <td>17.84</td>\n",
       "      <td>86.24</td>\n",
       "      <td>572.3</td>\n",
       "      <td>0.07948</td>\n",
       "      <td>0.04052</td>\n",
       "      <td>0.019970</td>\n",
       "      <td>0.012380</td>\n",
       "      <td>0.1573</td>\n",
       "      <td>0.05520</td>\n",
       "      <td>...</td>\n",
       "      <td>15.500</td>\n",
       "      <td>26.10</td>\n",
       "      <td>98.91</td>\n",
       "      <td>739.1</td>\n",
       "      <td>0.10500</td>\n",
       "      <td>0.07622</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.05185</td>\n",
       "      <td>0.2335</td>\n",
       "      <td>0.06263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>16.690</td>\n",
       "      <td>20.20</td>\n",
       "      <td>107.10</td>\n",
       "      <td>857.6</td>\n",
       "      <td>0.07497</td>\n",
       "      <td>0.07112</td>\n",
       "      <td>0.036490</td>\n",
       "      <td>0.023070</td>\n",
       "      <td>0.1846</td>\n",
       "      <td>0.05325</td>\n",
       "      <td>...</td>\n",
       "      <td>19.180</td>\n",
       "      <td>26.56</td>\n",
       "      <td>127.30</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>0.10090</td>\n",
       "      <td>0.29200</td>\n",
       "      <td>0.24770</td>\n",
       "      <td>0.08737</td>\n",
       "      <td>0.4677</td>\n",
       "      <td>0.07623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>277</td>\n",
       "      <td>18.810</td>\n",
       "      <td>19.98</td>\n",
       "      <td>120.90</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>0.08923</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>0.080200</td>\n",
       "      <td>0.058430</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>0.04996</td>\n",
       "      <td>...</td>\n",
       "      <td>19.960</td>\n",
       "      <td>24.30</td>\n",
       "      <td>129.00</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0.12430</td>\n",
       "      <td>0.11600</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.12940</td>\n",
       "      <td>0.2567</td>\n",
       "      <td>0.05737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>12.460</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.227300</td>\n",
       "      <td>0.085430</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>...</td>\n",
       "      <td>15.090</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.18530</td>\n",
       "      <td>1.05800</td>\n",
       "      <td>1.10500</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>359</td>\n",
       "      <td>9.436</td>\n",
       "      <td>18.32</td>\n",
       "      <td>59.82</td>\n",
       "      <td>278.6</td>\n",
       "      <td>0.10090</td>\n",
       "      <td>0.05956</td>\n",
       "      <td>0.027100</td>\n",
       "      <td>0.014060</td>\n",
       "      <td>0.1506</td>\n",
       "      <td>0.06959</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020</td>\n",
       "      <td>25.02</td>\n",
       "      <td>75.79</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.13330</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.11440</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>0.08136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>9.720</td>\n",
       "      <td>18.22</td>\n",
       "      <td>60.73</td>\n",
       "      <td>288.1</td>\n",
       "      <td>0.06950</td>\n",
       "      <td>0.02344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1653</td>\n",
       "      <td>0.06447</td>\n",
       "      <td>...</td>\n",
       "      <td>9.968</td>\n",
       "      <td>20.83</td>\n",
       "      <td>62.25</td>\n",
       "      <td>303.8</td>\n",
       "      <td>0.07117</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1909</td>\n",
       "      <td>0.06559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>559</td>\n",
       "      <td>11.510</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "      <td>0.09261</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.111200</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.06570</td>\n",
       "      <td>...</td>\n",
       "      <td>12.480</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
       "      <td>0.08732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>426 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "293       11.850         17.46           75.54      432.7          0.08372   \n",
       "332       11.220         19.86           71.94      387.3          0.10540   \n",
       "565       20.130         28.25          131.20     1261.0          0.09780   \n",
       "278       13.590         17.84           86.24      572.3          0.07948   \n",
       "489       16.690         20.20          107.10      857.6          0.07497   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "277       18.810         19.98          120.90     1102.0          0.08923   \n",
       "9         12.460         24.04           83.97      475.9          0.11860   \n",
       "359        9.436         18.32           59.82      278.6          0.10090   \n",
       "192        9.720         18.22           60.73      288.1          0.06950   \n",
       "559       11.510         23.93           74.52      403.5          0.09261   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "293           0.05642        0.026880             0.022800         0.1875   \n",
       "332           0.06779        0.005006             0.007583         0.1940   \n",
       "565           0.10340        0.144000             0.097910         0.1752   \n",
       "278           0.04052        0.019970             0.012380         0.1573   \n",
       "489           0.07112        0.036490             0.023070         0.1846   \n",
       "..                ...             ...                  ...            ...   \n",
       "277           0.05884        0.080200             0.058430         0.1550   \n",
       "9             0.23960        0.227300             0.085430         0.2030   \n",
       "359           0.05956        0.027100             0.014060         0.1506   \n",
       "192           0.02344        0.000000             0.000000         0.1653   \n",
       "559           0.10210        0.111200             0.041050         0.1388   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "293                 0.05715  ...        13.060          25.75   \n",
       "332                 0.06028  ...        11.980          25.78   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "278                 0.05520  ...        15.500          26.10   \n",
       "489                 0.05325  ...        19.180          26.56   \n",
       "..                      ...  ...           ...            ...   \n",
       "277                 0.04996  ...        19.960          24.30   \n",
       "9                   0.08243  ...        15.090          40.68   \n",
       "359                 0.06959  ...        12.020          25.02   \n",
       "192                 0.06447  ...         9.968          20.83   \n",
       "559                 0.06570  ...        12.480          37.16   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "293            84.35       517.8           0.13690            0.17580   \n",
       "332            76.91       436.1           0.14240            0.09669   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "278            98.91       739.1           0.10500            0.07622   \n",
       "489           127.30      1084.0           0.10090            0.29200   \n",
       "..               ...         ...               ...                ...   \n",
       "277           129.00      1236.0           0.12430            0.11600   \n",
       "9              97.65       711.4           0.18530            1.05800   \n",
       "359            75.79       439.6           0.13330            0.10490   \n",
       "192            62.25       303.8           0.07117            0.02729   \n",
       "559            82.28       474.2           0.12980            0.25170   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "293          0.13160               0.09140          0.3101   \n",
       "332          0.01335               0.02022          0.3292   \n",
       "565          0.32150               0.16280          0.2572   \n",
       "278          0.10600               0.05185          0.2335   \n",
       "489          0.24770               0.08737          0.4677   \n",
       "..               ...                   ...             ...   \n",
       "277          0.22100               0.12940          0.2567   \n",
       "9            1.10500               0.22100          0.4366   \n",
       "359          0.11440               0.05052          0.2454   \n",
       "192          0.00000               0.00000          0.1909   \n",
       "559          0.36300               0.09653          0.2112   \n",
       "\n",
       "     worst fractal dimension  \n",
       "293                  0.07007  \n",
       "332                  0.06522  \n",
       "565                  0.06637  \n",
       "278                  0.06263  \n",
       "489                  0.07623  \n",
       "..                       ...  \n",
       "277                  0.05737  \n",
       "9                    0.20750  \n",
       "359                  0.08136  \n",
       "192                  0.06559  \n",
       "559                  0.08732  \n",
       "\n",
       "[426 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(df, cancer.target, train_size = 0.75, random_state = 0, test_size = 0.25)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = nb.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9370629370629371\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Accuracy : \", metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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