classification_breast_cancer_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read.csv('C:/Users/MANI/Documents/R sem 2/decision tree/cancerdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id</th><th scope=col>diagnosis</th><th scope=col>radius_mean</th><th scope=col>texture_mean</th><th scope=col>perimeter_mean</th><th scope=col>area_mean</th><th scope=col>smoothness_mean</th><th scope=col>compactness_mean</th><th scope=col>concavity_mean</th><th scope=col>concave.points_mean</th><th scope=col>...</th><th scope=col>radius_worst</th><th scope=col>texture_worst</th><th scope=col>perimeter_worst</th><th scope=col>area_worst</th><th scope=col>smoothness_worst</th><th scope=col>compactness_worst</th><th scope=col>concavity_worst</th><th scope=col>concave.points_worst</th><th scope=col>symmetry_worst</th><th scope=col>fractal_dimension_worst</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>  842302</td><td>M       </td><td>17.99   </td><td>10.38   </td><td>122.80  </td><td>1001.0  </td><td>0.11840 </td><td>0.27760 </td><td>0.3001  </td><td>0.14710 </td><td>...     </td><td>25.38   </td><td>17.33   </td><td>184.60  </td><td>2019.0  </td><td>0.1622  </td><td>0.6656  </td><td>0.7119  </td><td>0.2654  </td><td>0.4601  </td><td>0.11890 </td></tr>\n",
       "\t<tr><td>  842517</td><td>M       </td><td>20.57   </td><td>17.77   </td><td>132.90  </td><td>1326.0  </td><td>0.08474 </td><td>0.07864 </td><td>0.0869  </td><td>0.07017 </td><td>...     </td><td>24.99   </td><td>23.41   </td><td>158.80  </td><td>1956.0  </td><td>0.1238  </td><td>0.1866  </td><td>0.2416  </td><td>0.1860  </td><td>0.2750  </td><td>0.08902 </td></tr>\n",
       "\t<tr><td>84300903</td><td>M       </td><td>19.69   </td><td>21.25   </td><td>130.00  </td><td>1203.0  </td><td>0.10960 </td><td>0.15990 </td><td>0.1974  </td><td>0.12790 </td><td>...     </td><td>23.57   </td><td>25.53   </td><td>152.50  </td><td>1709.0  </td><td>0.1444  </td><td>0.4245  </td><td>0.4504  </td><td>0.2430  </td><td>0.3613  </td><td>0.08758 </td></tr>\n",
       "\t<tr><td>84348301</td><td>M       </td><td>11.42   </td><td>20.38   </td><td> 77.58  </td><td> 386.1  </td><td>0.14250 </td><td>0.28390 </td><td>0.2414  </td><td>0.10520 </td><td>...     </td><td>14.91   </td><td>26.50   </td><td> 98.87  </td><td> 567.7  </td><td>0.2098  </td><td>0.8663  </td><td>0.6869  </td><td>0.2575  </td><td>0.6638  </td><td>0.17300 </td></tr>\n",
       "\t<tr><td>84358402</td><td>M       </td><td>20.29   </td><td>14.34   </td><td>135.10  </td><td>1297.0  </td><td>0.10030 </td><td>0.13280 </td><td>0.1980  </td><td>0.10430 </td><td>...     </td><td>22.54   </td><td>16.67   </td><td>152.20  </td><td>1575.0  </td><td>0.1374  </td><td>0.2050  </td><td>0.4000  </td><td>0.1625  </td><td>0.2364  </td><td>0.07678 </td></tr>\n",
       "\t<tr><td>  843786</td><td>M       </td><td>12.45   </td><td>15.70   </td><td> 82.57  </td><td> 477.1  </td><td>0.12780 </td><td>0.17000 </td><td>0.1578  </td><td>0.08089 </td><td>...     </td><td>15.47   </td><td>23.75   </td><td>103.40  </td><td> 741.6  </td><td>0.1791  </td><td>0.5249  </td><td>0.5355  </td><td>0.1741  </td><td>0.3985  </td><td>0.12440 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllll}\n",
       " id & diagnosis & radius\\_mean & texture\\_mean & perimeter\\_mean & area\\_mean & smoothness\\_mean & compactness\\_mean & concavity\\_mean & concave.points\\_mean & ... & radius\\_worst & texture\\_worst & perimeter\\_worst & area\\_worst & smoothness\\_worst & compactness\\_worst & concavity\\_worst & concave.points\\_worst & symmetry\\_worst & fractal\\_dimension\\_worst\\\\\n",
       "\\hline\n",
       "\t   842302 & M        & 17.99    & 10.38    & 122.80   & 1001.0   & 0.11840  & 0.27760  & 0.3001   & 0.14710  & ...      & 25.38    & 17.33    & 184.60   & 2019.0   & 0.1622   & 0.6656   & 0.7119   & 0.2654   & 0.4601   & 0.11890 \\\\\n",
       "\t   842517 & M        & 20.57    & 17.77    & 132.90   & 1326.0   & 0.08474  & 0.07864  & 0.0869   & 0.07017  & ...      & 24.99    & 23.41    & 158.80   & 1956.0   & 0.1238   & 0.1866   & 0.2416   & 0.1860   & 0.2750   & 0.08902 \\\\\n",
       "\t 84300903 & M        & 19.69    & 21.25    & 130.00   & 1203.0   & 0.10960  & 0.15990  & 0.1974   & 0.12790  & ...      & 23.57    & 25.53    & 152.50   & 1709.0   & 0.1444   & 0.4245   & 0.4504   & 0.2430   & 0.3613   & 0.08758 \\\\\n",
       "\t 84348301 & M        & 11.42    & 20.38    &  77.58   &  386.1   & 0.14250  & 0.28390  & 0.2414   & 0.10520  & ...      & 14.91    & 26.50    &  98.87   &  567.7   & 0.2098   & 0.8663   & 0.6869   & 0.2575   & 0.6638   & 0.17300 \\\\\n",
       "\t 84358402 & M        & 20.29    & 14.34    & 135.10   & 1297.0   & 0.10030  & 0.13280  & 0.1980   & 0.10430  & ...      & 22.54    & 16.67    & 152.20   & 1575.0   & 0.1374   & 0.2050   & 0.4000   & 0.1625   & 0.2364   & 0.07678 \\\\\n",
       "\t   843786 & M        & 12.45    & 15.70    &  82.57   &  477.1   & 0.12780  & 0.17000  & 0.1578   & 0.08089  & ...      & 15.47    & 23.75    & 103.40   &  741.6   & 0.1791   & 0.5249   & 0.5355   & 0.1741   & 0.3985   & 0.12440 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| id | diagnosis | radius_mean | texture_mean | perimeter_mean | area_mean | smoothness_mean | compactness_mean | concavity_mean | concave.points_mean | ... | radius_worst | texture_worst | perimeter_worst | area_worst | smoothness_worst | compactness_worst | concavity_worst | concave.points_worst | symmetry_worst | fractal_dimension_worst |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|   842302 | M        | 17.99    | 10.38    | 122.80   | 1001.0   | 0.11840  | 0.27760  | 0.3001   | 0.14710  | ...      | 25.38    | 17.33    | 184.60   | 2019.0   | 0.1622   | 0.6656   | 0.7119   | 0.2654   | 0.4601   | 0.11890  |\n",
       "|   842517 | M        | 20.57    | 17.77    | 132.90   | 1326.0   | 0.08474  | 0.07864  | 0.0869   | 0.07017  | ...      | 24.99    | 23.41    | 158.80   | 1956.0   | 0.1238   | 0.1866   | 0.2416   | 0.1860   | 0.2750   | 0.08902  |\n",
       "| 84300903 | M        | 19.69    | 21.25    | 130.00   | 1203.0   | 0.10960  | 0.15990  | 0.1974   | 0.12790  | ...      | 23.57    | 25.53    | 152.50   | 1709.0   | 0.1444   | 0.4245   | 0.4504   | 0.2430   | 0.3613   | 0.08758  |\n",
       "| 84348301 | M        | 11.42    | 20.38    |  77.58   |  386.1   | 0.14250  | 0.28390  | 0.2414   | 0.10520  | ...      | 14.91    | 26.50    |  98.87   |  567.7   | 0.2098   | 0.8663   | 0.6869   | 0.2575   | 0.6638   | 0.17300  |\n",
       "| 84358402 | M        | 20.29    | 14.34    | 135.10   | 1297.0   | 0.10030  | 0.13280  | 0.1980   | 0.10430  | ...      | 22.54    | 16.67    | 152.20   | 1575.0   | 0.1374   | 0.2050   | 0.4000   | 0.1625   | 0.2364   | 0.07678  |\n",
       "|   843786 | M        | 12.45    | 15.70    |  82.57   |  477.1   | 0.12780  | 0.17000  | 0.1578   | 0.08089  | ...      | 15.47    | 23.75    | 103.40   |  741.6   | 0.1791   | 0.5249   | 0.5355   | 0.1741   | 0.3985   | 0.12440  |\n",
       "\n"
      ],
      "text/plain": [
       "  id       diagnosis radius_mean texture_mean perimeter_mean area_mean\n",
       "1   842302 M         17.99       10.38        122.80         1001.0   \n",
       "2   842517 M         20.57       17.77        132.90         1326.0   \n",
       "3 84300903 M         19.69       21.25        130.00         1203.0   \n",
       "4 84348301 M         11.42       20.38         77.58          386.1   \n",
       "5 84358402 M         20.29       14.34        135.10         1297.0   \n",
       "6   843786 M         12.45       15.70         82.57          477.1   \n",
       "  smoothness_mean compactness_mean concavity_mean concave.points_mean ...\n",
       "1 0.11840         0.27760          0.3001         0.14710             ...\n",
       "2 0.08474         0.07864          0.0869         0.07017             ...\n",
       "3 0.10960         0.15990          0.1974         0.12790             ...\n",
       "4 0.14250         0.28390          0.2414         0.10520             ...\n",
       "5 0.10030         0.13280          0.1980         0.10430             ...\n",
       "6 0.12780         0.17000          0.1578         0.08089             ...\n",
       "  radius_worst texture_worst perimeter_worst area_worst smoothness_worst\n",
       "1 25.38        17.33         184.60          2019.0     0.1622          \n",
       "2 24.99        23.41         158.80          1956.0     0.1238          \n",
       "3 23.57        25.53         152.50          1709.0     0.1444          \n",
       "4 14.91        26.50          98.87           567.7     0.2098          \n",
       "5 22.54        16.67         152.20          1575.0     0.1374          \n",
       "6 15.47        23.75         103.40           741.6     0.1791          \n",
       "  compactness_worst concavity_worst concave.points_worst symmetry_worst\n",
       "1 0.6656            0.7119          0.2654               0.4601        \n",
       "2 0.1866            0.2416          0.1860               0.2750        \n",
       "3 0.4245            0.4504          0.2430               0.3613        \n",
       "4 0.8663            0.6869          0.2575               0.6638        \n",
       "5 0.2050            0.4000          0.1625               0.2364        \n",
       "6 0.5249            0.5355          0.1741               0.3985        \n",
       "  fractal_dimension_worst\n",
       "1 0.11890                \n",
       "2 0.08902                \n",
       "3 0.08758                \n",
       "4 0.17300                \n",
       "5 0.07678                \n",
       "6 0.12440                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# problem statement: to classify the data that is benign or malignant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t569 obs. of  32 variables:\n",
      " $ id                     : int  842302 842517 84300903 84348301 84358402 843786 844359 84458202 844981 84501001 ...\n",
      " $ diagnosis              : Factor w/ 2 levels \"B\",\"M\": 2 2 2 2 2 2 2 2 2 2 ...\n",
      " $ radius_mean            : num  18 20.6 19.7 11.4 20.3 ...\n",
      " $ texture_mean           : num  10.4 17.8 21.2 20.4 14.3 ...\n",
      " $ perimeter_mean         : num  122.8 132.9 130 77.6 135.1 ...\n",
      " $ area_mean              : num  1001 1326 1203 386 1297 ...\n",
      " $ smoothness_mean        : num  0.1184 0.0847 0.1096 0.1425 0.1003 ...\n",
      " $ compactness_mean       : num  0.2776 0.0786 0.1599 0.2839 0.1328 ...\n",
      " $ concavity_mean         : num  0.3001 0.0869 0.1974 0.2414 0.198 ...\n",
      " $ concave.points_mean    : num  0.1471 0.0702 0.1279 0.1052 0.1043 ...\n",
      " $ symmetry_mean          : num  0.242 0.181 0.207 0.26 0.181 ...\n",
      " $ fractal_dimension_mean : num  0.0787 0.0567 0.06 0.0974 0.0588 ...\n",
      " $ radius_se              : num  1.095 0.543 0.746 0.496 0.757 ...\n",
      " $ texture_se             : num  0.905 0.734 0.787 1.156 0.781 ...\n",
      " $ perimeter_se           : num  8.59 3.4 4.58 3.44 5.44 ...\n",
      " $ area_se                : num  153.4 74.1 94 27.2 94.4 ...\n",
      " $ smoothness_se          : num  0.0064 0.00522 0.00615 0.00911 0.01149 ...\n",
      " $ compactness_se         : num  0.049 0.0131 0.0401 0.0746 0.0246 ...\n",
      " $ concavity_se           : num  0.0537 0.0186 0.0383 0.0566 0.0569 ...\n",
      " $ concave.points_se      : num  0.0159 0.0134 0.0206 0.0187 0.0188 ...\n",
      " $ symmetry_se            : num  0.03 0.0139 0.0225 0.0596 0.0176 ...\n",
      " $ fractal_dimension_se   : num  0.00619 0.00353 0.00457 0.00921 0.00511 ...\n",
      " $ radius_worst           : num  25.4 25 23.6 14.9 22.5 ...\n",
      " $ texture_worst          : num  17.3 23.4 25.5 26.5 16.7 ...\n",
      " $ perimeter_worst        : num  184.6 158.8 152.5 98.9 152.2 ...\n",
      " $ area_worst             : num  2019 1956 1709 568 1575 ...\n",
      " $ smoothness_worst       : num  0.162 0.124 0.144 0.21 0.137 ...\n",
      " $ compactness_worst      : num  0.666 0.187 0.424 0.866 0.205 ...\n",
      " $ concavity_worst        : num  0.712 0.242 0.45 0.687 0.4 ...\n",
      " $ concave.points_worst   : num  0.265 0.186 0.243 0.258 0.163 ...\n",
      " $ symmetry_worst         : num  0.46 0.275 0.361 0.664 0.236 ...\n",
      " $ fractal_dimension_worst: num  0.1189 0.089 0.0876 0.173 0.0768 ...\n"
     ]
    }
   ],
   "source": [
    "str(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       id            diagnosis  radius_mean      texture_mean  \n",
       " Min.   :     8670   B:357     Min.   : 6.981   Min.   : 9.71  \n",
       " 1st Qu.:   869218   M:212     1st Qu.:11.700   1st Qu.:16.17  \n",
       " Median :   906024             Median :13.370   Median :18.84  \n",
       " Mean   : 30371831             Mean   :14.127   Mean   :19.29  \n",
       " 3rd Qu.:  8813129             3rd Qu.:15.780   3rd Qu.:21.80  \n",
       " Max.   :911320502             Max.   :28.110   Max.   :39.28  \n",
       " perimeter_mean     area_mean      smoothness_mean   compactness_mean \n",
       " Min.   : 43.79   Min.   : 143.5   Min.   :0.05263   Min.   :0.01938  \n",
       " 1st Qu.: 75.17   1st Qu.: 420.3   1st Qu.:0.08637   1st Qu.:0.06492  \n",
       " Median : 86.24   Median : 551.1   Median :0.09587   Median :0.09263  \n",
       " Mean   : 91.97   Mean   : 654.9   Mean   :0.09636   Mean   :0.10434  \n",
       " 3rd Qu.:104.10   3rd Qu.: 782.7   3rd Qu.:0.10530   3rd Qu.:0.13040  \n",
       " Max.   :188.50   Max.   :2501.0   Max.   :0.16340   Max.   :0.34540  \n",
       " concavity_mean    concave.points_mean symmetry_mean    fractal_dimension_mean\n",
       " Min.   :0.00000   Min.   :0.00000     Min.   :0.1060   Min.   :0.04996       \n",
       " 1st Qu.:0.02956   1st Qu.:0.02031     1st Qu.:0.1619   1st Qu.:0.05770       \n",
       " Median :0.06154   Median :0.03350     Median :0.1792   Median :0.06154       \n",
       " Mean   :0.08880   Mean   :0.04892     Mean   :0.1812   Mean   :0.06280       \n",
       " 3rd Qu.:0.13070   3rd Qu.:0.07400     3rd Qu.:0.1957   3rd Qu.:0.06612       \n",
       " Max.   :0.42680   Max.   :0.20120     Max.   :0.3040   Max.   :0.09744       \n",
       "   radius_se        texture_se      perimeter_se       area_se       \n",
       " Min.   :0.1115   Min.   :0.3602   Min.   : 0.757   Min.   :  6.802  \n",
       " 1st Qu.:0.2324   1st Qu.:0.8339   1st Qu.: 1.606   1st Qu.: 17.850  \n",
       " Median :0.3242   Median :1.1080   Median : 2.287   Median : 24.530  \n",
       " Mean   :0.4052   Mean   :1.2169   Mean   : 2.866   Mean   : 40.337  \n",
       " 3rd Qu.:0.4789   3rd Qu.:1.4740   3rd Qu.: 3.357   3rd Qu.: 45.190  \n",
       " Max.   :2.8730   Max.   :4.8850   Max.   :21.980   Max.   :542.200  \n",
       " smoothness_se      compactness_se      concavity_se     concave.points_se \n",
       " Min.   :0.001713   Min.   :0.002252   Min.   :0.00000   Min.   :0.000000  \n",
       " 1st Qu.:0.005169   1st Qu.:0.013080   1st Qu.:0.01509   1st Qu.:0.007638  \n",
       " Median :0.006380   Median :0.020450   Median :0.02589   Median :0.010930  \n",
       " Mean   :0.007041   Mean   :0.025478   Mean   :0.03189   Mean   :0.011796  \n",
       " 3rd Qu.:0.008146   3rd Qu.:0.032450   3rd Qu.:0.04205   3rd Qu.:0.014710  \n",
       " Max.   :0.031130   Max.   :0.135400   Max.   :0.39600   Max.   :0.052790  \n",
       "  symmetry_se       fractal_dimension_se  radius_worst   texture_worst  \n",
       " Min.   :0.007882   Min.   :0.0008948    Min.   : 7.93   Min.   :12.02  \n",
       " 1st Qu.:0.015160   1st Qu.:0.0022480    1st Qu.:13.01   1st Qu.:21.08  \n",
       " Median :0.018730   Median :0.0031870    Median :14.97   Median :25.41  \n",
       " Mean   :0.020542   Mean   :0.0037949    Mean   :16.27   Mean   :25.68  \n",
       " 3rd Qu.:0.023480   3rd Qu.:0.0045580    3rd Qu.:18.79   3rd Qu.:29.72  \n",
       " Max.   :0.078950   Max.   :0.0298400    Max.   :36.04   Max.   :49.54  \n",
       " perimeter_worst    area_worst     smoothness_worst  compactness_worst\n",
       " Min.   : 50.41   Min.   : 185.2   Min.   :0.07117   Min.   :0.02729  \n",
       " 1st Qu.: 84.11   1st Qu.: 515.3   1st Qu.:0.11660   1st Qu.:0.14720  \n",
       " Median : 97.66   Median : 686.5   Median :0.13130   Median :0.21190  \n",
       " Mean   :107.26   Mean   : 880.6   Mean   :0.13237   Mean   :0.25427  \n",
       " 3rd Qu.:125.40   3rd Qu.:1084.0   3rd Qu.:0.14600   3rd Qu.:0.33910  \n",
       " Max.   :251.20   Max.   :4254.0   Max.   :0.22260   Max.   :1.05800  \n",
       " concavity_worst  concave.points_worst symmetry_worst   fractal_dimension_worst\n",
       " Min.   :0.0000   Min.   :0.00000      Min.   :0.1565   Min.   :0.05504        \n",
       " 1st Qu.:0.1145   1st Qu.:0.06493      1st Qu.:0.2504   1st Qu.:0.07146        \n",
       " Median :0.2267   Median :0.09993      Median :0.2822   Median :0.08004        \n",
       " Mean   :0.2722   Mean   :0.11461      Mean   :0.2901   Mean   :0.08395        \n",
       " 3rd Qu.:0.3829   3rd Qu.:0.16140      3rd Qu.:0.3179   3rd Qu.:0.09208        \n",
       " Max.   :1.2520   Max.   :0.29100      Max.   :0.6638   Max.   :0.20750        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "any(is.na(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xopUyakmTiukTLldUiffrzhcMWvkRJx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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "ggplot(df,aes(diagnosis)) + geom_bar() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are more Benign cases in the graph than malignant cases in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "num.cols = sapply(df,is.numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "filtering numeric columns for correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>radius_mean</th><th scope=col>texture_mean</th><th scope=col>perimeter_mean</th><th scope=col>area_mean</th><th scope=col>smoothness_mean</th><th scope=col>compactness_mean</th><th scope=col>concavity_mean</th><th scope=col>concave.points_mean</th><th scope=col>symmetry_mean</th><th scope=col>fractal_dimension_mean</th><th scope=col>...</th><th scope=col>radius_worst</th><th scope=col>texture_worst</th><th scope=col>perimeter_worst</th><th scope=col>area_worst</th><th scope=col>smoothness_worst</th><th scope=col>compactness_worst</th><th scope=col>concavity_worst</th><th scope=col>concave.points_worst</th><th scope=col>symmetry_worst</th><th scope=col>fractal_dimension_worst</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>radius_mean</th><td> 1.000000000 </td><td> 0.323781891 </td><td> 0.997855281 </td><td> 0.987357170 </td><td> 0.17058119  </td><td>0.50612358   </td><td>0.67676355   </td><td>0.82252852   </td><td>0.14774124   </td><td>-0.3116308263</td><td>...          </td><td> 0.96953897  </td><td> 0.297007644 </td><td> 0.965136514 </td><td> 0.94108246  </td><td> 0.11961614  </td><td> 0.41346282  </td><td> 0.52691146  </td><td> 0.7442142   </td><td> 0.16395333  </td><td> 0.007065886 </td></tr>\n",
       "\t<tr><th scope=row>texture_mean</th><td> 0.323781891 </td><td> 1.000000000 </td><td> 0.329533059 </td><td> 0.321085696 </td><td>-0.02338852  </td><td>0.23670222   </td><td>0.30241783   </td><td>0.29346405   </td><td>0.07140098   </td><td>-0.0764371834</td><td>...          </td><td> 0.35257295  </td><td> 0.912044589 </td><td> 0.358039575 </td><td> 0.34354595  </td><td> 0.07750336  </td><td> 0.27782959  </td><td> 0.30102522  </td><td> 0.2953158   </td><td> 0.10500791  </td><td> 0.119205351 </td></tr>\n",
       "\t<tr><th scope=row>perimeter_mean</th><td> 0.997855281 </td><td> 0.329533059 </td><td> 1.000000000 </td><td> 0.986506804 </td><td> 0.20727816  </td><td>0.55693621   </td><td>0.71613565   </td><td>0.85097704   </td><td>0.18302721   </td><td>-0.2614769081</td><td>...          </td><td> 0.96947636  </td><td> 0.303038372 </td><td> 0.970386887 </td><td> 0.94154981  </td><td> 0.15054940  </td><td> 0.45577423  </td><td> 0.56387926  </td><td> 0.7712408   </td><td> 0.18911504  </td><td> 0.051018530 </td></tr>\n",
       "\t<tr><th scope=row>area_mean</th><td> 0.987357170 </td><td> 0.321085696 </td><td> 0.986506804 </td><td> 1.000000000 </td><td> 0.17702838  </td><td>0.49850168   </td><td>0.68598283   </td><td>0.82326887   </td><td>0.15129308   </td><td>-0.2831098117</td><td>...          </td><td> 0.96274609  </td><td> 0.287488627 </td><td> 0.959119574 </td><td> 0.95921333  </td><td> 0.12352294  </td><td> 0.39041031  </td><td> 0.51260592  </td><td> 0.7220166   </td><td> 0.14356991  </td><td> 0.003737597 </td></tr>\n",
       "\t<tr><th scope=row>smoothness_mean</th><td> 0.170581187 </td><td>-0.023388516 </td><td> 0.207278164 </td><td> 0.177028377 </td><td> 1.00000000  </td><td>0.65912322   </td><td>0.52198377   </td><td>0.55369517   </td><td>0.55777479   </td><td> 0.5847920019</td><td>...          </td><td> 0.21312014  </td><td> 0.036071799 </td><td> 0.238852626 </td><td> 0.20671836  </td><td> 0.80532420  </td><td> 0.47246844  </td><td> 0.43492571  </td><td> 0.5030534   </td><td> 0.39430948  </td><td> 0.499316369 </td></tr>\n",
       "\t<tr><th scope=row>compactness_mean</th><td> 0.506123578 </td><td> 0.236702222 </td><td> 0.556936211 </td><td> 0.498501682 </td><td> 0.65912322  </td><td>1.00000000   </td><td>0.88312067   </td><td>0.83113504   </td><td>0.60264105   </td><td> 0.5653686634</td><td>...          </td><td> 0.53531540  </td><td> 0.248132833 </td><td> 0.590210428 </td><td> 0.50960381  </td><td> 0.56554117  </td><td> 0.86580904  </td><td> 0.81627525  </td><td> 0.8155732   </td><td> 0.51022343  </td><td> 0.687382323 </td></tr>\n",
       "\t<tr><th scope=row>concavity_mean</th><td> 0.676763550 </td><td> 0.302417828 </td><td> 0.716135650 </td><td> 0.685982829 </td><td> 0.52198377  </td><td>0.88312067   </td><td>1.00000000   </td><td>0.92139103   </td><td>0.50066662   </td><td> 0.3367833594</td><td>...          </td><td> 0.68823641  </td><td> 0.299878889 </td><td> 0.729564917 </td><td> 0.67598723  </td><td> 0.44882204  </td><td> 0.75496802  </td><td> 0.88410264  </td><td> 0.8613230   </td><td> 0.40946413  </td><td> 0.514929891 </td></tr>\n",
       "\t<tr><th scope=row>concave.points_mean</th><td> 0.822528522 </td><td> 0.293464051 </td><td> 0.850977041 </td><td> 0.823268869 </td><td> 0.55369517  </td><td>0.83113504   </td><td>0.92139103   </td><td>1.00000000   </td><td>0.46249739   </td><td> 0.1669173832</td><td>...          </td><td> 0.83031763  </td><td> 0.292751713 </td><td> 0.855923128 </td><td> 0.80962962  </td><td> 0.45275305  </td><td> 0.66745368  </td><td> 0.75239950  </td><td> 0.9101553   </td><td> 0.37574415  </td><td> 0.368661134 </td></tr>\n",
       "\t<tr><th scope=row>symmetry_mean</th><td> 0.147741242 </td><td> 0.071400980 </td><td> 0.183027212 </td><td> 0.151293079 </td><td> 0.55777479  </td><td>0.60264105   </td><td>0.50066662   </td><td>0.46249739   </td><td>1.00000000   </td><td> 0.4799213301</td><td>...          </td><td> 0.18572775  </td><td> 0.090650688 </td><td> 0.219168559 </td><td> 0.17719338  </td><td> 0.42667503  </td><td> 0.47320001  </td><td> 0.43372101  </td><td> 0.4302966   </td><td> 0.69982580  </td><td> 0.438413498 </td></tr>\n",
       "\t<tr><th scope=row>fractal_dimension_mean</th><td>-0.311630826 </td><td>-0.076437183 </td><td>-0.261476908 </td><td>-0.283109812 </td><td> 0.58479200  </td><td>0.56536866   </td><td>0.33678336   </td><td>0.16691738   </td><td>0.47992133   </td><td> 1.0000000000</td><td>...          </td><td>-0.25369149  </td><td>-0.051269202 </td><td>-0.205151211 </td><td>-0.23185445  </td><td> 0.50494208  </td><td> 0.45879816  </td><td> 0.34623388  </td><td> 0.1753254   </td><td> 0.33401868  </td><td> 0.767296779 </td></tr>\n",
       "\t<tr><th scope=row>radius_se</th><td> 0.679090388 </td><td> 0.275868676 </td><td> 0.691765014 </td><td> 0.732562227 </td><td> 0.30146710  </td><td>0.49747345   </td><td>0.63192482   </td><td>0.69804983   </td><td>0.30337926   </td><td> 0.0001109951</td><td>...          </td><td> 0.71506520  </td><td> 0.194798557 </td><td> 0.719683804 </td><td> 0.75154848  </td><td> 0.14191855  </td><td> 0.28710317  </td><td> 0.38058463  </td><td> 0.5310623   </td><td> 0.09454283  </td><td> 0.049559432 </td></tr>\n",
       "\t<tr><th scope=row>texture_se</th><td>-0.097317443 </td><td> 0.386357623 </td><td>-0.086761078 </td><td>-0.066280214 </td><td> 0.06840645  </td><td>0.04620483   </td><td>0.07621835   </td><td>0.02147958   </td><td>0.12805293   </td><td> 0.1641739659</td><td>...          </td><td>-0.11169031  </td><td> 0.409002766 </td><td>-0.102241922 </td><td>-0.08319499  </td><td>-0.07365766  </td><td>-0.09243935  </td><td>-0.06895622  </td><td>-0.1196375   </td><td>-0.12821476  </td><td>-0.045654569 </td></tr>\n",
       "\t<tr><th scope=row>perimeter_se</th><td> 0.674171616 </td><td> 0.281673115 </td><td> 0.693134890 </td><td> 0.726628328 </td><td> 0.29609193  </td><td>0.54890526   </td><td>0.66039079   </td><td>0.71064987   </td><td>0.31389276   </td><td> 0.0398299316</td><td>...          </td><td> 0.69720059  </td><td> 0.200370854 </td><td> 0.721031310 </td><td> 0.73071297  </td><td> 0.13005439  </td><td> 0.34191945  </td><td> 0.41889882  </td><td> 0.5548972   </td><td> 0.10993043  </td><td> 0.085432572 </td></tr>\n",
       "\t<tr><th scope=row>area_se</th><td> 0.735863663 </td><td> 0.259844987 </td><td> 0.744982694 </td><td> 0.800085921 </td><td> 0.24655243  </td><td>0.45565285   </td><td>0.61742681   </td><td>0.69029854   </td><td>0.22397022   </td><td>-0.0901702475</td><td>...          </td><td> 0.75737319  </td><td> 0.196496649 </td><td> 0.761212636 </td><td> 0.81140796  </td><td> 0.12538943  </td><td> 0.28325654  </td><td> 0.38510014  </td><td> 0.5381663   </td><td> 0.07412629  </td><td> 0.017539295 </td></tr>\n",
       "\t<tr><th scope=row>smoothness_se</th><td>-0.222600125 </td><td> 0.006613777 </td><td>-0.202694026 </td><td>-0.166776667 </td><td> 0.33237544  </td><td>0.13529927   </td><td>0.09856375   </td><td>0.02765331   </td><td>0.18732117   </td><td> 0.4019644254</td><td>...          </td><td>-0.23069071  </td><td>-0.074742965 </td><td>-0.217303755 </td><td>-0.18219548  </td><td> 0.31445746  </td><td>-0.05555814  </td><td>-0.05829839  </td><td>-0.1020068   </td><td>-0.10734210  </td><td> 0.101480315 </td></tr>\n",
       "\t<tr><th scope=row>compactness_se</th><td> 0.205999980 </td><td> 0.191974611 </td><td> 0.250743681 </td><td> 0.212582551 </td><td> 0.31894330  </td><td>0.73872179   </td><td>0.67027882   </td><td>0.49042425   </td><td>0.42165915   </td><td> 0.5598366906</td><td>...          </td><td> 0.20460717  </td><td> 0.143002583 </td><td> 0.260515840 </td><td> 0.19937133  </td><td> 0.22739423  </td><td> 0.67878035  </td><td> 0.63914670  </td><td> 0.4832083   </td><td> 0.27787843  </td><td> 0.590972763 </td></tr>\n",
       "\t<tr><th scope=row>concavity_se</th><td> 0.194203623 </td><td> 0.143293077 </td><td> 0.228082345 </td><td> 0.207660060 </td><td> 0.24839568  </td><td>0.57051687   </td><td>0.69127021   </td><td>0.43916707   </td><td>0.34262702   </td><td> 0.4466303217</td><td>...          </td><td> 0.18690352  </td><td> 0.100240984 </td><td> 0.226680426 </td><td> 0.18835265  </td><td> 0.16848132  </td><td> 0.48485780  </td><td> 0.66256413  </td><td> 0.4404723   </td><td> 0.19778782  </td><td> 0.439329269 </td></tr>\n",
       "\t<tr><th scope=row>concave.points_se</th><td> 0.376168956 </td><td> 0.163851025 </td><td> 0.407216916 </td><td> 0.372320282 </td><td> 0.38067569  </td><td>0.64226185   </td><td>0.68325992   </td><td>0.61563413   </td><td>0.39329787   </td><td> 0.3411980444</td><td>...          </td><td> 0.35812667  </td><td> 0.086741210 </td><td> 0.394999252 </td><td> 0.34227116  </td><td> 0.21535060  </td><td> 0.45288838  </td><td> 0.54959238  </td><td> 0.6024496   </td><td> 0.14311567  </td><td> 0.310654551 </td></tr>\n",
       "\t<tr><th scope=row>symmetry_se</th><td>-0.104320881 </td><td> 0.009127168 </td><td>-0.081629327 </td><td>-0.072496588 </td><td> 0.20077438  </td><td>0.22997659   </td><td>0.17800921   </td><td>0.09535079   </td><td>0.44913654   </td><td> 0.3450073971</td><td>...          </td><td>-0.12812077  </td><td>-0.077473420 </td><td>-0.103753044 </td><td>-0.11034274  </td><td>-0.01266180  </td><td> 0.06025488  </td><td> 0.03711905  </td><td>-0.0304134   </td><td> 0.38940248  </td><td> 0.078079476 </td></tr>\n",
       "\t<tr><th scope=row>fractal_dimension_se</th><td>-0.042641269 </td><td> 0.054457520 </td><td>-0.005523391 </td><td>-0.019886963 </td><td> 0.28360670  </td><td>0.50731813   </td><td>0.44930075   </td><td>0.25758375   </td><td>0.33178615   </td><td> 0.6881315775</td><td>...          </td><td>-0.03748762  </td><td>-0.003195029 </td><td>-0.001000398 </td><td>-0.02273615  </td><td> 0.17056832  </td><td> 0.39015884  </td><td> 0.37997466  </td><td> 0.2152040   </td><td> 0.11109396  </td><td> 0.591328066 </td></tr>\n",
       "\t<tr><th scope=row>radius_worst</th><td> 0.969538973 </td><td> 0.352572947 </td><td> 0.969476363 </td><td> 0.962746086 </td><td> 0.21312014  </td><td>0.53531540   </td><td>0.68823641   </td><td>0.83031763   </td><td>0.18572775   </td><td>-0.2536914949</td><td>...          </td><td> 1.00000000  </td><td> 0.359920754 </td><td> 0.993707916 </td><td> 0.98401456  </td><td> 0.21657443  </td><td> 0.47582004  </td><td> 0.57397471  </td><td> 0.7874239   </td><td> 0.24352920  </td><td> 0.093491979 </td></tr>\n",
       "\t<tr><th scope=row>texture_worst</th><td> 0.297007644 </td><td> 0.912044589 </td><td> 0.303038372 </td><td> 0.287488627 </td><td> 0.03607180  </td><td>0.24813283   </td><td>0.29987889   </td><td>0.29275171   </td><td>0.09065069   </td><td>-0.0512692020</td><td>...          </td><td> 0.35992075  </td><td> 1.000000000 </td><td> 0.365098245 </td><td> 0.34584228  </td><td> 0.22542941  </td><td> 0.36083234  </td><td> 0.36836561  </td><td> 0.3597546   </td><td> 0.23302746  </td><td> 0.219122425 </td></tr>\n",
       "\t<tr><th scope=row>perimeter_worst</th><td> 0.965136514 </td><td> 0.358039575 </td><td> 0.970386887 </td><td> 0.959119574 </td><td> 0.23885263  </td><td>0.59021043   </td><td>0.72956492   </td><td>0.85592313   </td><td>0.21916856   </td><td>-0.2051512113</td><td>...          </td><td> 0.99370792  </td><td> 0.365098245 </td><td> 1.000000000 </td><td> 0.97757809  </td><td> 0.23677460  </td><td> 0.52940769  </td><td> 0.61834408  </td><td> 0.8163221   </td><td> 0.26949277  </td><td> 0.138956862 </td></tr>\n",
       "\t<tr><th scope=row>area_worst</th><td> 0.941082460 </td><td> 0.343545947 </td><td> 0.941549808 </td><td> 0.959213326 </td><td> 0.20671836  </td><td>0.50960381   </td><td>0.67598723   </td><td>0.80962962   </td><td>0.17719338   </td><td>-0.2318544512</td><td>...          </td><td> 0.98401456  </td><td> 0.345842283 </td><td> 0.977578091 </td><td> 1.00000000  </td><td> 0.20914533  </td><td> 0.43829628  </td><td> 0.54333053  </td><td> 0.7474188   </td><td> 0.20914551  </td><td> 0.079647034 </td></tr>\n",
       "\t<tr><th scope=row>smoothness_worst</th><td> 0.119616140 </td><td> 0.077503359 </td><td> 0.150549404 </td><td> 0.123522939 </td><td> 0.80532420  </td><td>0.56554117   </td><td>0.44882204   </td><td>0.45275305   </td><td>0.42667503   </td><td> 0.5049420754</td><td>...          </td><td> 0.21657443  </td><td> 0.225429415 </td><td> 0.236774604 </td><td> 0.20914533  </td><td> 1.00000000  </td><td> 0.56818652  </td><td> 0.51852329  </td><td> 0.5476909   </td><td> 0.49383833  </td><td> 0.617624192 </td></tr>\n",
       "\t<tr><th scope=row>compactness_worst</th><td> 0.413462823 </td><td> 0.277829592 </td><td> 0.455774228 </td><td> 0.390410309 </td><td> 0.47246844  </td><td>0.86580904   </td><td>0.75496802   </td><td>0.66745368   </td><td>0.47320001   </td><td> 0.4587981567</td><td>...          </td><td> 0.47582004  </td><td> 0.360832339 </td><td> 0.529407690 </td><td> 0.43829628  </td><td> 0.56818652  </td><td> 1.00000000  </td><td> 0.89226090  </td><td> 0.8010804   </td><td> 0.61444050  </td><td> 0.810454856 </td></tr>\n",
       "\t<tr><th scope=row>concavity_worst</th><td> 0.526911462 </td><td> 0.301025224 </td><td> 0.563879263 </td><td> 0.512605920 </td><td> 0.43492571  </td><td>0.81627525   </td><td>0.88410264   </td><td>0.75239950   </td><td>0.43372101   </td><td> 0.3462338763</td><td>...          </td><td> 0.57397471  </td><td> 0.368365607 </td><td> 0.618344080 </td><td> 0.54333053  </td><td> 0.51852329  </td><td> 0.89226090  </td><td> 1.00000000  </td><td> 0.8554339   </td><td> 0.53251973  </td><td> 0.686510921 </td></tr>\n",
       "\t<tr><th scope=row>concave.points_worst</th><td> 0.744214198 </td><td> 0.295315843 </td><td> 0.771240789 </td><td> 0.722016626 </td><td> 0.50305335  </td><td>0.81557322   </td><td>0.86132303   </td><td>0.91015531   </td><td>0.43029661   </td><td> 0.1753254492</td><td>...          </td><td> 0.78742385  </td><td> 0.359754610 </td><td> 0.816322102 </td><td> 0.74741880  </td><td> 0.54769090  </td><td> 0.80108036  </td><td> 0.85543386  </td><td> 1.0000000   </td><td> 0.50252849  </td><td> 0.511114146 </td></tr>\n",
       "\t<tr><th scope=row>symmetry_worst</th><td> 0.163953335 </td><td> 0.105007910 </td><td> 0.189115040 </td><td> 0.143569914 </td><td> 0.39430948  </td><td>0.51022343   </td><td>0.40946413   </td><td>0.37574415   </td><td>0.69982580   </td><td> 0.3340186839</td><td>...          </td><td> 0.24352920  </td><td> 0.233027461 </td><td> 0.269492769 </td><td> 0.20914551  </td><td> 0.49383833  </td><td> 0.61444050  </td><td> 0.53251973  </td><td> 0.5025285   </td><td> 1.00000000  </td><td> 0.537848206 </td></tr>\n",
       "\t<tr><th scope=row>fractal_dimension_worst</th><td> 0.007065886 </td><td> 0.119205351 </td><td> 0.051018530 </td><td> 0.003737597 </td><td> 0.49931637  </td><td>0.68738232   </td><td>0.51492989   </td><td>0.36866113   </td><td>0.43841350   </td><td> 0.7672967792</td><td>...          </td><td> 0.09349198  </td><td> 0.219122425 </td><td> 0.138956862 </td><td> 0.07964703  </td><td> 0.61762419  </td><td> 0.81045486  </td><td> 0.68651092  </td><td> 0.5111141   </td><td> 0.53784821  </td><td> 1.000000000 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllll}\n",
       "  & radius\\_mean & texture\\_mean & perimeter\\_mean & area\\_mean & smoothness\\_mean & compactness\\_mean & concavity\\_mean & concave.points\\_mean & symmetry\\_mean & fractal\\_dimension\\_mean & ... & radius\\_worst & texture\\_worst & perimeter\\_worst & area\\_worst & smoothness\\_worst & compactness\\_worst & concavity\\_worst & concave.points\\_worst & symmetry\\_worst & fractal\\_dimension\\_worst\\\\\n",
       "\\hline\n",
       "\tradius\\_mean &  1.000000000  &  0.323781891  &  0.997855281  &  0.987357170  &  0.17058119   & 0.50612358    & 0.67676355    & 0.82252852    & 0.14774124    & -0.3116308263 & ...           &  0.96953897   &  0.297007644  &  0.965136514  &  0.94108246   &  0.11961614   &  0.41346282   &  0.52691146   &  0.7442142    &  0.16395333   &  0.007065886 \\\\\n",
       "\ttexture\\_mean &  0.323781891  &  1.000000000  &  0.329533059  &  0.321085696  & -0.02338852   & 0.23670222    & 0.30241783    & 0.29346405    & 0.07140098    & -0.0764371834 & ...           &  0.35257295   &  0.912044589  &  0.358039575  &  0.34354595   &  0.07750336   &  0.27782959   &  0.30102522   &  0.2953158    &  0.10500791   &  0.119205351 \\\\\n",
       "\tperimeter\\_mean &  0.997855281  &  0.329533059  &  1.000000000  &  0.986506804  &  0.20727816   & 0.55693621    & 0.71613565    & 0.85097704    & 0.18302721    & -0.2614769081 & ...           &  0.96947636   &  0.303038372  &  0.970386887  &  0.94154981   &  0.15054940   &  0.45577423   &  0.56387926   &  0.7712408    &  0.18911504   &  0.051018530 \\\\\n",
       "\tarea\\_mean &  0.987357170  &  0.321085696  &  0.986506804  &  1.000000000  &  0.17702838   & 0.49850168    & 0.68598283    & 0.82326887    & 0.15129308    & -0.2831098117 & ...           &  0.96274609   &  0.287488627  &  0.959119574  &  0.95921333   &  0.12352294   &  0.39041031   &  0.51260592   &  0.7220166    &  0.14356991   &  0.003737597 \\\\\n",
       "\tsmoothness\\_mean &  0.170581187  & -0.023388516  &  0.207278164  &  0.177028377  &  1.00000000   & 0.65912322    & 0.52198377    & 0.55369517    & 0.55777479    &  0.5847920019 & ...           &  0.21312014   &  0.036071799  &  0.238852626  &  0.20671836   &  0.80532420   &  0.47246844   &  0.43492571   &  0.5030534    &  0.39430948   &  0.499316369 \\\\\n",
       "\tcompactness\\_mean &  0.506123578  &  0.236702222  &  0.556936211  &  0.498501682  &  0.65912322   & 1.00000000    & 0.88312067    & 0.83113504    & 0.60264105    &  0.5653686634 & ...           &  0.53531540   &  0.248132833  &  0.590210428  &  0.50960381   &  0.56554117   &  0.86580904   &  0.81627525   &  0.8155732    &  0.51022343   &  0.687382323 \\\\\n",
       "\tconcavity\\_mean &  0.676763550  &  0.302417828  &  0.716135650  &  0.685982829  &  0.52198377   & 0.88312067    & 1.00000000    & 0.92139103    & 0.50066662    &  0.3367833594 & ...           &  0.68823641   &  0.299878889  &  0.729564917  &  0.67598723   &  0.44882204   &  0.75496802   &  0.88410264   &  0.8613230    &  0.40946413   &  0.514929891 \\\\\n",
       "\tconcave.points\\_mean &  0.822528522  &  0.293464051  &  0.850977041  &  0.823268869  &  0.55369517   & 0.83113504    & 0.92139103    & 1.00000000    & 0.46249739    &  0.1669173832 & ...           &  0.83031763   &  0.292751713  &  0.855923128  &  0.80962962   &  0.45275305   &  0.66745368   &  0.75239950   &  0.9101553    &  0.37574415   &  0.368661134 \\\\\n",
       "\tsymmetry\\_mean &  0.147741242  &  0.071400980  &  0.183027212  &  0.151293079  &  0.55777479   & 0.60264105    & 0.50066662    & 0.46249739    & 1.00000000    &  0.4799213301 & ...           &  0.18572775   &  0.090650688  &  0.219168559  &  0.17719338   &  0.42667503   &  0.47320001   &  0.43372101   &  0.4302966    &  0.69982580   &  0.438413498 \\\\\n",
       "\tfractal\\_dimension\\_mean & -0.311630826  & -0.076437183  & -0.261476908  & -0.283109812  &  0.58479200   & 0.56536866    & 0.33678336    & 0.16691738    & 0.47992133    &  1.0000000000 & ...           & -0.25369149   & -0.051269202  & -0.205151211  & -0.23185445   &  0.50494208   &  0.45879816   &  0.34623388   &  0.1753254    &  0.33401868   &  0.767296779 \\\\\n",
       "\tradius\\_se &  0.679090388  &  0.275868676  &  0.691765014  &  0.732562227  &  0.30146710   & 0.49747345    & 0.63192482    & 0.69804983    & 0.30337926    &  0.0001109951 & ...           &  0.71506520   &  0.194798557  &  0.719683804  &  0.75154848   &  0.14191855   &  0.28710317   &  0.38058463   &  0.5310623    &  0.09454283   &  0.049559432 \\\\\n",
       "\ttexture\\_se & -0.097317443  &  0.386357623  & -0.086761078  & -0.066280214  &  0.06840645   & 0.04620483    & 0.07621835    & 0.02147958    & 0.12805293    &  0.1641739659 & ...           & -0.11169031   &  0.409002766  & -0.102241922  & -0.08319499   & -0.07365766   & -0.09243935   & -0.06895622   & -0.1196375    & -0.12821476   & -0.045654569 \\\\\n",
       "\tperimeter\\_se &  0.674171616  &  0.281673115  &  0.693134890  &  0.726628328  &  0.29609193   & 0.54890526    & 0.66039079    & 0.71064987    & 0.31389276    &  0.0398299316 & ...           &  0.69720059   &  0.200370854  &  0.721031310  &  0.73071297   &  0.13005439   &  0.34191945   &  0.41889882   &  0.5548972    &  0.10993043   &  0.085432572 \\\\\n",
       "\tarea\\_se &  0.735863663  &  0.259844987  &  0.744982694  &  0.800085921  &  0.24655243   & 0.45565285    & 0.61742681    & 0.69029854    & 0.22397022    & -0.0901702475 & ...           &  0.75737319   &  0.196496649  &  0.761212636  &  0.81140796   &  0.12538943   &  0.28325654   &  0.38510014   &  0.5381663    &  0.07412629   &  0.017539295 \\\\\n",
       "\tsmoothness\\_se & -0.222600125  &  0.006613777  & -0.202694026  & -0.166776667  &  0.33237544   & 0.13529927    & 0.09856375    & 0.02765331    & 0.18732117    &  0.4019644254 & ...           & -0.23069071   & -0.074742965  & -0.217303755  & -0.18219548   &  0.31445746   & -0.05555814   & -0.05829839   & -0.1020068    & -0.10734210   &  0.101480315 \\\\\n",
       "\tcompactness\\_se &  0.205999980  &  0.191974611  &  0.250743681  &  0.212582551  &  0.31894330   & 0.73872179    & 0.67027882    & 0.49042425    & 0.42165915    &  0.5598366906 & ...           &  0.20460717   &  0.143002583  &  0.260515840  &  0.19937133   &  0.22739423   &  0.67878035   &  0.63914670   &  0.4832083    &  0.27787843   &  0.590972763 \\\\\n",
       "\tconcavity\\_se &  0.194203623  &  0.143293077  &  0.228082345  &  0.207660060  &  0.24839568   & 0.57051687    & 0.69127021    & 0.43916707    & 0.34262702    &  0.4466303217 & ...           &  0.18690352   &  0.100240984  &  0.226680426  &  0.18835265   &  0.16848132   &  0.48485780   &  0.66256413   &  0.4404723    &  0.19778782   &  0.439329269 \\\\\n",
       "\tconcave.points\\_se &  0.376168956  &  0.163851025  &  0.407216916  &  0.372320282  &  0.38067569   & 0.64226185    & 0.68325992    & 0.61563413    & 0.39329787    &  0.3411980444 & ...           &  0.35812667   &  0.086741210  &  0.394999252  &  0.34227116   &  0.21535060   &  0.45288838   &  0.54959238   &  0.6024496    &  0.14311567   &  0.310654551 \\\\\n",
       "\tsymmetry\\_se & -0.104320881  &  0.009127168  & -0.081629327  & -0.072496588  &  0.20077438   & 0.22997659    & 0.17800921    & 0.09535079    & 0.44913654    &  0.3450073971 & ...           & -0.12812077   & -0.077473420  & -0.103753044  & -0.11034274   & -0.01266180   &  0.06025488   &  0.03711905   & -0.0304134    &  0.38940248   &  0.078079476 \\\\\n",
       "\tfractal\\_dimension\\_se & -0.042641269  &  0.054457520  & -0.005523391  & -0.019886963  &  0.28360670   & 0.50731813    & 0.44930075    & 0.25758375    & 0.33178615    &  0.6881315775 & ...           & -0.03748762   & -0.003195029  & -0.001000398  & -0.02273615   &  0.17056832   &  0.39015884   &  0.37997466   &  0.2152040    &  0.11109396   &  0.591328066 \\\\\n",
       "\tradius\\_worst &  0.969538973  &  0.352572947  &  0.969476363  &  0.962746086  &  0.21312014   & 0.53531540    & 0.68823641    & 0.83031763    & 0.18572775    & -0.2536914949 & ...           &  1.00000000   &  0.359920754  &  0.993707916  &  0.98401456   &  0.21657443   &  0.47582004   &  0.57397471   &  0.7874239    &  0.24352920   &  0.093491979 \\\\\n",
       "\ttexture\\_worst &  0.297007644  &  0.912044589  &  0.303038372  &  0.287488627  &  0.03607180   & 0.24813283    & 0.29987889    & 0.29275171    & 0.09065069    & -0.0512692020 & ...           &  0.35992075   &  1.000000000  &  0.365098245  &  0.34584228   &  0.22542941   &  0.36083234   &  0.36836561   &  0.3597546    &  0.23302746   &  0.219122425 \\\\\n",
       "\tperimeter\\_worst &  0.965136514  &  0.358039575  &  0.970386887  &  0.959119574  &  0.23885263   & 0.59021043    & 0.72956492    & 0.85592313    & 0.21916856    & -0.2051512113 & ...           &  0.99370792   &  0.365098245  &  1.000000000  &  0.97757809   &  0.23677460   &  0.52940769   &  0.61834408   &  0.8163221    &  0.26949277   &  0.138956862 \\\\\n",
       "\tarea\\_worst &  0.941082460  &  0.343545947  &  0.941549808  &  0.959213326  &  0.20671836   & 0.50960381    & 0.67598723    & 0.80962962    & 0.17719338    & -0.2318544512 & ...           &  0.98401456   &  0.345842283  &  0.977578091  &  1.00000000   &  0.20914533   &  0.43829628   &  0.54333053   &  0.7474188    &  0.20914551   &  0.079647034 \\\\\n",
       "\tsmoothness\\_worst &  0.119616140  &  0.077503359  &  0.150549404  &  0.123522939  &  0.80532420   & 0.56554117    & 0.44882204    & 0.45275305    & 0.42667503    &  0.5049420754 & ...           &  0.21657443   &  0.225429415  &  0.236774604  &  0.20914533   &  1.00000000   &  0.56818652   &  0.51852329   &  0.5476909    &  0.49383833   &  0.617624192 \\\\\n",
       "\tcompactness\\_worst &  0.413462823  &  0.277829592  &  0.455774228  &  0.390410309  &  0.47246844   & 0.86580904    & 0.75496802    & 0.66745368    & 0.47320001    &  0.4587981567 & ...           &  0.47582004   &  0.360832339  &  0.529407690  &  0.43829628   &  0.56818652   &  1.00000000   &  0.89226090   &  0.8010804    &  0.61444050   &  0.810454856 \\\\\n",
       "\tconcavity\\_worst &  0.526911462  &  0.301025224  &  0.563879263  &  0.512605920  &  0.43492571   & 0.81627525    & 0.88410264    & 0.75239950    & 0.43372101    &  0.3462338763 & ...           &  0.57397471   &  0.368365607  &  0.618344080  &  0.54333053   &  0.51852329   &  0.89226090   &  1.00000000   &  0.8554339    &  0.53251973   &  0.686510921 \\\\\n",
       "\tconcave.points\\_worst &  0.744214198  &  0.295315843  &  0.771240789  &  0.722016626  &  0.50305335   & 0.81557322    & 0.86132303    & 0.91015531    & 0.43029661    &  0.1753254492 & ...           &  0.78742385   &  0.359754610  &  0.816322102  &  0.74741880   &  0.54769090   &  0.80108036   &  0.85543386   &  1.0000000    &  0.50252849   &  0.511114146 \\\\\n",
       "\tsymmetry\\_worst &  0.163953335  &  0.105007910  &  0.189115040  &  0.143569914  &  0.39430948   & 0.51022343    & 0.40946413    & 0.37574415    & 0.69982580    &  0.3340186839 & ...           &  0.24352920   &  0.233027461  &  0.269492769  &  0.20914551   &  0.49383833   &  0.61444050   &  0.53251973   &  0.5025285    &  1.00000000   &  0.537848206 \\\\\n",
       "\tfractal\\_dimension\\_worst &  0.007065886  &  0.119205351  &  0.051018530  &  0.003737597  &  0.49931637   & 0.68738232    & 0.51492989    & 0.36866113    & 0.43841350    &  0.7672967792 & ...           &  0.09349198   &  0.219122425  &  0.138956862  &  0.07964703   &  0.61762419   &  0.81045486   &  0.68651092   &  0.5111141    &  0.53784821   &  1.000000000 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | radius_mean | texture_mean | perimeter_mean | area_mean | smoothness_mean | compactness_mean | concavity_mean | concave.points_mean | symmetry_mean | fractal_dimension_mean | ... | radius_worst | texture_worst | perimeter_worst | area_worst | smoothness_worst | compactness_worst | concavity_worst | concave.points_worst | symmetry_worst | fractal_dimension_worst |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| radius_mean |  1.000000000  |  0.323781891  |  0.997855281  |  0.987357170  |  0.17058119   | 0.50612358    | 0.67676355    | 0.82252852    | 0.14774124    | -0.3116308263 | ...           |  0.96953897   |  0.297007644  |  0.965136514  |  0.94108246   |  0.11961614   |  0.41346282   |  0.52691146   |  0.7442142    |  0.16395333   |  0.007065886  |\n",
       "| texture_mean |  0.323781891  |  1.000000000  |  0.329533059  |  0.321085696  | -0.02338852   | 0.23670222    | 0.30241783    | 0.29346405    | 0.07140098    | -0.0764371834 | ...           |  0.35257295   |  0.912044589  |  0.358039575  |  0.34354595   |  0.07750336   |  0.27782959   |  0.30102522   |  0.2953158    |  0.10500791   |  0.119205351  |\n",
       "| perimeter_mean |  0.997855281  |  0.329533059  |  1.000000000  |  0.986506804  |  0.20727816   | 0.55693621    | 0.71613565    | 0.85097704    | 0.18302721    | -0.2614769081 | ...           |  0.96947636   |  0.303038372  |  0.970386887  |  0.94154981   |  0.15054940   |  0.45577423   |  0.56387926   |  0.7712408    |  0.18911504   |  0.051018530  |\n",
       "| area_mean |  0.987357170  |  0.321085696  |  0.986506804  |  1.000000000  |  0.17702838   | 0.49850168    | 0.68598283    | 0.82326887    | 0.15129308    | -0.2831098117 | ...           |  0.96274609   |  0.287488627  |  0.959119574  |  0.95921333   |  0.12352294   |  0.39041031   |  0.51260592   |  0.7220166    |  0.14356991   |  0.003737597  |\n",
       "| smoothness_mean |  0.170581187  | -0.023388516  |  0.207278164  |  0.177028377  |  1.00000000   | 0.65912322    | 0.52198377    | 0.55369517    | 0.55777479    |  0.5847920019 | ...           |  0.21312014   |  0.036071799  |  0.238852626  |  0.20671836   |  0.80532420   |  0.47246844   |  0.43492571   |  0.5030534    |  0.39430948   |  0.499316369  |\n",
       "| compactness_mean |  0.506123578  |  0.236702222  |  0.556936211  |  0.498501682  |  0.65912322   | 1.00000000    | 0.88312067    | 0.83113504    | 0.60264105    |  0.5653686634 | ...           |  0.53531540   |  0.248132833  |  0.590210428  |  0.50960381   |  0.56554117   |  0.86580904   |  0.81627525   |  0.8155732    |  0.51022343   |  0.687382323  |\n",
       "| concavity_mean |  0.676763550  |  0.302417828  |  0.716135650  |  0.685982829  |  0.52198377   | 0.88312067    | 1.00000000    | 0.92139103    | 0.50066662    |  0.3367833594 | ...           |  0.68823641   |  0.299878889  |  0.729564917  |  0.67598723   |  0.44882204   |  0.75496802   |  0.88410264   |  0.8613230    |  0.40946413   |  0.514929891  |\n",
       "| concave.points_mean |  0.822528522  |  0.293464051  |  0.850977041  |  0.823268869  |  0.55369517   | 0.83113504    | 0.92139103    | 1.00000000    | 0.46249739    |  0.1669173832 | ...           |  0.83031763   |  0.292751713  |  0.855923128  |  0.80962962   |  0.45275305   |  0.66745368   |  0.75239950   |  0.9101553    |  0.37574415   |  0.368661134  |\n",
       "| symmetry_mean |  0.147741242  |  0.071400980  |  0.183027212  |  0.151293079  |  0.55777479   | 0.60264105    | 0.50066662    | 0.46249739    | 1.00000000    |  0.4799213301 | ...           |  0.18572775   |  0.090650688  |  0.219168559  |  0.17719338   |  0.42667503   |  0.47320001   |  0.43372101   |  0.4302966    |  0.69982580   |  0.438413498  |\n",
       "| fractal_dimension_mean | -0.311630826  | -0.076437183  | -0.261476908  | -0.283109812  |  0.58479200   | 0.56536866    | 0.33678336    | 0.16691738    | 0.47992133    |  1.0000000000 | ...           | -0.25369149   | -0.051269202  | -0.205151211  | -0.23185445   |  0.50494208   |  0.45879816   |  0.34623388   |  0.1753254    |  0.33401868   |  0.767296779  |\n",
       "| radius_se |  0.679090388  |  0.275868676  |  0.691765014  |  0.732562227  |  0.30146710   | 0.49747345    | 0.63192482    | 0.69804983    | 0.30337926    |  0.0001109951 | ...           |  0.71506520   |  0.194798557  |  0.719683804  |  0.75154848   |  0.14191855   |  0.28710317   |  0.38058463   |  0.5310623    |  0.09454283   |  0.049559432  |\n",
       "| texture_se | -0.097317443  |  0.386357623  | -0.086761078  | -0.066280214  |  0.06840645   | 0.04620483    | 0.07621835    | 0.02147958    | 0.12805293    |  0.1641739659 | ...           | -0.11169031   |  0.409002766  | -0.102241922  | -0.08319499   | -0.07365766   | -0.09243935   | -0.06895622   | -0.1196375    | -0.12821476   | -0.045654569  |\n",
       "| perimeter_se |  0.674171616  |  0.281673115  |  0.693134890  |  0.726628328  |  0.29609193   | 0.54890526    | 0.66039079    | 0.71064987    | 0.31389276    |  0.0398299316 | ...           |  0.69720059   |  0.200370854  |  0.721031310  |  0.73071297   |  0.13005439   |  0.34191945   |  0.41889882   |  0.5548972    |  0.10993043   |  0.085432572  |\n",
       "| area_se |  0.735863663  |  0.259844987  |  0.744982694  |  0.800085921  |  0.24655243   | 0.45565285    | 0.61742681    | 0.69029854    | 0.22397022    | -0.0901702475 | ...           |  0.75737319   |  0.196496649  |  0.761212636  |  0.81140796   |  0.12538943   |  0.28325654   |  0.38510014   |  0.5381663    |  0.07412629   |  0.017539295  |\n",
       "| smoothness_se | -0.222600125  |  0.006613777  | -0.202694026  | -0.166776667  |  0.33237544   | 0.13529927    | 0.09856375    | 0.02765331    | 0.18732117    |  0.4019644254 | ...           | -0.23069071   | -0.074742965  | -0.217303755  | -0.18219548   |  0.31445746   | -0.05555814   | -0.05829839   | -0.1020068    | -0.10734210   |  0.101480315  |\n",
       "| compactness_se |  0.205999980  |  0.191974611  |  0.250743681  |  0.212582551  |  0.31894330   | 0.73872179    | 0.67027882    | 0.49042425    | 0.42165915    |  0.5598366906 | ...           |  0.20460717   |  0.143002583  |  0.260515840  |  0.19937133   |  0.22739423   |  0.67878035   |  0.63914670   |  0.4832083    |  0.27787843   |  0.590972763  |\n",
       "| concavity_se |  0.194203623  |  0.143293077  |  0.228082345  |  0.207660060  |  0.24839568   | 0.57051687    | 0.69127021    | 0.43916707    | 0.34262702    |  0.4466303217 | ...           |  0.18690352   |  0.100240984  |  0.226680426  |  0.18835265   |  0.16848132   |  0.48485780   |  0.66256413   |  0.4404723    |  0.19778782   |  0.439329269  |\n",
       "| concave.points_se |  0.376168956  |  0.163851025  |  0.407216916  |  0.372320282  |  0.38067569   | 0.64226185    | 0.68325992    | 0.61563413    | 0.39329787    |  0.3411980444 | ...           |  0.35812667   |  0.086741210  |  0.394999252  |  0.34227116   |  0.21535060   |  0.45288838   |  0.54959238   |  0.6024496    |  0.14311567   |  0.310654551  |\n",
       "| symmetry_se | -0.104320881  |  0.009127168  | -0.081629327  | -0.072496588  |  0.20077438   | 0.22997659    | 0.17800921    | 0.09535079    | 0.44913654    |  0.3450073971 | ...           | -0.12812077   | -0.077473420  | -0.103753044  | -0.11034274   | -0.01266180   |  0.06025488   |  0.03711905   | -0.0304134    |  0.38940248   |  0.078079476  |\n",
       "| fractal_dimension_se | -0.042641269  |  0.054457520  | -0.005523391  | -0.019886963  |  0.28360670   | 0.50731813    | 0.44930075    | 0.25758375    | 0.33178615    |  0.6881315775 | ...           | -0.03748762   | -0.003195029  | -0.001000398  | -0.02273615   |  0.17056832   |  0.39015884   |  0.37997466   |  0.2152040    |  0.11109396   |  0.591328066  |\n",
       "| radius_worst |  0.969538973  |  0.352572947  |  0.969476363  |  0.962746086  |  0.21312014   | 0.53531540    | 0.68823641    | 0.83031763    | 0.18572775    | -0.2536914949 | ...           |  1.00000000   |  0.359920754  |  0.993707916  |  0.98401456   |  0.21657443   |  0.47582004   |  0.57397471   |  0.7874239    |  0.24352920   |  0.093491979  |\n",
       "| texture_worst |  0.297007644  |  0.912044589  |  0.303038372  |  0.287488627  |  0.03607180   | 0.24813283    | 0.29987889    | 0.29275171    | 0.09065069    | -0.0512692020 | ...           |  0.35992075   |  1.000000000  |  0.365098245  |  0.34584228   |  0.22542941   |  0.36083234   |  0.36836561   |  0.3597546    |  0.23302746   |  0.219122425  |\n",
       "| perimeter_worst |  0.965136514  |  0.358039575  |  0.970386887  |  0.959119574  |  0.23885263   | 0.59021043    | 0.72956492    | 0.85592313    | 0.21916856    | -0.2051512113 | ...           |  0.99370792   |  0.365098245  |  1.000000000  |  0.97757809   |  0.23677460   |  0.52940769   |  0.61834408   |  0.8163221    |  0.26949277   |  0.138956862  |\n",
       "| area_worst |  0.941082460  |  0.343545947  |  0.941549808  |  0.959213326  |  0.20671836   | 0.50960381    | 0.67598723    | 0.80962962    | 0.17719338    | -0.2318544512 | ...           |  0.98401456   |  0.345842283  |  0.977578091  |  1.00000000   |  0.20914533   |  0.43829628   |  0.54333053   |  0.7474188    |  0.20914551   |  0.079647034  |\n",
       "| smoothness_worst |  0.119616140  |  0.077503359  |  0.150549404  |  0.123522939  |  0.80532420   | 0.56554117    | 0.44882204    | 0.45275305    | 0.42667503    |  0.5049420754 | ...           |  0.21657443   |  0.225429415  |  0.236774604  |  0.20914533   |  1.00000000   |  0.56818652   |  0.51852329   |  0.5476909    |  0.49383833   |  0.617624192  |\n",
       "| compactness_worst |  0.413462823  |  0.277829592  |  0.455774228  |  0.390410309  |  0.47246844   | 0.86580904    | 0.75496802    | 0.66745368    | 0.47320001    |  0.4587981567 | ...           |  0.47582004   |  0.360832339  |  0.529407690  |  0.43829628   |  0.56818652   |  1.00000000   |  0.89226090   |  0.8010804    |  0.61444050   |  0.810454856  |\n",
       "| concavity_worst |  0.526911462  |  0.301025224  |  0.563879263  |  0.512605920  |  0.43492571   | 0.81627525    | 0.88410264    | 0.75239950    | 0.43372101    |  0.3462338763 | ...           |  0.57397471   |  0.368365607  |  0.618344080  |  0.54333053   |  0.51852329   |  0.89226090   |  1.00000000   |  0.8554339    |  0.53251973   |  0.686510921  |\n",
       "| concave.points_worst |  0.744214198  |  0.295315843  |  0.771240789  |  0.722016626  |  0.50305335   | 0.81557322    | 0.86132303    | 0.91015531    | 0.43029661    |  0.1753254492 | ...           |  0.78742385   |  0.359754610  |  0.816322102  |  0.74741880   |  0.54769090   |  0.80108036   |  0.85543386   |  1.0000000    |  0.50252849   |  0.511114146  |\n",
       "| symmetry_worst |  0.163953335  |  0.105007910  |  0.189115040  |  0.143569914  |  0.39430948   | 0.51022343    | 0.40946413    | 0.37574415    | 0.69982580    |  0.3340186839 | ...           |  0.24352920   |  0.233027461  |  0.269492769  |  0.20914551   |  0.49383833   |  0.61444050   |  0.53251973   |  0.5025285    |  1.00000000   |  0.537848206  |\n",
       "| fractal_dimension_worst |  0.007065886  |  0.119205351  |  0.051018530  |  0.003737597  |  0.49931637   | 0.68738232    | 0.51492989    | 0.36866113    | 0.43841350    |  0.7672967792 | ...           |  0.09349198   |  0.219122425  |  0.138956862  |  0.07964703   |  0.61762419   |  0.81045486   |  0.68651092   |  0.5111141    |  0.53784821   |  1.000000000  |\n",
       "\n"
      ],
      "text/plain": [
       "                        radius_mean  texture_mean perimeter_mean area_mean   \n",
       "radius_mean              1.000000000  0.323781891  0.997855281    0.987357170\n",
       "texture_mean             0.323781891  1.000000000  0.329533059    0.321085696\n",
       "perimeter_mean           0.997855281  0.329533059  1.000000000    0.986506804\n",
       "area_mean                0.987357170  0.321085696  0.986506804    1.000000000\n",
       "smoothness_mean          0.170581187 -0.023388516  0.207278164    0.177028377\n",
       "compactness_mean         0.506123578  0.236702222  0.556936211    0.498501682\n",
       "concavity_mean           0.676763550  0.302417828  0.716135650    0.685982829\n",
       "concave.points_mean      0.822528522  0.293464051  0.850977041    0.823268869\n",
       "symmetry_mean            0.147741242  0.071400980  0.183027212    0.151293079\n",
       "fractal_dimension_mean  -0.311630826 -0.076437183 -0.261476908   -0.283109812\n",
       "radius_se                0.679090388  0.275868676  0.691765014    0.732562227\n",
       "texture_se              -0.097317443  0.386357623 -0.086761078   -0.066280214\n",
       "perimeter_se             0.674171616  0.281673115  0.693134890    0.726628328\n",
       "area_se                  0.735863663  0.259844987  0.744982694    0.800085921\n",
       "smoothness_se           -0.222600125  0.006613777 -0.202694026   -0.166776667\n",
       "compactness_se           0.205999980  0.191974611  0.250743681    0.212582551\n",
       "concavity_se             0.194203623  0.143293077  0.228082345    0.207660060\n",
       "concave.points_se        0.376168956  0.163851025  0.407216916    0.372320282\n",
       "symmetry_se             -0.104320881  0.009127168 -0.081629327   -0.072496588\n",
       "fractal_dimension_se    -0.042641269  0.054457520 -0.005523391   -0.019886963\n",
       "radius_worst             0.969538973  0.352572947  0.969476363    0.962746086\n",
       "texture_worst            0.297007644  0.912044589  0.303038372    0.287488627\n",
       "perimeter_worst          0.965136514  0.358039575  0.970386887    0.959119574\n",
       "area_worst               0.941082460  0.343545947  0.941549808    0.959213326\n",
       "smoothness_worst         0.119616140  0.077503359  0.150549404    0.123522939\n",
       "compactness_worst        0.413462823  0.277829592  0.455774228    0.390410309\n",
       "concavity_worst          0.526911462  0.301025224  0.563879263    0.512605920\n",
       "concave.points_worst     0.744214198  0.295315843  0.771240789    0.722016626\n",
       "symmetry_worst           0.163953335  0.105007910  0.189115040    0.143569914\n",
       "fractal_dimension_worst  0.007065886  0.119205351  0.051018530    0.003737597\n",
       "                        smoothness_mean compactness_mean concavity_mean\n",
       "radius_mean              0.17058119     0.50612358       0.67676355    \n",
       "texture_mean            -0.02338852     0.23670222       0.30241783    \n",
       "perimeter_mean           0.20727816     0.55693621       0.71613565    \n",
       "area_mean                0.17702838     0.49850168       0.68598283    \n",
       "smoothness_mean          1.00000000     0.65912322       0.52198377    \n",
       "compactness_mean         0.65912322     1.00000000       0.88312067    \n",
       "concavity_mean           0.52198377     0.88312067       1.00000000    \n",
       "concave.points_mean      0.55369517     0.83113504       0.92139103    \n",
       "symmetry_mean            0.55777479     0.60264105       0.50066662    \n",
       "fractal_dimension_mean   0.58479200     0.56536866       0.33678336    \n",
       "radius_se                0.30146710     0.49747345       0.63192482    \n",
       "texture_se               0.06840645     0.04620483       0.07621835    \n",
       "perimeter_se             0.29609193     0.54890526       0.66039079    \n",
       "area_se                  0.24655243     0.45565285       0.61742681    \n",
       "smoothness_se            0.33237544     0.13529927       0.09856375    \n",
       "compactness_se           0.31894330     0.73872179       0.67027882    \n",
       "concavity_se             0.24839568     0.57051687       0.69127021    \n",
       "concave.points_se        0.38067569     0.64226185       0.68325992    \n",
       "symmetry_se              0.20077438     0.22997659       0.17800921    \n",
       "fractal_dimension_se     0.28360670     0.50731813       0.44930075    \n",
       "radius_worst             0.21312014     0.53531540       0.68823641    \n",
       "texture_worst            0.03607180     0.24813283       0.29987889    \n",
       "perimeter_worst          0.23885263     0.59021043       0.72956492    \n",
       "area_worst               0.20671836     0.50960381       0.67598723    \n",
       "smoothness_worst         0.80532420     0.56554117       0.44882204    \n",
       "compactness_worst        0.47246844     0.86580904       0.75496802    \n",
       "concavity_worst          0.43492571     0.81627525       0.88410264    \n",
       "concave.points_worst     0.50305335     0.81557322       0.86132303    \n",
       "symmetry_worst           0.39430948     0.51022343       0.40946413    \n",
       "fractal_dimension_worst  0.49931637     0.68738232       0.51492989    \n",
       "                        concave.points_mean symmetry_mean\n",
       "radius_mean             0.82252852          0.14774124   \n",
       "texture_mean            0.29346405          0.07140098   \n",
       "perimeter_mean          0.85097704          0.18302721   \n",
       "area_mean               0.82326887          0.15129308   \n",
       "smoothness_mean         0.55369517          0.55777479   \n",
       "compactness_mean        0.83113504          0.60264105   \n",
       "concavity_mean          0.92139103          0.50066662   \n",
       "concave.points_mean     1.00000000          0.46249739   \n",
       "symmetry_mean           0.46249739          1.00000000   \n",
       "fractal_dimension_mean  0.16691738          0.47992133   \n",
       "radius_se               0.69804983          0.30337926   \n",
       "texture_se              0.02147958          0.12805293   \n",
       "perimeter_se            0.71064987          0.31389276   \n",
       "area_se                 0.69029854          0.22397022   \n",
       "smoothness_se           0.02765331          0.18732117   \n",
       "compactness_se          0.49042425          0.42165915   \n",
       "concavity_se            0.43916707          0.34262702   \n",
       "concave.points_se       0.61563413          0.39329787   \n",
       "symmetry_se             0.09535079          0.44913654   \n",
       "fractal_dimension_se    0.25758375          0.33178615   \n",
       "radius_worst            0.83031763          0.18572775   \n",
       "texture_worst           0.29275171          0.09065069   \n",
       "perimeter_worst         0.85592313          0.21916856   \n",
       "area_worst              0.80962962          0.17719338   \n",
       "smoothness_worst        0.45275305          0.42667503   \n",
       "compactness_worst       0.66745368          0.47320001   \n",
       "concavity_worst         0.75239950          0.43372101   \n",
       "concave.points_worst    0.91015531          0.43029661   \n",
       "symmetry_worst          0.37574415          0.69982580   \n",
       "fractal_dimension_worst 0.36866113          0.43841350   \n",
       "                        fractal_dimension_mean ... radius_worst texture_worst\n",
       "radius_mean             -0.3116308263          ...  0.96953897   0.297007644 \n",
       "texture_mean            -0.0764371834          ...  0.35257295   0.912044589 \n",
       "perimeter_mean          -0.2614769081          ...  0.96947636   0.303038372 \n",
       "area_mean               -0.2831098117          ...  0.96274609   0.287488627 \n",
       "smoothness_mean          0.5847920019          ...  0.21312014   0.036071799 \n",
       "compactness_mean         0.5653686634          ...  0.53531540   0.248132833 \n",
       "concavity_mean           0.3367833594          ...  0.68823641   0.299878889 \n",
       "concave.points_mean      0.1669173832          ...  0.83031763   0.292751713 \n",
       "symmetry_mean            0.4799213301          ...  0.18572775   0.090650688 \n",
       "fractal_dimension_mean   1.0000000000          ... -0.25369149  -0.051269202 \n",
       "radius_se                0.0001109951          ...  0.71506520   0.194798557 \n",
       "texture_se               0.1641739659          ... -0.11169031   0.409002766 \n",
       "perimeter_se             0.0398299316          ...  0.69720059   0.200370854 \n",
       "area_se                 -0.0901702475          ...  0.75737319   0.196496649 \n",
       "smoothness_se            0.4019644254          ... -0.23069071  -0.074742965 \n",
       "compactness_se           0.5598366906          ...  0.20460717   0.143002583 \n",
       "concavity_se             0.4466303217          ...  0.18690352   0.100240984 \n",
       "concave.points_se        0.3411980444          ...  0.35812667   0.086741210 \n",
       "symmetry_se              0.3450073971          ... -0.12812077  -0.077473420 \n",
       "fractal_dimension_se     0.6881315775          ... -0.03748762  -0.003195029 \n",
       "radius_worst            -0.2536914949          ...  1.00000000   0.359920754 \n",
       "texture_worst           -0.0512692020          ...  0.35992075   1.000000000 \n",
       "perimeter_worst         -0.2051512113          ...  0.99370792   0.365098245 \n",
       "area_worst              -0.2318544512          ...  0.98401456   0.345842283 \n",
       "smoothness_worst         0.5049420754          ...  0.21657443   0.225429415 \n",
       "compactness_worst        0.4587981567          ...  0.47582004   0.360832339 \n",
       "concavity_worst          0.3462338763          ...  0.57397471   0.368365607 \n",
       "concave.points_worst     0.1753254492          ...  0.78742385   0.359754610 \n",
       "symmetry_worst           0.3340186839          ...  0.24352920   0.233027461 \n",
       "fractal_dimension_worst  0.7672967792          ...  0.09349198   0.219122425 \n",
       "                        perimeter_worst area_worst  smoothness_worst\n",
       "radius_mean              0.965136514     0.94108246  0.11961614     \n",
       "texture_mean             0.358039575     0.34354595  0.07750336     \n",
       "perimeter_mean           0.970386887     0.94154981  0.15054940     \n",
       "area_mean                0.959119574     0.95921333  0.12352294     \n",
       "smoothness_mean          0.238852626     0.20671836  0.80532420     \n",
       "compactness_mean         0.590210428     0.50960381  0.56554117     \n",
       "concavity_mean           0.729564917     0.67598723  0.44882204     \n",
       "concave.points_mean      0.855923128     0.80962962  0.45275305     \n",
       "symmetry_mean            0.219168559     0.17719338  0.42667503     \n",
       "fractal_dimension_mean  -0.205151211    -0.23185445  0.50494208     \n",
       "radius_se                0.719683804     0.75154848  0.14191855     \n",
       "texture_se              -0.102241922    -0.08319499 -0.07365766     \n",
       "perimeter_se             0.721031310     0.73071297  0.13005439     \n",
       "area_se                  0.761212636     0.81140796  0.12538943     \n",
       "smoothness_se           -0.217303755    -0.18219548  0.31445746     \n",
       "compactness_se           0.260515840     0.19937133  0.22739423     \n",
       "concavity_se             0.226680426     0.18835265  0.16848132     \n",
       "concave.points_se        0.394999252     0.34227116  0.21535060     \n",
       "symmetry_se             -0.103753044    -0.11034274 -0.01266180     \n",
       "fractal_dimension_se    -0.001000398    -0.02273615  0.17056832     \n",
       "radius_worst             0.993707916     0.98401456  0.21657443     \n",
       "texture_worst            0.365098245     0.34584228  0.22542941     \n",
       "perimeter_worst          1.000000000     0.97757809  0.23677460     \n",
       "area_worst               0.977578091     1.00000000  0.20914533     \n",
       "smoothness_worst         0.236774604     0.20914533  1.00000000     \n",
       "compactness_worst        0.529407690     0.43829628  0.56818652     \n",
       "concavity_worst          0.618344080     0.54333053  0.51852329     \n",
       "concave.points_worst     0.816322102     0.74741880  0.54769090     \n",
       "symmetry_worst           0.269492769     0.20914551  0.49383833     \n",
       "fractal_dimension_worst  0.138956862     0.07964703  0.61762419     \n",
       "                        compactness_worst concavity_worst concave.points_worst\n",
       "radius_mean              0.41346282        0.52691146      0.7442142          \n",
       "texture_mean             0.27782959        0.30102522      0.2953158          \n",
       "perimeter_mean           0.45577423        0.56387926      0.7712408          \n",
       "area_mean                0.39041031        0.51260592      0.7220166          \n",
       "smoothness_mean          0.47246844        0.43492571      0.5030534          \n",
       "compactness_mean         0.86580904        0.81627525      0.8155732          \n",
       "concavity_mean           0.75496802        0.88410264      0.8613230          \n",
       "concave.points_mean      0.66745368        0.75239950      0.9101553          \n",
       "symmetry_mean            0.47320001        0.43372101      0.4302966          \n",
       "fractal_dimension_mean   0.45879816        0.34623388      0.1753254          \n",
       "radius_se                0.28710317        0.38058463      0.5310623          \n",
       "texture_se              -0.09243935       -0.06895622     -0.1196375          \n",
       "perimeter_se             0.34191945        0.41889882      0.5548972          \n",
       "area_se                  0.28325654        0.38510014      0.5381663          \n",
       "smoothness_se           -0.05555814       -0.05829839     -0.1020068          \n",
       "compactness_se           0.67878035        0.63914670      0.4832083          \n",
       "concavity_se             0.48485780        0.66256413      0.4404723          \n",
       "concave.points_se        0.45288838        0.54959238      0.6024496          \n",
       "symmetry_se              0.06025488        0.03711905     -0.0304134          \n",
       "fractal_dimension_se     0.39015884        0.37997466      0.2152040          \n",
       "radius_worst             0.47582004        0.57397471      0.7874239          \n",
       "texture_worst            0.36083234        0.36836561      0.3597546          \n",
       "perimeter_worst          0.52940769        0.61834408      0.8163221          \n",
       "area_worst               0.43829628        0.54333053      0.7474188          \n",
       "smoothness_worst         0.56818652        0.51852329      0.5476909          \n",
       "compactness_worst        1.00000000        0.89226090      0.8010804          \n",
       "concavity_worst          0.89226090        1.00000000      0.8554339          \n",
       "concave.points_worst     0.80108036        0.85543386      1.0000000          \n",
       "symmetry_worst           0.61444050        0.53251973      0.5025285          \n",
       "fractal_dimension_worst  0.81045486        0.68651092      0.5111141          \n",
       "                        symmetry_worst fractal_dimension_worst\n",
       "radius_mean              0.16395333     0.007065886           \n",
       "texture_mean             0.10500791     0.119205351           \n",
       "perimeter_mean           0.18911504     0.051018530           \n",
       "area_mean                0.14356991     0.003737597           \n",
       "smoothness_mean          0.39430948     0.499316369           \n",
       "compactness_mean         0.51022343     0.687382323           \n",
       "concavity_mean           0.40946413     0.514929891           \n",
       "concave.points_mean      0.37574415     0.368661134           \n",
       "symmetry_mean            0.69982580     0.438413498           \n",
       "fractal_dimension_mean   0.33401868     0.767296779           \n",
       "radius_se                0.09454283     0.049559432           \n",
       "texture_se              -0.12821476    -0.045654569           \n",
       "perimeter_se             0.10993043     0.085432572           \n",
       "area_se                  0.07412629     0.017539295           \n",
       "smoothness_se           -0.10734210     0.101480315           \n",
       "compactness_se           0.27787843     0.590972763           \n",
       "concavity_se             0.19778782     0.439329269           \n",
       "concave.points_se        0.14311567     0.310654551           \n",
       "symmetry_se              0.38940248     0.078079476           \n",
       "fractal_dimension_se     0.11109396     0.591328066           \n",
       "radius_worst             0.24352920     0.093491979           \n",
       "texture_worst            0.23302746     0.219122425           \n",
       "perimeter_worst          0.26949277     0.138956862           \n",
       "area_worst               0.20914551     0.079647034           \n",
       "smoothness_worst         0.49383833     0.617624192           \n",
       "compactness_worst        0.61444050     0.810454856           \n",
       "concavity_worst          0.53251973     0.686510921           \n",
       "concave.points_worst     0.50252849     0.511114146           \n",
       "symmetry_worst           1.00000000     0.537848206           \n",
       "fractal_dimension_worst  0.53784821     1.000000000           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor.data = cor(df[,num.cols])\n",
    "cor.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "checking for the correlation of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Km/AtNteVtKTa9YrtI7nUBSaex55bwt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\nIIJEURFEkCgqgv4/2pAaVOQ8rQoAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(corrplot)\n",
    "corrplot(cor.data,method='color')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In the corrplot concave.points mean , perimeter mean and texture worst are correlated. so the tree took it for the classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(rpart)\n",
    "df = select(df, c(-id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping the ID column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(caTools)\n",
    "\n",
    "set.seed(101) \n",
    "\n",
    "sample = sample.split(df$diagnosis, SplitRatio = .70)\n",
    "train = subset(df, sample == TRUE)\n",
    "test = subset(df, sample == FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# splitting the data into train and test data using sample.split function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree <- rpart(diagnosis ~ .,method='class',data = train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using target variable diagnosis training the model using rpart function and we are doing classification so we are using method class and data is training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.preds <- predict(tree,test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the values using test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>B</th><th scope=col>M</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>11</th><td>0.0530303 </td><td>0.94696970</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>0.9788136 </td><td>0.02118644</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>0.0530303 </td><td>0.94696970</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>0.0530303 </td><td>0.94696970</td></tr>\n",
       "\t<tr><th scope=row>28</th><td>0.0530303 </td><td>0.94696970</td></tr>\n",
       "\t<tr><th scope=row>29</th><td>0.0530303 </td><td>0.94696970</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & B & M\\\\\n",
       "\\hline\n",
       "\t11 & 0.0530303  & 0.94696970\\\\\n",
       "\t14 & 0.9788136  & 0.02118644\\\\\n",
       "\t17 & 0.0530303  & 0.94696970\\\\\n",
       "\t25 & 0.0530303  & 0.94696970\\\\\n",
       "\t28 & 0.0530303  & 0.94696970\\\\\n",
       "\t29 & 0.0530303  & 0.94696970\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | B | M |\n",
       "|---|---|---|\n",
       "| 11 | 0.0530303  | 0.94696970 |\n",
       "| 14 | 0.9788136  | 0.02118644 |\n",
       "| 17 | 0.0530303  | 0.94696970 |\n",
       "| 25 | 0.0530303  | 0.94696970 |\n",
       "| 28 | 0.0530303  | 0.94696970 |\n",
       "| 29 | 0.0530303  | 0.94696970 |\n",
       "\n"
      ],
      "text/plain": [
       "   B         M         \n",
       "11 0.0530303 0.94696970\n",
       "14 0.9788136 0.02118644\n",
       "17 0.0530303 0.94696970\n",
       "25 0.0530303 0.94696970\n",
       "28 0.0530303 0.94696970\n",
       "29 0.0530303 0.94696970"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tree.preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.preds <- as.data.frame(tree.preds)\n",
    "joiner <- function(x){\n",
    "    if (x>0.5){\n",
    "        return('M')\n",
    "    }else{\n",
    "        return('B')\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# created a function joiner giving a threshold value of 0.5 to classify benign and malignant cases in the predicted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.preds$diagnosis <- sapply(tree.preds$M,joiner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>B</th><th scope=col>M</th><th scope=col>diagnosis</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>11</th><td>0.0530303 </td><td>0.94696970</td><td>M         </td></tr>\n",
       "\t<tr><th scope=row>14</th><td>0.9788136 </td><td>0.02118644</td><td>B         </td></tr>\n",
       "\t<tr><th scope=row>17</th><td>0.0530303 </td><td>0.94696970</td><td>M         </td></tr>\n",
       "\t<tr><th scope=row>25</th><td>0.0530303 </td><td>0.94696970</td><td>M         </td></tr>\n",
       "\t<tr><th scope=row>28</th><td>0.0530303 </td><td>0.94696970</td><td>M         </td></tr>\n",
       "\t<tr><th scope=row>29</th><td>0.0530303 </td><td>0.94696970</td><td>M         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       "  & B & M & diagnosis\\\\\n",
       "\\hline\n",
       "\t11 & 0.0530303  & 0.94696970 & M         \\\\\n",
       "\t14 & 0.9788136  & 0.02118644 & B         \\\\\n",
       "\t17 & 0.0530303  & 0.94696970 & M         \\\\\n",
       "\t25 & 0.0530303  & 0.94696970 & M         \\\\\n",
       "\t28 & 0.0530303  & 0.94696970 & M         \\\\\n",
       "\t29 & 0.0530303  & 0.94696970 & M         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | B | M | diagnosis |\n",
       "|---|---|---|---|\n",
       "| 11 | 0.0530303  | 0.94696970 | M          |\n",
       "| 14 | 0.9788136  | 0.02118644 | B          |\n",
       "| 17 | 0.0530303  | 0.94696970 | M          |\n",
       "| 25 | 0.0530303  | 0.94696970 | M          |\n",
       "| 28 | 0.0530303  | 0.94696970 | M          |\n",
       "| 29 | 0.0530303  | 0.94696970 | M          |\n",
       "\n"
      ],
      "text/plain": [
       "   B         M          diagnosis\n",
       "11 0.0530303 0.94696970 M        \n",
       "14 0.9788136 0.02118644 B        \n",
       "17 0.0530303 0.94696970 M        \n",
       "25 0.0530303 0.94696970 M        \n",
       "28 0.0530303 0.94696970 M        \n",
       "29 0.0530303 0.94696970 M        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tree.preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8fhjJ8jpeG+XtKiT4Z0+fC694FX7eJw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\noAAhAQUICShASEABQgIKEBJQgJCAAv8PmD4HobHDqUYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(tree.preds,aes(diagnosis)) + geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the graph for predicted values as there are more benign cases in the predicted data also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.preds$diagnosis = factor(tree.preds$diagnosis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   \n",
       "      B   M\n",
       "  B 102   6\n",
       "  M   5  58"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(tree.preds$diagnosis,test$diagnosis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "58 patients has actual cancer and model truly predicted cancer.\n",
    "102 patients doesnt actually have cancer and model truly predicted doesnot actually have cancer.\n",
    "6 actually doesnt have cancer but model wrongly predicted they had cancer.\n",
    "5 patients actually have cancer and but model predicted they doesnt have cancer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = 58\n",
    "TN = 102\n",
    "FP = 6\n",
    "FN = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.935672514619883"
      ],
      "text/latex": [
       "0.935672514619883"
      ],
      "text/markdown": [
       "0.935672514619883"
      ],
      "text/plain": [
       "[1] 0.9356725"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = (TP + TN) / (TP+TN+FN+FP)\n",
    "accuracy\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So the model accuracy got  93% so it can corectly classify the data between malignant and benign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.064327485380117"
      ],
      "text/latex": [
       "0.064327485380117"
      ],
      "text/markdown": [
       "0.064327485380117"
      ],
      "text/plain": [
       "[1] 0.06432749"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error.rate = (FP+FN) / (TP+TN+FN+FP)\n",
    "error.rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6% is the error rate of wrongly predicted values in the model that is false positive and false negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(rpart.plot)\n",
    "prp(tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If perimeter mean < 113 and concave.points mean < 0.14 then it is benign.\n",
    "\n",
    "If perimeter mean > 113 then it is malignant.\n",
    "\n",
    "If perimeter mean < 113 and concave.points mean > 0.14 and texture_worst > 20 then it is malignant.\n",
    "\n",
    "\n",
    "If perimeter mean < 113 and concave.points mean > 0.14 and texture_worst < 20 then it is malignant.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(pROC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "change = function(x){\n",
    "    if(x == 'M')\n",
    "        return(1)\n",
    "    else\n",
    "        return(0)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prob = sapply(tree.preds$diagnosis,change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+se3n/Gt1Ru/27h2VFdPq7aN/sL+tP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\nEBIggJAAAf8BzOIucLPuJFYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_roc=roc(test$diagnosis ~ test_prob,plot =TRUE,print.auc=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Area Under Curve got 93.0% in the model.So the score is 0.93. The perfect classifier is 1.So the model can classify 0.93 better between negative class (0) and positive class (1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(randomForest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.model <- randomForest(diagnosis ~ ., data = train,importance = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model using randomforest function target variable is diagnosis and the data is train and we are saving the model in the rf.model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "p <- predict(rf.model,test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting the model using predict function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   \n",
       "p     B   M\n",
       "  B 105   4\n",
       "  M   2  60"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(p,test$diagnosis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "60 patients has actual cancer and model truly predicted cancer.\n",
    "105 patients doesnt actually have cancer and model truly predicted doesnot actually have cancer.\n",
    "4 actually doesnt have cancer but model wrongly predicted they had cancer.\n",
    "2 patients actually have cancer and but model predicted they doesnt have cancer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = 60\n",
    "TN = 105\n",
    "FN = 2\n",
    "FP = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.964912280701754"
      ],
      "text/latex": [
       "0.964912280701754"
      ],
      "text/markdown": [
       "0.964912280701754"
      ],
      "text/plain": [
       "[1] 0.9649123"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = (TP + TN) / (TP+TN+FN+FP)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So the model accuracy got 96% so it can corectly classify the data between malignant and benign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0350877192982456"
      ],
      "text/latex": [
       "0.0350877192982456"
      ],
      "text/markdown": [
       "0.0350877192982456"
      ],
      "text/plain": [
       "[1] 0.03508772"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error.rate = (FP+FN) / (TP+TN+FN+FP)\n",
    "error.rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6% is the error rate of wrongly predicted values in the model that is false positive and false negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(pROC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prob = predict(rf.model,newdata = test, type =\"response\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "change = function(x){\n",
    "    if(x == 'M')\n",
    "        return(1)\n",
    "    else\n",
    "        return(0)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prob = sapply(test_prob,change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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88WhZAKMOroeDuw+PgQ0vK2jbR7uNTn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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_roc=roc(test$diagnosis ~ test_prob,plot =TRUE,print.auc=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Area Under Curve got 95.9% in the model.So the score is 0.96. The perfect classifier is 1.So the model can classify 0.96 better between negative class (0) and positive class (1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
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