classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1256,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "      ...       texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0     ...               17.33           184.60      2019.0            0.1622   \n",
       "1     ...               23.41           158.80      1956.0            0.1238   \n",
       "2     ...               25.53           152.50      1709.0            0.1444   \n",
       "3     ...               26.50            98.87       567.7            0.2098   \n",
       "4     ...               16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 1257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.690000e+02</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean     ...       texture_worst  perimeter_worst  \\\n",
       "count     569.000000     ...          569.000000       569.000000   \n",
       "mean        0.181162     ...           25.677223       107.261213   \n",
       "std         0.027414     ...            6.146258        33.602542   \n",
       "min         0.106000     ...           12.020000        50.410000   \n",
       "25%         0.161900     ...           21.080000        84.110000   \n",
       "50%         0.179200     ...           25.410000        97.660000   \n",
       "75%         0.195700     ...           29.720000       125.400000   \n",
       "max         0.304000     ...           49.540000       251.200000   \n",
       "\n",
       "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "count            569.000000      569.000000               569.000000   \n",
       "mean               0.114606        0.290076                 0.083946   \n",
       "std                0.065732        0.061867                 0.018061   \n",
       "min                0.000000        0.156500                 0.055040   \n",
       "25%                0.064930        0.250400                 0.071460   \n",
       "50%                0.099930        0.282200                 0.080040   \n",
       "75%                0.161400        0.317900                 0.092080   \n",
       "max                0.291000        0.663800                 0.207500   \n",
       "\n",
       "       Unnamed: 32  \n",
       "count          0.0  \n",
       "mean           NaN  \n",
       "std            NaN  \n",
       "min            NaN  \n",
       "25%            NaN  \n",
       "50%            NaN  \n",
       "75%            NaN  \n",
       "max            NaN  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 1258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1259,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 1260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['diagnosis'].unique() #The goal is to make a model for classification. The tumor will be recognized as \n",
    "#malignant or benign based on the characteristics (30 features in total). Here are the two classes we have got:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         False\n",
       "diagnosis                  False\n",
       "radius_mean                False\n",
       "texture_mean               False\n",
       "perimeter_mean             False\n",
       "area_mean                  False\n",
       "smoothness_mean            False\n",
       "compactness_mean           False\n",
       "concavity_mean             False\n",
       "concave points_mean        False\n",
       "symmetry_mean              False\n",
       "fractal_dimension_mean     False\n",
       "radius_se                  False\n",
       "texture_se                 False\n",
       "perimeter_se               False\n",
       "area_se                    False\n",
       "smoothness_se              False\n",
       "compactness_se             False\n",
       "concavity_se               False\n",
       "concave points_se          False\n",
       "symmetry_se                False\n",
       "fractal_dimension_se       False\n",
       "radius_worst               False\n",
       "texture_worst              False\n",
       "perimeter_worst            False\n",
       "area_worst                 False\n",
       "smoothness_worst           False\n",
       "compactness_worst          False\n",
       "concavity_worst            False\n",
       "concave points_worst       False\n",
       "symmetry_worst             False\n",
       "fractal_dimension_worst    False\n",
       "Unnamed: 32                 True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 1261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's prepare the data for analysis. Let's explore the data and check for missing values \n",
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569"
      ]
     },
     "execution_count": 1262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Unnamed: 32'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "            ...             radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           ...                    25.38          17.33           184.60   \n",
       "1           ...                    24.99          23.41           158.80   \n",
       "2           ...                    23.57          25.53           152.50   \n",
       "3           ...                    14.91          26.50            98.87   \n",
       "4           ...                    22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 1263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.drop('Unnamed: 32', 1) #droping the column with NaN values. Normaly this should be clarified \n",
    "#with those who provided data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569"
      ]
     },
     "execution_count": 1264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1265,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Splitting for comfy exploratory preview of the data\n",
    "df1 = df.iloc[:, 1:6]\n",
    "df2 = df.iloc[:, 6:12]\n",
    "df2['diagnosis']=df1['diagnosis']\n",
    "df3=df.iloc[:, 12:17]\n",
    "df3['diagnosis']=df1['diagnosis']\n",
    "df4=df.iloc[:, 17:23]\n",
    "df4['diagnosis']=df1['diagnosis']\n",
    "df5=df.iloc[:, 23:27]\n",
    "df5['diagnosis']=df1['diagnosis']\n",
    "df6=df.iloc[:, 27:32]\n",
    "df6['diagnosis']=df1['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness_se</th>\n",
       "      <th>concavity_se</th>\n",
       "      <th>concave points_se</th>\n",
       "      <th>symmetry_se</th>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>25.38</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>24.99</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>23.57</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>14.91</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>22.54</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness_se  concavity_se  concave points_se  symmetry_se  \\\n",
       "0         0.04904       0.05373            0.01587      0.03003   \n",
       "1         0.01308       0.01860            0.01340      0.01389   \n",
       "2         0.04006       0.03832            0.02058      0.02250   \n",
       "3         0.07458       0.05661            0.01867      0.05963   \n",
       "4         0.02461       0.05688            0.01885      0.01756   \n",
       "\n",
       "   fractal_dimension_se  radius_worst diagnosis  \n",
       "0              0.006193         25.38         M  \n",
       "1              0.003532         24.99         M  \n",
       "2              0.004571         23.57         M  \n",
       "3              0.009208         14.91         M  \n",
       "4              0.005115         22.54         M  "
      ]
     },
     "execution_count": 1266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1267,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1268,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop(\"id\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1269,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feat_mean= list(df.columns[1:11])\n",
    "feat_se= list(df.columns[11:20])\n",
    "feat_worst=list(df.columns[21:31])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19.0\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import sklearn\n",
    "print(sklearn.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1271,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train, test = train_test_split(df, test_size = 0.3)\n",
    "\n",
    "#training_input = train[inputs]# taking the training data input \n",
    "training_input = train.iloc[:, 1:32]\n",
    "training_output=train.diagnosis # This is output of our training data\n",
    "# same we have to do for test\n",
    "testing_input = test.iloc[:, 1:32]\n",
    "#testing_input= test[inputs] # taking test data inputs\n",
    "testing_output =test.diagnosis   #output value of test dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features: [ True False False False False False  True False False False False False\n",
      " False False False False False False False False  True False False False\n",
      " False False  True  True False False]\n",
      "Feature Ranking: [ 1  8 13 24 12 17  1  2  3 22 25  7 21 10 23 15 14 19 18 26  1  9  6 20 11\n",
      "  4  1  1 16  5]\n",
      "Selected features: Index(['radius_mean', 'concavity_mean', 'radius_worst', 'concavity_worst',\n",
      "       'concave points_worst'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Given an external estimator that assigns weights to features \n",
    "#(e.g., the coefficients of a linear model), the goal of recursive feature elimination (RFE) \n",
    "#is to select features by recursively considering smaller and smaller sets of features. \n",
    "\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "col = df.columns[1:]\n",
    "\n",
    "\n",
    "LR = LogisticRegression()\n",
    "\n",
    "selector = RFE(LR, 5, step=1)\n",
    "selector = selector.fit(training_input, training_output)\n",
    "index = np.where(selector.support_)[0]\n",
    "inp = col[index]\n",
    "\n",
    "train, test = train_test_split(df, test_size = 0.3)\n",
    "training_input = train[inp]# taking the training data input \n",
    "training_output=train.diagnosis # This is output of our training data\n",
    "# same we have to do for test\n",
    "testing_input= test[inp] # taking test data inputs\n",
    "testing_output =test.diagnosis   #output value of test dat\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(\"Selected Features: %s\" % selector.support_)\n",
    "print(\"Feature Ranking: %s\" % selector.ranking_)\n",
    "index = np.where(selector.support_)[0]\n",
    "print(\"Selected features: %s\" % col[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#All mean inputs\n",
    "inputs = ['area_mean','radius_mean','texture_mean','perimeter_mean','smoothness_mean','compactness_mean','symmetry_mean', 'concavity_mean', 'concave points_mean']\n",
    "#Reduced mean inputs based on exclusion of correlated factors\n",
    "inputs2 = ['radius_mean', 'symmetry_mean', 'texture_mean', 'compactness_mean']\n",
    "#5 Features selected by RFE method\n",
    "inputs4 = col[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1274,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train, test = train_test_split(df, test_size = 0.3)\n",
    "training_input = train[inputs4]# taking the training data input \n",
    "training_output=train.diagnosis # This is output of our training data\n",
    "# same we have to do for test\n",
    "testing_input= test[inputs4] # taking test data inputs\n",
    "testing_output =test.diagnosis   #output value of test dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1275,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#It is possible to tune parameters in order to use the optimal one providing best accuracy. \n",
    "#We will use gridSearchCV for that purpose.\n",
    "#From confusion_matrix we will get rates of predicted values (True negative rate, true positive rate, and so on)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best classifier accuracy score: 0.949748743718593\n",
      "Best parameters: {'criterion': 'gini', 'max_depth': 2, 'max_features': 4, 'splitter': 'best'}\n",
      "Cross_val: [ 0.9137931   0.84482759  0.87719298  0.92982456  0.89473684  0.89473684\n",
      "  0.9122807   0.94642857  0.94642857  0.94642857]\n",
      "False negative rate: 0.09090909090909091\n",
      "Overall accuracy: 0.9181286549707602\n",
      "Recall: 0.9181286549707602\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "DTC = DecisionTreeClassifier()\n",
    "\n",
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "                  'splitter': ['best', 'random'],\n",
    "                  'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "    \n",
    "\n",
    "grid_search = GridSearchCV(DTC,\n",
    "                           param_grid=parameters,\n",
    "                           cv=10)\n",
    "\n",
    "grid_search.fit(training_input, training_output)\n",
    "cv = cross_val_score(DTC, df[inputs4], df['diagnosis'], cv=10)\n",
    "\n",
    "DTC = DecisionTreeClassifier(**grid_search.best_params_)\n",
    "\n",
    "\n",
    "\n",
    "trained = DTC.fit(training_input, training_output)\n",
    "predicted = DTC.predict(testing_input)\n",
    "cm = confusion_matrix(testing_output, predicted)\n",
    "\n",
    "FP = cm[0][1]  \n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "TP = cm[0][0]\n",
    "\n",
    "# Recall/sensitivity/hit rate, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "# True negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "# False negative rate\n",
    "FNR_DTC = FN/(TP+FN)\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC_DTC = (TP+TN)/(TP+FP+FN+TN)\n",
    "recall = recall_score(testing_output, predicted, average='weighted') \n",
    "\n",
    "\n",
    "print('Best classifier accuracy score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "print('Cross_val: {}'.format(cv))\n",
    "print('False negative rate: {}'.format(FNR_DTC))\n",
    "print('Overall accuracy: {}'.format(ACC_DTC))\n",
    "print('Recall: {}'.format(recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9522613065326633\n",
      "Best parameters: {'criterion': 'gini', 'max_features': 1, 'n_estimators': 50, 'warm_start': False}\n",
      "Cross_val: [ 0.9137931   0.86206897  0.89473684  0.92982456  0.92982456  0.9122807\n",
      "  0.92982456  0.94642857  0.85714286  0.94642857]\n",
      "False negative rate: 0.06481481481481481\n",
      "Overall accuracy: 0.9415204678362573\n",
      "Recall: 0.9415204678362573\n"
     ]
    }
   ],
   "source": [
    "#Lets try Random forest classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RFC = RandomForestClassifier()\n",
    "\n",
    "parameters = {'n_estimators': [5, 10, 25, 50],\n",
    "                  'criterion': ['gini', 'entropy'],\n",
    "                  'max_features': [1, 2, 3, 4],\n",
    "                  'warm_start': [True, False]}\n",
    "\n",
    "\n",
    "grid_search = GridSearchCV(RFC,\n",
    "                           param_grid=parameters,\n",
    "                           cv=10)\n",
    "\n",
    "grid_search.fit(training_input, training_output)\n",
    "cv = cross_val_score(DTC, df[inputs4], df['diagnosis'], cv=10)\n",
    "RFC = RandomForestClassifier(**grid_search.best_params_)\n",
    "trained = RFC.fit(training_input, training_output)\n",
    "predicted = RFC.predict(testing_input)\n",
    "\n",
    "\n",
    "cm = confusion_matrix(testing_output, predicted)\n",
    "\n",
    "FP = cm[0][1]  \n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "TP = cm[0][0]\n",
    "\n",
    "# Recall/sensitivity/hit rate, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "# True negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "# False negative rate\n",
    "FNR_RFC = FN/(TP+FN)\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC_RFC = (TP+TN)/(TP+FP+FN+TN)\n",
    "recall = recall_score(testing_output, predicted, average='weighted') \n",
    "\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "print('Cross_val: {}'.format(cv))\n",
    "print('False negative rate: {}'.format(FNR_RFC))\n",
    "print('Overall accuracy: {}'.format(ACC_RFC))\n",
    "print('Recall: {}'.format(recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9422110552763819\n",
      "Best parameters: {'leaf_size': 1, 'n_neighbors': 4, 'weights': 'distance'}\n",
      "Cross_val: [ 0.9137931   0.87931034  0.89473684  0.96491228  0.94736842  0.9122807\n",
      "  0.94736842  0.91071429  0.91071429  0.98214286]\n",
      "False negative rate: 0.09090909090909091\n",
      "Overall accuracy: 0.9181286549707602\n",
      "Recall: 0.9181286549707602\n"
     ]
    }
   ],
   "source": [
    "#Nearest neighbor classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "CN = KNeighborsClassifier()\n",
    "\n",
    "k_range = list(range(1, 30))\n",
    "leaf_size = list(range(1,30))\n",
    "weight_options = ['uniform', 'distance']\n",
    "parameters = {'n_neighbors': k_range, 'leaf_size': leaf_size, 'weights': weight_options}\n",
    "\n",
    "grid_search = GridSearchCV(CN,\n",
    "                           param_grid=parameters,\n",
    "                           cv=10)\n",
    "\n",
    "grid_search.fit(training_input, training_output)\n",
    "cv = cross_val_score(CN, df[inputs4], df['diagnosis'], cv=10)\n",
    "CN = KNeighborsClassifier(**grid_search.best_params_)\n",
    "\n",
    "trained = CN.fit(training_input, training_output)\n",
    "predicted = CN.predict(testing_input)\n",
    "cm = confusion_matrix(testing_output, predicted)\n",
    "\n",
    "FP = cm[0][1]  \n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "TP = cm[0][0]\n",
    "\n",
    "# Recall/sensitivity/hit rate, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "# True negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "# False negative rate\n",
    "FNR_CN = FN/(TP+FN)\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC_CN = (TP+TN)/(TP+FP+FN+TN)\n",
    "recall = recall_score(testing_output, predicted, average='weighted') \n",
    "\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "print('Cross_val: {}'.format(cv))\n",
    "print('False negative rate: {}'.format(FNR_CN))\n",
    "print('Overall accuracy: {}'.format(ACC_CN))\n",
    "print('Recall: {}'.format(recall))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.957286432160804\n",
      "Best parameters: {'C': 10, 'kernel': 'linear'}\n",
      "Cross_val: [ 0.94827586  0.89655172  0.9122807   0.96491228  0.96491228  0.94736842\n",
      "  0.94736842  0.92857143  0.94642857  0.96428571]\n",
      "False negative rate: 0.047619047619047616\n",
      "Overall accuracy: 0.9473684210526315\n",
      "Recall: 0.9473684210526315\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "#train, test = train_test_split(df, test_size = 0.3)\n",
    "#training_input = train[inputs]# taking the training data input \n",
    "#training_output=train.diagnosis # This is output of our training data\n",
    "# same we have to do for test\n",
    "#testing_input= test[inputs] # taking test data inputs\n",
    "#testing_output =test.diagnosis   #output value of test dat\n",
    "\n",
    "\n",
    "\n",
    "SV = SVC()\n",
    "\n",
    "\n",
    "parameters = [\n",
    "              {'C': [1, 10, 100, 1000], \n",
    "               'kernel': ['linear']\n",
    "              },\n",
    "              {'C': [1, 10, 100, 1000], \n",
    "               'gamma': [0.001, 0.0001], \n",
    "               'kernel': ['rbf']\n",
    "              },\n",
    " ]\n",
    "\n",
    "grid_search = GridSearchCV(SV,\n",
    "                           param_grid=parameters,\n",
    "                           cv=10)\n",
    "\n",
    "grid_search.fit(training_input, training_output)\n",
    "cv = cross_val_score(SV, df[inputs4], df['diagnosis'], cv=10)\n",
    "SV = SVC(**grid_search.best_params_)\n",
    "trained = SV.fit(training_input, training_output)\n",
    "predicted = SV.predict(testing_input)\n",
    "cm = confusion_matrix(testing_output, predicted)\n",
    "\n",
    "FP = cm[0][1]  \n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "TP = cm[0][0]\n",
    "\n",
    "# Recall/sensitivity/hit rate, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "# True negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "# False negative rate\n",
    "FNR_SV = FN/(TP+FN)\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC_SV = (TP+TN)/(TP+FP+FN+TN)\n",
    "recall = recall_score(testing_output, predicted, average='weighted') \n",
    "\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "print('Cross_val: {}'.format(cv))\n",
    "print('False negative rate: {}'.format(FNR_SV))\n",
    "print('Overall accuracy: {}'.format(ACC_SV))\n",
    "print('Recall: {}'.format(recall))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.957286432160804\n",
      "Best parameters: {'C': 10, 'kernel': 'linear'}\n",
      "Cross_val: [ 0.9137931   0.93103448  0.92982456  0.96491228  0.94736842  0.9122807\n",
      "  0.96491228  0.92857143  0.94642857  0.98214286]\n",
      "False negative rate: 0.06481481481481481\n",
      "Overall accuracy: 0.9415204678362573\n",
      "Recall: 0.9415204678362573\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LR = LogisticRegression()\n",
    "LR.fit(training_input, training_output)\n",
    "cv = cross_val_score(LR, df[inputs4], df['diagnosis'], cv=10)\n",
    "classifier_accuracy = LR.score(testing_input, testing_output)\n",
    "\n",
    "trained = LR.fit(training_input, training_output)\n",
    "predicted = LR.predict(testing_input)\n",
    "cm = confusion_matrix(testing_output, predicted)\n",
    "\n",
    "FP = cm[0][1]  \n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "TP = cm[0][0]\n",
    "\n",
    "# Recall/sensitivity/hit rate, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "# True negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "# False negative rate\n",
    "FNR_LR = FN/(TP+FN)\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC_LR = (TP+TN)/(TP+FP+FN+TN)\n",
    "recall = recall_score(testing_output, predicted, average='weighted') \n",
    "\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "print('Cross_val: {}'.format(cv))\n",
    "print('False negative rate: {}'.format(FNR_LR))\n",
    "print('Overall accuracy: {}'.format(ACC_LR))\n",
    "print('Recall: {}'.format(recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e7aff3940>"
      ]
     },
     "execution_count": 1283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R49GNazlot/07TE9VhXK+/bq/fpzT9fVXMtZ15+hVe+/NlK/P4+3b7xjkiLJTVlZGW1tb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\nGGN8lhSMMcb4LCkYY4zxWVIwxhjj+//gGdcXlvdt5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e8d31e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#It turns out that RFC shouws better results. Comparing classifiers:\n",
    "\n",
    "\n",
    "rf_df = pd.DataFrame({'accuracy': cross_val_score(RFC, df[inputs4], df['diagnosis'], cv=10),\n",
    "                       'classifier': ['Random Forest'] * 10})\n",
    "dt_df = pd.DataFrame({'accuracy': cross_val_score(DTC, df[inputs4], df['diagnosis'], cv=10),\n",
    "                      'classifier': ['Dec Tree'] * 10})\n",
    "cn_df = pd.DataFrame({'accuracy': cross_val_score(CN, df[inputs4], df['diagnosis'], cv=10),\n",
    "                      'classifier': ['Nearest neigh'] * 10})\n",
    "sv_df = pd.DataFrame({'accuracy': cross_val_score(SV, df[inputs4], df['diagnosis'], cv=10),\n",
    "                      'classifier': ['SVN'] * 10})\n",
    "lr_df = pd.DataFrame({'accuracy': cross_val_score(LR, df[inputs4], df['diagnosis'], cv=10),\n",
    "                      'classifier': ['Log regr'] * 10})\n",
    "\n",
    "\n",
    "all_df = rf_df.append([dt_df, cn_df, sv_df, lr_df])\n",
    "\n",
    "\n",
    "sb.boxplot(x='classifier', y='accuracy', data=all_df)\n",
    "sb.stripplot(x='classifier', y='accuracy', data=all_df, jitter=True, color='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2e7be2f940>"
      ]
     },
     "execution_count": 1284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e88e6e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = pd.DataFrame({'FNR': FNR_LR,'classifier':['Log regr'] * 10})\n",
    "rf = pd.DataFrame({'FNR': FNR_RFC,'classifier':['Random Forest'] * 10})\n",
    "dt = pd.DataFrame({'FNR': FNR_DTC,'classifier':['Decision Tree'] * 10})\n",
    "cn = pd.DataFrame({'FNR': FNR_CN,'classifier':['Nearest neighb'] * 10})\n",
    "sv = pd.DataFrame({'FNR': FNR_SV,'classifier':['SVN'] * 10})\n",
    "\n",
    "\n",
    "all_d = lr.append([rf, dt, cn, sv])\n",
    "\n",
    "\n",
    "sb.boxplot(x='classifier', y='FNR', data=all_d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross_val: [ 0.94827586  0.93103448  0.92982456  0.96491228  0.98245614  0.94736842\n",
      "  0.98245614  0.94642857  0.94642857  0.98214286]\n",
      "False negative rate: 0.047619047619047616\n",
      "Overall accuracy: 0.9473684210526315\n",
      "Recall: 0.9473684210526315\n",
      "radius_mean\t\tB\t(Actual: B)\n",
      "concavity_mean\t\tM\t(Actual: M)\n",
      "radius_worst\t\tB\t(Actual: B)\n",
      "concavity_worst\t\tB\t(Actual: B)\n",
      "concave points_worst\t\tB\t(Actual: B)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "SV = SVC(**grid_search.best_params_)\n",
    "\n",
    "SV.fit(training_input, training_output)\n",
    "trained = SV.fit(training_input, training_output)\n",
    "predicted = SV.predict(testing_input)\n",
    "cm = confusion_matrix(testing_output, predicted)\n",
    "\n",
    "FP = cm[0][1]  \n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "TP = cm[0][0]\n",
    "\n",
    "# Recall/sensitivity/hit rate, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "# True negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "# False negative rate\n",
    "FNR_SV = FN/(TP+FN)\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC_SV = (TP+TN)/(TP+FP+FN+TN)\n",
    "\n",
    "recall = recall_score(testing_output, predicted, average='weighted')  \n",
    "cv = cross_val_score(SV, df[inputs4], df['diagnosis'], cv=10)\n",
    "classifier_accuracy = SV.score(testing_input, testing_output)\n",
    "\n",
    "\n",
    "print('Cross_val: {}'.format(cv))\n",
    "print('False negative rate: {}'.format(FNR_SV))\n",
    "print('Overall accuracy: {}'.format(ACC_SV))\n",
    "print('Recall: {}'.format(recall))\n",
    "\n",
    "\n",
    "for input_features, prediction, actual in zip(testing_input,\n",
    "                                              SV.predict(testing_input),\n",
    "                                              testing_output):\n",
    "    print('{}\\t\\t{}\\t(Actual: {})'.format(input_features, prediction, actual))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1144,
   "metadata": {
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   "source": [
    "#After optimizations, 0.045 is the lowest False Negative Rate obtained with SV classifier.\n",
    "#Corresponding recall is 0.95 - measures the proportion of positives that are correctly identified as such \n",
    "#(i.e. the percentage of sick people who are correctly identified as having the condition)"
   ]
  }
 ],
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