Breast Cancer Classification using KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension   ...    worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871   ...            17.33           184.60      2019.0   \n",
       "1                 0.05667   ...            23.41           158.80      1956.0   \n",
       "2                 0.05999   ...            25.53           152.50      1709.0   \n",
       "3                 0.09744   ...            26.50            98.87       567.7   \n",
       "4                 0.05883   ...            16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
    "df['target'] = cancer.target\n",
    "df.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "malignant    212\n",
       "benign       357\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_distrubution = pd.Series([212,357],name='target',index=['malignant','benign'])\n",
    "class_distrubution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.pop('target')\n",
    "X = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
      "0         17.990         10.38          122.80     1001.0          0.11840   \n",
      "1         20.570         17.77          132.90     1326.0          0.08474   \n",
      "2         19.690         21.25          130.00     1203.0          0.10960   \n",
      "3         11.420         20.38           77.58      386.1          0.14250   \n",
      "4         20.290         14.34          135.10     1297.0          0.10030   \n",
      "5         12.450         15.70           82.57      477.1          0.12780   \n",
      "6         18.250         19.98          119.60     1040.0          0.09463   \n",
      "7         13.710         20.83           90.20      577.9          0.11890   \n",
      "8         13.000         21.82           87.50      519.8          0.12730   \n",
      "9         12.460         24.04           83.97      475.9          0.11860   \n",
      "10        16.020         23.24          102.70      797.8          0.08206   \n",
      "11        15.780         17.89          103.60      781.0          0.09710   \n",
      "12        19.170         24.80          132.40     1123.0          0.09740   \n",
      "13        15.850         23.95          103.70      782.7          0.08401   \n",
      "14        13.730         22.61           93.60      578.3          0.11310   \n",
      "15        14.540         27.54           96.73      658.8          0.11390   \n",
      "16        14.680         20.13           94.74      684.5          0.09867   \n",
      "17        16.130         20.68          108.10      798.8          0.11700   \n",
      "18        19.810         22.15          130.00     1260.0          0.09831   \n",
      "19        13.540         14.36           87.46      566.3          0.09779   \n",
      "20        13.080         15.71           85.63      520.0          0.10750   \n",
      "21         9.504         12.44           60.34      273.9          0.10240   \n",
      "22        15.340         14.26          102.50      704.4          0.10730   \n",
      "23        21.160         23.04          137.20     1404.0          0.09428   \n",
      "24        16.650         21.38          110.00      904.6          0.11210   \n",
      "25        17.140         16.40          116.00      912.7          0.11860   \n",
      "26        14.580         21.53           97.41      644.8          0.10540   \n",
      "27        18.610         20.25          122.10     1094.0          0.09440   \n",
      "28        15.300         25.27          102.40      732.4          0.10820   \n",
      "29        17.570         15.05          115.00      955.1          0.09847   \n",
      "..           ...           ...             ...        ...              ...   \n",
      "539        7.691         25.44           48.34      170.4          0.08668   \n",
      "540       11.540         14.44           74.65      402.9          0.09984   \n",
      "541       14.470         24.99           95.81      656.4          0.08837   \n",
      "542       14.740         25.42           94.70      668.6          0.08275   \n",
      "543       13.210         28.06           84.88      538.4          0.08671   \n",
      "544       13.870         20.70           89.77      584.8          0.09578   \n",
      "545       13.620         23.23           87.19      573.2          0.09246   \n",
      "546       10.320         16.35           65.31      324.9          0.09434   \n",
      "547       10.260         16.58           65.85      320.8          0.08877   \n",
      "548        9.683         19.34           61.05      285.7          0.08491   \n",
      "549       10.820         24.21           68.89      361.6          0.08192   \n",
      "550       10.860         21.48           68.51      360.5          0.07431   \n",
      "551       11.130         22.44           71.49      378.4          0.09566   \n",
      "552       12.770         29.43           81.35      507.9          0.08276   \n",
      "553        9.333         21.94           59.01      264.0          0.09240   \n",
      "554       12.880         28.92           82.50      514.3          0.08123   \n",
      "555       10.290         27.61           65.67      321.4          0.09030   \n",
      "556       10.160         19.59           64.73      311.7          0.10030   \n",
      "557        9.423         27.88           59.26      271.3          0.08123   \n",
      "558       14.590         22.68           96.39      657.1          0.08473   \n",
      "559       11.510         23.93           74.52      403.5          0.09261   \n",
      "560       14.050         27.15           91.38      600.4          0.09929   \n",
      "561       11.200         29.37           70.67      386.0          0.07449   \n",
      "562       15.220         30.62          103.40      716.9          0.10480   \n",
      "563       20.920         25.09          143.00     1347.0          0.10990   \n",
      "564       21.560         22.39          142.00     1479.0          0.11100   \n",
      "565       20.130         28.25          131.20     1261.0          0.09780   \n",
      "566       16.600         28.08          108.30      858.1          0.08455   \n",
      "567       20.600         29.33          140.10     1265.0          0.11780   \n",
      "568        7.760         24.54           47.92      181.0          0.05263   \n",
      "\n",
      "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
      "0             0.27760        0.300100             0.147100         0.2419   \n",
      "1             0.07864        0.086900             0.070170         0.1812   \n",
      "2             0.15990        0.197400             0.127900         0.2069   \n",
      "3             0.28390        0.241400             0.105200         0.2597   \n",
      "4             0.13280        0.198000             0.104300         0.1809   \n",
      "5             0.17000        0.157800             0.080890         0.2087   \n",
      "6             0.10900        0.112700             0.074000         0.1794   \n",
      "7             0.16450        0.093660             0.059850         0.2196   \n",
      "8             0.19320        0.185900             0.093530         0.2350   \n",
      "9             0.23960        0.227300             0.085430         0.2030   \n",
      "10            0.06669        0.032990             0.033230         0.1528   \n",
      "11            0.12920        0.099540             0.066060         0.1842   \n",
      "12            0.24580        0.206500             0.111800         0.2397   \n",
      "13            0.10020        0.099380             0.053640         0.1847   \n",
      "14            0.22930        0.212800             0.080250         0.2069   \n",
      "15            0.15950        0.163900             0.073640         0.2303   \n",
      "16            0.07200        0.073950             0.052590         0.1586   \n",
      "17            0.20220        0.172200             0.102800         0.2164   \n",
      "18            0.10270        0.147900             0.094980         0.1582   \n",
      "19            0.08129        0.066640             0.047810         0.1885   \n",
      "20            0.12700        0.045680             0.031100         0.1967   \n",
      "21            0.06492        0.029560             0.020760         0.1815   \n",
      "22            0.21350        0.207700             0.097560         0.2521   \n",
      "23            0.10220        0.109700             0.086320         0.1769   \n",
      "24            0.14570        0.152500             0.091700         0.1995   \n",
      "25            0.22760        0.222900             0.140100         0.3040   \n",
      "26            0.18680        0.142500             0.087830         0.2252   \n",
      "27            0.10660        0.149000             0.077310         0.1697   \n",
      "28            0.16970        0.168300             0.087510         0.1926   \n",
      "29            0.11570        0.098750             0.079530         0.1739   \n",
      "..                ...             ...                  ...            ...   \n",
      "539           0.11990        0.092520             0.013640         0.2037   \n",
      "540           0.11200        0.067370             0.025940         0.1818   \n",
      "541           0.12300        0.100900             0.038900         0.1872   \n",
      "542           0.07214        0.041050             0.030270         0.1840   \n",
      "543           0.06877        0.029870             0.032750         0.1628   \n",
      "544           0.10180        0.036880             0.023690         0.1620   \n",
      "545           0.06747        0.029740             0.024430         0.1664   \n",
      "546           0.04994        0.010120             0.005495         0.1885   \n",
      "547           0.08066        0.043580             0.024380         0.1669   \n",
      "548           0.05030        0.023370             0.009615         0.1580   \n",
      "549           0.06602        0.015480             0.008160         0.1976   \n",
      "550           0.04227        0.000000             0.000000         0.1661   \n",
      "551           0.08194        0.048240             0.022570         0.2030   \n",
      "552           0.04234        0.019970             0.014990         0.1539   \n",
      "553           0.05605        0.039960             0.012820         0.1692   \n",
      "554           0.05824        0.061950             0.023430         0.1566   \n",
      "555           0.07658        0.059990             0.027380         0.1593   \n",
      "556           0.07504        0.005025             0.011160         0.1791   \n",
      "557           0.04971        0.000000             0.000000         0.1742   \n",
      "558           0.13300        0.102900             0.037360         0.1454   \n",
      "559           0.10210        0.111200             0.041050         0.1388   \n",
      "560           0.11260        0.044620             0.043040         0.1537   \n",
      "561           0.03558        0.000000             0.000000         0.1060   \n",
      "562           0.20870        0.255000             0.094290         0.2128   \n",
      "563           0.22360        0.317400             0.147400         0.2149   \n",
      "564           0.11590        0.243900             0.138900         0.1726   \n",
      "565           0.10340        0.144000             0.097910         0.1752   \n",
      "566           0.10230        0.092510             0.053020         0.1590   \n",
      "567           0.27700        0.351400             0.152000         0.2397   \n",
      "568           0.04362        0.000000             0.000000         0.1587   \n",
      "\n",
      "     mean fractal dimension           ...             worst radius  \\\n",
      "0                   0.07871           ...                   25.380   \n",
      "1                   0.05667           ...                   24.990   \n",
      "2                   0.05999           ...                   23.570   \n",
      "3                   0.09744           ...                   14.910   \n",
      "4                   0.05883           ...                   22.540   \n",
      "5                   0.07613           ...                   15.470   \n",
      "6                   0.05742           ...                   22.880   \n",
      "7                   0.07451           ...                   17.060   \n",
      "8                   0.07389           ...                   15.490   \n",
      "9                   0.08243           ...                   15.090   \n",
      "10                  0.05697           ...                   19.190   \n",
      "11                  0.06082           ...                   20.420   \n",
      "12                  0.07800           ...                   20.960   \n",
      "13                  0.05338           ...                   16.840   \n",
      "14                  0.07682           ...                   15.030   \n",
      "15                  0.07077           ...                   17.460   \n",
      "16                  0.05922           ...                   19.070   \n",
      "17                  0.07356           ...                   20.960   \n",
      "18                  0.05395           ...                   27.320   \n",
      "19                  0.05766           ...                   15.110   \n",
      "20                  0.06811           ...                   14.500   \n",
      "21                  0.06905           ...                   10.230   \n",
      "22                  0.07032           ...                   18.070   \n",
      "23                  0.05278           ...                   29.170   \n",
      "24                  0.06330           ...                   26.460   \n",
      "25                  0.07413           ...                   22.250   \n",
      "26                  0.06924           ...                   17.620   \n",
      "27                  0.05699           ...                   21.310   \n",
      "28                  0.06540           ...                   20.270   \n",
      "29                  0.06149           ...                   20.010   \n",
      "..                      ...           ...                      ...   \n",
      "539                 0.07751           ...                    8.678   \n",
      "540                 0.06782           ...                   12.260   \n",
      "541                 0.06341           ...                   16.220   \n",
      "542                 0.05680           ...                   16.510   \n",
      "543                 0.05781           ...                   14.370   \n",
      "544                 0.06688           ...                   15.050   \n",
      "545                 0.05801           ...                   15.350   \n",
      "546                 0.06201           ...                   11.250   \n",
      "547                 0.06714           ...                   10.830   \n",
      "548                 0.06235           ...                   10.930   \n",
      "549                 0.06328           ...                   13.030   \n",
      "550                 0.05948           ...                   11.660   \n",
      "551                 0.06552           ...                   12.020   \n",
      "552                 0.05637           ...                   13.870   \n",
      "553                 0.06576           ...                    9.845   \n",
      "554                 0.05708           ...                   13.890   \n",
      "555                 0.06127           ...                   10.840   \n",
      "556                 0.06331           ...                   10.650   \n",
      "557                 0.06059           ...                   10.490   \n",
      "558                 0.06147           ...                   15.480   \n",
      "559                 0.06570           ...                   12.480   \n",
      "560                 0.06171           ...                   15.300   \n",
      "561                 0.05502           ...                   11.920   \n",
      "562                 0.07152           ...                   17.520   \n",
      "563                 0.06879           ...                   24.290   \n",
      "564                 0.05623           ...                   25.450   \n",
      "565                 0.05533           ...                   23.690   \n",
      "566                 0.05648           ...                   18.980   \n",
      "567                 0.07016           ...                   25.740   \n",
      "568                 0.05884           ...                    9.456   \n",
      "\n",
      "     worst texture  worst perimeter  worst area  worst smoothness  \\\n",
      "0            17.33           184.60      2019.0           0.16220   \n",
      "1            23.41           158.80      1956.0           0.12380   \n",
      "2            25.53           152.50      1709.0           0.14440   \n",
      "3            26.50            98.87       567.7           0.20980   \n",
      "4            16.67           152.20      1575.0           0.13740   \n",
      "5            23.75           103.40       741.6           0.17910   \n",
      "6            27.66           153.20      1606.0           0.14420   \n",
      "7            28.14           110.60       897.0           0.16540   \n",
      "8            30.73           106.20       739.3           0.17030   \n",
      "9            40.68            97.65       711.4           0.18530   \n",
      "10           33.88           123.80      1150.0           0.11810   \n",
      "11           27.28           136.50      1299.0           0.13960   \n",
      "12           29.94           151.70      1332.0           0.10370   \n",
      "13           27.66           112.00       876.5           0.11310   \n",
      "14           32.01           108.80       697.7           0.16510   \n",
      "15           37.13           124.10       943.2           0.16780   \n",
      "16           30.88           123.40      1138.0           0.14640   \n",
      "17           31.48           136.80      1315.0           0.17890   \n",
      "18           30.88           186.80      2398.0           0.15120   \n",
      "19           19.26            99.70       711.2           0.14400   \n",
      "20           20.49            96.09       630.5           0.13120   \n",
      "21           15.66            65.13       314.9           0.13240   \n",
      "22           19.08           125.10       980.9           0.13900   \n",
      "23           35.59           188.00      2615.0           0.14010   \n",
      "24           31.56           177.00      2215.0           0.18050   \n",
      "25           21.40           152.40      1461.0           0.15450   \n",
      "26           33.21           122.40       896.9           0.15250   \n",
      "27           27.26           139.90      1403.0           0.13380   \n",
      "28           36.71           149.30      1269.0           0.16410   \n",
      "29           19.52           134.90      1227.0           0.12550   \n",
      "..             ...              ...         ...               ...   \n",
      "539          31.89            54.49       223.6           0.15960   \n",
      "540          19.68            78.78       457.8           0.13450   \n",
      "541          31.73           113.50       808.9           0.13400   \n",
      "542          32.29           107.40       826.4           0.10600   \n",
      "543          37.17            92.48       629.6           0.10720   \n",
      "544          24.75            99.17       688.6           0.12640   \n",
      "545          29.09            97.58       729.8           0.12160   \n",
      "546          21.77            71.12       384.9           0.12850   \n",
      "547          22.04            71.08       357.4           0.14610   \n",
      "548          25.59            69.10       364.2           0.11990   \n",
      "549          31.45            83.90       505.6           0.12040   \n",
      "550          24.77            74.08       412.3           0.10010   \n",
      "551          28.26            77.80       436.6           0.10870   \n",
      "552          36.00            88.10       594.7           0.12340   \n",
      "553          25.05            62.86       295.8           0.11030   \n",
      "554          35.74            88.84       595.7           0.12270   \n",
      "555          34.91            69.57       357.6           0.13840   \n",
      "556          22.88            67.88       347.3           0.12650   \n",
      "557          34.24            66.50       330.6           0.10730   \n",
      "558          27.27           105.90       733.5           0.10260   \n",
      "559          37.16            82.28       474.2           0.12980   \n",
      "560          33.17           100.20       706.7           0.12410   \n",
      "561          38.30            75.19       439.6           0.09267   \n",
      "562          42.79           128.70       915.0           0.14170   \n",
      "563          29.41           179.10      1819.0           0.14070   \n",
      "564          26.40           166.10      2027.0           0.14100   \n",
      "565          38.25           155.00      1731.0           0.11660   \n",
      "566          34.12           126.70      1124.0           0.11390   \n",
      "567          39.42           184.60      1821.0           0.16500   \n",
      "568          30.37            59.16       268.6           0.08996   \n",
      "\n",
      "     worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
      "0              0.66560          0.71190               0.26540          0.4601   \n",
      "1              0.18660          0.24160               0.18600          0.2750   \n",
      "2              0.42450          0.45040               0.24300          0.3613   \n",
      "3              0.86630          0.68690               0.25750          0.6638   \n",
      "4              0.20500          0.40000               0.16250          0.2364   \n",
      "5              0.52490          0.53550               0.17410          0.3985   \n",
      "6              0.25760          0.37840               0.19320          0.3063   \n",
      "7              0.36820          0.26780               0.15560          0.3196   \n",
      "8              0.54010          0.53900               0.20600          0.4378   \n",
      "9              1.05800          1.10500               0.22100          0.4366   \n",
      "10             0.15510          0.14590               0.09975          0.2948   \n",
      "11             0.56090          0.39650               0.18100          0.3792   \n",
      "12             0.39030          0.36390               0.17670          0.3176   \n",
      "13             0.19240          0.23220               0.11190          0.2809   \n",
      "14             0.77250          0.69430               0.22080          0.3596   \n",
      "15             0.65770          0.70260               0.17120          0.4218   \n",
      "16             0.18710          0.29140               0.16090          0.3029   \n",
      "17             0.42330          0.47840               0.20730          0.3706   \n",
      "18             0.31500          0.53720               0.23880          0.2768   \n",
      "19             0.17730          0.23900               0.12880          0.2977   \n",
      "20             0.27760          0.18900               0.07283          0.3184   \n",
      "21             0.11480          0.08867               0.06227          0.2450   \n",
      "22             0.59540          0.63050               0.23930          0.4667   \n",
      "23             0.26000          0.31550               0.20090          0.2822   \n",
      "24             0.35780          0.46950               0.20950          0.3613   \n",
      "25             0.39490          0.38530               0.25500          0.4066   \n",
      "26             0.66430          0.55390               0.27010          0.4264   \n",
      "27             0.21170          0.34460               0.14900          0.2341   \n",
      "28             0.61100          0.63350               0.20240          0.4027   \n",
      "29             0.28120          0.24890               0.14560          0.2756   \n",
      "..                 ...              ...                   ...             ...   \n",
      "539            0.30640          0.33930               0.05000          0.2790   \n",
      "540            0.21180          0.17970               0.06918          0.2329   \n",
      "541            0.42020          0.40400               0.12050          0.3187   \n",
      "542            0.13760          0.16110               0.10950          0.2722   \n",
      "543            0.13810          0.10620               0.07958          0.2473   \n",
      "544            0.20370          0.13770               0.06845          0.2249   \n",
      "545            0.15170          0.10490               0.07174          0.2642   \n",
      "546            0.08842          0.04384               0.02381          0.2681   \n",
      "547            0.22460          0.17830               0.08333          0.2691   \n",
      "548            0.09546          0.09350               0.03846          0.2552   \n",
      "549            0.16330          0.06194               0.03264          0.3059   \n",
      "550            0.07348          0.00000               0.00000          0.2458   \n",
      "551            0.17820          0.15640               0.06413          0.3169   \n",
      "552            0.10640          0.08653               0.06498          0.2407   \n",
      "553            0.08298          0.07993               0.02564          0.2435   \n",
      "554            0.16200          0.24390               0.06493          0.2372   \n",
      "555            0.17100          0.20000               0.09127          0.2226   \n",
      "556            0.12000          0.01005               0.02232          0.2262   \n",
      "557            0.07158          0.00000               0.00000          0.2475   \n",
      "558            0.31710          0.36620               0.11050          0.2258   \n",
      "559            0.25170          0.36300               0.09653          0.2112   \n",
      "560            0.22640          0.13260               0.10480          0.2250   \n",
      "561            0.05494          0.00000               0.00000          0.1566   \n",
      "562            0.79170          1.17000               0.23560          0.4089   \n",
      "563            0.41860          0.65990               0.25420          0.2929   \n",
      "564            0.21130          0.41070               0.22160          0.2060   \n",
      "565            0.19220          0.32150               0.16280          0.2572   \n",
      "566            0.30940          0.34030               0.14180          0.2218   \n",
      "567            0.86810          0.93870               0.26500          0.4087   \n",
      "568            0.06444          0.00000               0.00000          0.2871   \n",
      "\n",
      "     worst fractal dimension  \n",
      "0                    0.11890  \n",
      "1                    0.08902  \n",
      "2                    0.08758  \n",
      "3                    0.17300  \n",
      "4                    0.07678  \n",
      "5                    0.12440  \n",
      "6                    0.08368  \n",
      "7                    0.11510  \n",
      "8                    0.10720  \n",
      "9                    0.20750  \n",
      "10                   0.08452  \n",
      "11                   0.10480  \n",
      "12                   0.10230  \n",
      "13                   0.06287  \n",
      "14                   0.14310  \n",
      "15                   0.13410  \n",
      "16                   0.08216  \n",
      "17                   0.11420  \n",
      "18                   0.07615  \n",
      "19                   0.07259  \n",
      "20                   0.08183  \n",
      "21                   0.07773  \n",
      "22                   0.09946  \n",
      "23                   0.07526  \n",
      "24                   0.09564  \n",
      "25                   0.10590  \n",
      "26                   0.12750  \n",
      "27                   0.07421  \n",
      "28                   0.09876  \n",
      "29                   0.07919  \n",
      "..                       ...  \n",
      "539                  0.10660  \n",
      "540                  0.08134  \n",
      "541                  0.10230  \n",
      "542                  0.06956  \n",
      "543                  0.06443  \n",
      "544                  0.08492  \n",
      "545                  0.06953  \n",
      "546                  0.07399  \n",
      "547                  0.09479  \n",
      "548                  0.07920  \n",
      "549                  0.07626  \n",
      "550                  0.06592  \n",
      "551                  0.08032  \n",
      "552                  0.06484  \n",
      "553                  0.07393  \n",
      "554                  0.07242  \n",
      "555                  0.08283  \n",
      "556                  0.06742  \n",
      "557                  0.06969  \n",
      "558                  0.08004  \n",
      "559                  0.08732  \n",
      "560                  0.08321  \n",
      "561                  0.05905  \n",
      "562                  0.14090  \n",
      "563                  0.09873  \n",
      "564                  0.07115  \n",
      "565                  0.06637  \n",
      "566                  0.07820  \n",
      "567                  0.12400  \n",
      "568                  0.07039  \n",
      "\n",
      "[569 rows x 30 columns] 0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n",
      "5      0\n",
      "6      0\n",
      "7      0\n",
      "8      0\n",
      "9      0\n",
      "10     0\n",
      "11     0\n",
      "12     0\n",
      "13     0\n",
      "14     0\n",
      "15     0\n",
      "16     0\n",
      "17     0\n",
      "18     0\n",
      "19     1\n",
      "20     1\n",
      "21     1\n",
      "22     0\n",
      "23     0\n",
      "24     0\n",
      "25     0\n",
      "26     0\n",
      "27     0\n",
      "28     0\n",
      "29     0\n",
      "      ..\n",
      "539    1\n",
      "540    1\n",
      "541    1\n",
      "542    1\n",
      "543    1\n",
      "544    1\n",
      "545    1\n",
      "546    1\n",
      "547    1\n",
      "548    1\n",
      "549    1\n",
      "550    1\n",
      "551    1\n",
      "552    1\n",
      "553    1\n",
      "554    1\n",
      "555    1\n",
      "556    1\n",
      "557    1\n",
      "558    1\n",
      "559    1\n",
      "560    1\n",
      "561    1\n",
      "562    0\n",
      "563    0\n",
      "564    0\n",
      "565    0\n",
      "566    0\n",
      "567    0\n",
      "568    1\n",
      "Name: target, Length: 569, dtype: int32\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'head'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-3f419aa187f0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'head'"
     ]
    }
   ],
   "source": [
    "print(X,y).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.65,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.945"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = df.mean()[:-1].values.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Training and Test Accuracies for Malignant and Benign Cells')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mal_train_X = X_train[y_train==0]\n",
    "mal_train_y = y_train[y_train==0]\n",
    "ben_train_X = X_train[y_train==1]\n",
    "ben_train_y = y_train[y_train==1]\n",
    "\n",
    "mal_test_X = X_test[y_test==0]\n",
    "mal_test_y = y_test[y_test==0]\n",
    "ben_test_X = X_test[y_test==1]\n",
    "ben_test_y = y_test[y_test==1]\n",
    "\n",
    "    \n",
    "\n",
    "scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), \n",
    "          knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "        \n",
    "bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])\n",
    "\n",
    "        \n",
    "for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), \n",
    "        ha='center', color='w', fontsize=11)\n",
    "\n",
    "    \n",
    "plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
    "\n",
    "    \n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_visible(False)\n",
    "\n",
    "plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\nTest', 'Benign\\nTest'], alpha=0.8);\n",
    "plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)\n",
    "\n",
    "#accuracy_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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