Breast cancer pred(M or B).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('C:/Users/Manav Jain/Documents/breast cancer prediction/data.csv', index_col=False)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 33)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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   area_worst  \\\n",
       "count     569.000000  ...     569.000000       569.000000   569.000000   \n",
       "mean        0.181162  ...      25.677223       107.261213   880.583128   \n",
       "std         0.027414  ...       6.146258        33.602542   569.356993   \n",
       "min         0.106000  ...      12.020000        50.410000   185.200000   \n",
       "25%         0.161900  ...      21.080000        84.110000   515.300000   \n",
       "50%         0.179200  ...      25.410000        97.660000   686.500000   \n",
       "75%         0.195700  ...      29.720000       125.400000  1084.000000   \n",
       "max         0.304000  ...      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['diagnosis'] = data['diagnosis'].apply(lambda x: '1' if x == 'M' else '0')\n",
    "data = data.set_index('id')\n",
    "del data['Unnamed: 32']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagnosis\n",
      "0    357\n",
      "1    212\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('diagnosis').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 35 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='density', subplots=True, layout=(5,7), sharex=False, legend=False, fontsize=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm as cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "cmap = cm.get_cmap('jet', 30)\n",
    "cax = ax1.imshow(data.corr(), interpolation=\"none\", cmap=cmap)\n",
    "ax1.grid(True)\n",
    "plt.title('Breast Cancer Attributes Correlation')\n",
    "# Add colorbar, make sure to specify tick locations to match desired ticklabels\n",
    "fig.colorbar(cax, ticks=[.75,.8,.85,.90,.95,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['diagnosis'].values\n",
    "X = data.drop('diagnosis', axis=1).values\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split (X, Y, test_size = 0.20, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_list = []\n",
    "models_list.append(('CART', DecisionTreeClassifier()))\n",
    "models_list.append(('SVM', SVC())) \n",
    "models_list.append(('NB', GaussianNB()))\n",
    "models_list.append(('KNN', KNeighborsClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Manav Jain\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CART: 0.916473 (0.032582) (run time: 0.317219)\n",
      "SVM: 0.907681 (0.054723) (run time: 0.062557)\n",
      "NB: 0.940773 (0.033921) (run time: 0.053444)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Manav Jain\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "C:\\Users\\Manav Jain\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n",
      "C:\\Users\\Manav Jain\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: 0.927729 (0.055250) (run time: 0.196537)\n"
     ]
    }
   ],
   "source": [
    "num_folds = 10\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models_list:\n",
    "    kfold = KFold(n_splits=num_folds, random_state=123)\n",
    "    start = time.time()\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "    end = time.time()\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print( \"%s: %f (%f) (run time: %f)\" % (name, cv_results.mean(), cv_results.std(), end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "fig.suptitle('Performance Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScaledCART: 0.923285 (0.049411) (run time: 0.151433)\n",
      "ScaledSVM: 0.964879 (0.038621) (run time: 0.076629)\n",
      "ScaledNB: 0.931932 (0.038625) (run time: 0.046887)\n",
      "ScaledKNN: 0.958357 (0.038595) (run time: 0.129820)\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "# Standardize the dataset\n",
    "pipelines = []\n",
    "\n",
    "pipelines.append(('ScaledCART', Pipeline([('Scaler', StandardScaler()),('CART',\n",
    "                                                                        DecisionTreeClassifier())])))\n",
    "pipelines.append(('ScaledSVM', Pipeline([('Scaler', StandardScaler()),('SVM', SVC( ))])))\n",
    "pipelines.append(('ScaledNB', Pipeline([('Scaler', StandardScaler()),('NB',\n",
    "                                                                      GaussianNB())])))\n",
    "pipelines.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()),('KNN',\n",
    "                                                                       KNeighborsClassifier())])))\n",
    "results = []\n",
    "names = []\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    kfold = KFold(n_splits=num_folds, random_state=123)\n",
    "    for name, model in pipelines:\n",
    "        start = time.time()\n",
    "        cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "        end = time.time()\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "        print( \"%s: %f (%f) (run time: %f)\" % (name, cv_results.mean(), cv_results.std(), end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Manav Jain\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.969324 using {'C': 2.0, 'kernel': 'rbf'}\n",
      "0.964976 (0.026211) with: {'C': 0.1, 'kernel': 'linear'}\n",
      "0.828551 (0.054707) with: {'C': 0.1, 'kernel': 'poly'}\n",
      "0.940725 (0.038380) with: {'C': 0.1, 'kernel': 'rbf'}\n",
      "0.949469 (0.032899) with: {'C': 0.1, 'kernel': 'sigmoid'}\n",
      "0.962754 (0.029531) with: {'C': 0.3, 'kernel': 'linear'}\n",
      "0.863720 (0.050997) with: {'C': 0.3, 'kernel': 'poly'}\n",
      "0.956039 (0.032900) with: {'C': 0.3, 'kernel': 'rbf'}\n",
      "0.960386 (0.029499) with: {'C': 0.3, 'kernel': 'sigmoid'}\n",
      "0.956184 (0.030988) with: {'C': 0.5, 'kernel': 'linear'}\n",
      "0.879034 (0.053507) with: {'C': 0.5, 'kernel': 'poly'}\n",
      "0.964879 (0.030054) with: {'C': 0.5, 'kernel': 'rbf'}\n",
      "0.956087 (0.027848) with: {'C': 0.5, 'kernel': 'sigmoid'}\n",
      "0.954010 (0.031644) with: {'C': 0.7, 'kernel': 'linear'}\n",
      "0.885604 (0.038275) with: {'C': 0.7, 'kernel': 'poly'}\n",
      "0.967053 (0.037461) with: {'C': 0.7, 'kernel': 'rbf'}\n",
      "0.949565 (0.027831) with: {'C': 0.7, 'kernel': 'sigmoid'}\n",
      "0.951836 (0.028830) with: {'C': 0.9, 'kernel': 'linear'}\n",
      "0.887826 (0.039039) with: {'C': 0.9, 'kernel': 'poly'}\n",
      "0.967053 (0.037461) with: {'C': 0.9, 'kernel': 'rbf'}\n",
      "0.947391 (0.032846) with: {'C': 0.9, 'kernel': 'sigmoid'}\n",
      "0.954010 (0.026552) with: {'C': 1.0, 'kernel': 'linear'}\n",
      "0.890048 (0.038398) with: {'C': 1.0, 'kernel': 'poly'}\n",
      "0.967101 (0.033160) with: {'C': 1.0, 'kernel': 'rbf'}\n",
      "0.947391 (0.032846) with: {'C': 1.0, 'kernel': 'sigmoid'}\n",
      "0.956184 (0.025767) with: {'C': 1.3, 'kernel': 'linear'}\n",
      "0.894396 (0.039509) with: {'C': 1.3, 'kernel': 'poly'}\n",
      "0.967150 (0.028285) with: {'C': 1.3, 'kernel': 'rbf'}\n",
      "0.947391 (0.029759) with: {'C': 1.3, 'kernel': 'sigmoid'}\n",
      "0.958357 (0.024768) with: {'C': 1.5, 'kernel': 'linear'}\n",
      "0.898792 (0.033329) with: {'C': 1.5, 'kernel': 'poly'}\n",
      "0.967150 (0.028285) with: {'C': 1.5, 'kernel': 'rbf'}\n",
      "0.940821 (0.039361) with: {'C': 1.5, 'kernel': 'sigmoid'}\n",
      "0.956135 (0.021744) with: {'C': 1.7, 'kernel': 'linear'}\n",
      "0.900966 (0.034861) with: {'C': 1.7, 'kernel': 'poly'}\n",
      "0.967150 (0.024547) with: {'C': 1.7, 'kernel': 'rbf'}\n",
      "0.940918 (0.037900) with: {'C': 1.7, 'kernel': 'sigmoid'}\n",
      "0.956135 (0.021744) with: {'C': 2.0, 'kernel': 'linear'}\n",
      "0.907536 (0.034327) with: {'C': 2.0, 'kernel': 'poly'}\n",
      "0.969324 (0.022458) with: {'C': 2.0, 'kernel': 'rbf'}\n",
      "0.932029 (0.028300) with: {'C': 2.0, 'kernel': 'sigmoid'}\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "rescaledX = scaler.transform(X_train)\n",
    "c_values = [0.1, 0.3, 0.5, 0.7, 0.9, 1.0, 1.3, 1.5, 1.7, 2.0]\n",
    "kernel_values = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "param_grid = dict(C=c_values, kernel=kernel_values)\n",
    "model = SVC()\n",
    "kfold = KFold(n_splits=num_folds, random_state=21)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy', cv=kfold)\n",
    "grid_result = grid.fit(rescaledX, Y_train)\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run Time: 0.008004\n"
     ]
    }
   ],
   "source": [
    "# prepare the model\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "model = SVC(C=2.0, kernel='rbf')\n",
    "start = time.time()\n",
    "model.fit(X_train_scaled, Y_train)\n",
    "end = time.time()\n",
    "print( \"Run Time: %f\" % (end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# estimate accuracy on test dataset\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "predictions = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score 0.991228\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99        75\n",
      "           1       0.97      1.00      0.99        39\n",
      "\n",
      "    accuracy                           0.99       114\n",
      "   macro avg       0.99      0.99      0.99       114\n",
      "weighted avg       0.99      0.99      0.99       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy score %f\" % accuracy_score(Y_test, predictions))\n",
    "print(classification_report(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[74  1]\n",
      " [ 0 39]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '0', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0',\n",
       "       '0', '0', '1', '0', '0', '0', '0', '0', '1', '0', '1', '1', '0',\n",
       "       '0', '0', '1', '0', '0', '0', '0', '0', '1', '0', '0', '1', '1',\n",
       "       '1', '0', '1', '0', '0', '0', '1', '0', '0', '0', '0', '0', '0',\n",
       "       '0', '1', '0', '1', '1', '1', '1', '0', '0', '1', '0', '1', '1',\n",
       "       '1', '0', '0', '1', '0', '1', '1', '0', '1', '1', '0', '0', '0',\n",
       "       '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '0',\n",
       "       '0', '1', '1', '1', '0', '0', '0', '1', '0', '0', '0', '0', '0',\n",
       "       '1', '0', '0', '0', '0', '1', '1', '0', '0', '1'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.086e+01, 2.148e+01, 6.851e+01, ..., 0.000e+00, 2.458e-01,\n",
       "        6.592e-02],\n",
       "       [1.495e+01, 1.877e+01, 9.784e+01, ..., 8.405e-02, 2.852e-01,\n",
       "        9.218e-02],\n",
       "       [2.029e+01, 1.434e+01, 1.351e+02, ..., 1.625e-01, 2.364e-01,\n",
       "        7.678e-02],\n",
       "       ...,\n",
       "       [1.207e+01, 1.344e+01, 7.783e+01, ..., 7.393e-02, 2.781e-01,\n",
       "        8.052e-02],\n",
       "       [8.671e+00, 1.445e+01, 5.442e+01, ..., 0.000e+00, 2.592e-01,\n",
       "        7.848e-02],\n",
       "       [1.546e+01, 1.189e+01, 1.025e+02, ..., 1.827e-01, 3.216e-01,\n",
       "        1.010e-01]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '0', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0',\n",
       "       '0', '0', '1', '0', '0', '0', '0', '0', '1', '0', '1', '1', '0',\n",
       "       '0', '0', '1', '0', '0', '0', '0', '0', '1', '0', '0', '1', '0',\n",
       "       '1', '0', '1', '0', '0', '0', '1', '0', '0', '0', '0', '0', '0',\n",
       "       '0', '1', '0', '1', '1', '1', '1', '0', '0', '1', '0', '1', '1',\n",
       "       '1', '0', '0', '1', '0', '1', '1', '0', '1', '1', '0', '0', '0',\n",
       "       '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '0',\n",
       "       '0', '1', '1', '1', '0', '0', '0', '1', '0', '0', '0', '0', '0',\n",
       "       '1', '0', '0', '0', '0', '1', '1', '0', '0', '1'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.327e+01, 1.702e+01, 8.455e+01, ..., 9.678e-02, 2.506e-01,\n",
       "        7.623e-02],\n",
       "       [1.973e+01, 1.982e+01, 1.307e+02, ..., 2.507e-01, 2.749e-01,\n",
       "        1.297e-01],\n",
       "       [2.522e+01, 2.491e+01, 1.715e+02, ..., 2.867e-01, 2.355e-01,\n",
       "        1.051e-01],\n",
       "       ...,\n",
       "       [2.171e+01, 1.725e+01, 1.409e+02, ..., 1.820e-01, 2.510e-01,\n",
       "        6.494e-02],\n",
       "       [1.205e+01, 1.463e+01, 7.804e+01, ..., 6.548e-02, 2.747e-01,\n",
       "        8.301e-02],\n",
       "       [1.701e+01, 2.026e+01, 1.097e+02, ..., 1.096e-01, 3.275e-01,\n",
       "        6.469e-02]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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