Breast Cancer Classification 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, plot_confusion_matrix, f1_score, recall_score, classification_report\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>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  area_worst  \\\n",
       "0  ...         25.38          17.33           184.60      2019.0   \n",
       "1  ...         24.99          23.41           158.80      1956.0   \n",
       "2  ...         23.57          25.53           152.50      1709.0   \n",
       "3  ...         14.91          26.50            98.87       567.7   \n",
       "4  ...         22.54          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\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",
       "   symmetry_worst  fractal_dimension_worst  \n",
       "0          0.4601                  0.11890  \n",
       "1          0.2750                  0.08902  \n",
       "2          0.3613                  0.08758  \n",
       "3          0.6638                  0.17300  \n",
       "4          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>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>569.000000</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>16.269190</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",
       "    </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>4.833242</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",
       "    </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>7.930000</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",
       "    </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>13.010000</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",
       "    </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>14.970000</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",
       "    </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>18.790000</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",
       "    </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>36.040000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 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  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
       "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
       "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
       "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
       "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
       "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
       "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
       "max         0.304000  ...     36.040000      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",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 32 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      "dtypes: float64(30), int64(1), object(1)\n",
      "memory usage: 142.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By seeing the information of this dataset\n",
    "\n",
    "- All columns have float64 data-type\n",
    "- Target attribute 'diagnosis' has 'object' data-type\n",
    "- It has categorical output (2) variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "diagnosis                  0\n",
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave points_mean        0\n",
       "symmetry_mean              0\n",
       "fractal_dimension_mean     0\n",
       "radius_se                  0\n",
       "texture_se                 0\n",
       "perimeter_se               0\n",
       "area_se                    0\n",
       "smoothness_se              0\n",
       "compactness_se             0\n",
       "concavity_se               0\n",
       "concave points_se          0\n",
       "symmetry_se                0\n",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "area_worst                 0\n",
       "smoothness_worst           0\n",
       "compactness_worst          0\n",
       "concavity_worst            0\n",
       "concave points_worst       0\n",
       "symmetry_worst             0\n",
       "fractal_dimension_worst    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There is not a single null value present in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19d8b47e6d0>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.countplot(x='diagnosis', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is not much data imbalance present\n",
    "- So, we can proceed further without much caring for data imbalance\n",
    "- Or We can handle it by many ways - upsampling/downsampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will go one by one feature\n",
    "\n",
    "- As 'Id' is not carray any important information for our problem so it's better to drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['id'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 'diagnosis' is output variable\n",
    "- It has 2 categories present\n",
    "- B and M\n",
    "- Map it to 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "diag_map = {\n",
    "    \"M\":1,\n",
    "    \"B\":0\n",
    "}\n",
    "\n",
    "data['diagnosis'] = data['diagnosis'].map(diag_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     diagnosis\n",
       "0            1\n",
       "1            1\n",
       "2            1\n",
       "3            1\n",
       "4            1\n",
       "..         ...\n",
       "564          1\n",
       "565          1\n",
       "566          1\n",
       "567          1\n",
       "568          0\n",
       "\n",
       "[569 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['diagnosis']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's Plot the Histogram of remaining features to visualize their distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['diagnosis',\n",
       " 'radius_mean',\n",
       " 'texture_mean',\n",
       " 'perimeter_mean',\n",
       " 'area_mean',\n",
       " 'smoothness_mean',\n",
       " 'compactness_mean',\n",
       " 'concavity_mean',\n",
       " 'concave points_mean',\n",
       " 'symmetry_mean',\n",
       " 'fractal_dimension_mean',\n",
       " 'radius_se',\n",
       " 'texture_se',\n",
       " 'perimeter_se',\n",
       " 'area_se',\n",
       " 'smoothness_se',\n",
       " 'compactness_se',\n",
       " 'concavity_se',\n",
       " 'concave points_se',\n",
       " 'symmetry_se',\n",
       " 'fractal_dimension_se',\n",
       " 'radius_worst',\n",
       " 'texture_worst',\n",
       " 'perimeter_worst',\n",
       " 'area_worst',\n",
       " 'smoothness_worst',\n",
       " 'compactness_worst',\n",
       " 'concavity_worst',\n",
       " 'concave points_worst',\n",
       " 'symmetry_worst',\n",
       " 'fractal_dimension_worst']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_name = list(data.columns)\n",
    "cols_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x3960 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,55))\n",
    "for i in range(1, len(cols_name)):\n",
    "    plt.subplot(10, 3, i)\n",
    "    sns.distplot(data[cols_name[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Many attributes follow 'Normal / Gaussian Distribution'\n",
    "- Though many attributes have 'right skewness' present\n",
    "- We can use StandardScaler() to handle this data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's plot Box plots for outliers visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x3960 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,55))\n",
    "for i in range(1, len(cols_name)):\n",
    "    plt.subplot(10, 3, i)\n",
    "    sns.boxplot(data[cols_name[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Yes, we thought right, There are outliers present\n",
    "- We will handle it with simple mathematics concept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's try to plot Corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,15))\n",
    "sns.heatmap(data[cols_name[1:]].corr(), center = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are some features which are highly corr with each others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>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>fractal_dimension_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>radius_mean</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_mean</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_worst</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_worst</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_worst</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_worst</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_worst</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_worst</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_worst</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_worst</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_worst</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "radius_mean                     True         False            True       True   \n",
       "texture_mean                   False          True           False      False   \n",
       "perimeter_mean                  True         False            True       True   \n",
       "area_mean                       True         False            True       True   \n",
       "smoothness_mean                False         False           False      False   \n",
       "compactness_mean               False         False           False      False   \n",
       "concavity_mean                 False         False           False      False   \n",
       "concave points_mean            False         False           False      False   \n",
       "symmetry_mean                  False         False           False      False   \n",
       "fractal_dimension_mean         False         False           False      False   \n",
       "radius_se                      False         False           False      False   \n",
       "texture_se                     False         False           False      False   \n",
       "perimeter_se                   False         False           False      False   \n",
       "area_se                        False         False           False      False   \n",
       "smoothness_se                  False         False           False      False   \n",
       "compactness_se                 False         False           False      False   \n",
       "concavity_se                   False         False           False      False   \n",
       "concave points_se              False         False           False      False   \n",
       "symmetry_se                    False         False           False      False   \n",
       "fractal_dimension_se           False         False           False      False   \n",
       "radius_worst                    True         False            True       True   \n",
       "texture_worst                  False          True           False      False   \n",
       "perimeter_worst                 True         False            True       True   \n",
       "area_worst                      True         False            True       True   \n",
       "smoothness_worst               False         False           False      False   \n",
       "compactness_worst              False         False           False      False   \n",
       "concavity_worst                False         False           False      False   \n",
       "concave points_worst           False         False           False      False   \n",
       "symmetry_worst                 False         False           False      False   \n",
       "fractal_dimension_worst        False         False           False      False   \n",
       "\n",
       "                         smoothness_mean  compactness_mean  concavity_mean  \\\n",
       "radius_mean                        False             False           False   \n",
       "texture_mean                       False             False           False   \n",
       "perimeter_mean                     False             False           False   \n",
       "area_mean                          False             False           False   \n",
       "smoothness_mean                     True             False           False   \n",
       "compactness_mean                   False              True           False   \n",
       "concavity_mean                     False             False            True   \n",
       "concave points_mean                False             False            True   \n",
       "symmetry_mean                      False             False           False   \n",
       "fractal_dimension_mean             False             False           False   \n",
       "radius_se                          False             False           False   \n",
       "texture_se                         False             False           False   \n",
       "perimeter_se                       False             False           False   \n",
       "area_se                            False             False           False   \n",
       "smoothness_se                      False             False           False   \n",
       "compactness_se                     False             False           False   \n",
       "concavity_se                       False             False           False   \n",
       "concave points_se                  False             False           False   \n",
       "symmetry_se                        False             False           False   \n",
       "fractal_dimension_se               False             False           False   \n",
       "radius_worst                       False             False           False   \n",
       "texture_worst                      False             False           False   \n",
       "perimeter_worst                    False             False           False   \n",
       "area_worst                         False             False           False   \n",
       "smoothness_worst                   False             False           False   \n",
       "compactness_worst                  False             False           False   \n",
       "concavity_worst                    False             False           False   \n",
       "concave points_worst               False             False           False   \n",
       "symmetry_worst                     False             False           False   \n",
       "fractal_dimension_worst            False             False           False   \n",
       "\n",
       "                         concave points_mean  symmetry_mean  \\\n",
       "radius_mean                            False          False   \n",
       "texture_mean                           False          False   \n",
       "perimeter_mean                         False          False   \n",
       "area_mean                              False          False   \n",
       "smoothness_mean                        False          False   \n",
       "compactness_mean                       False          False   \n",
       "concavity_mean                          True          False   \n",
       "concave points_mean                     True          False   \n",
       "symmetry_mean                          False           True   \n",
       "fractal_dimension_mean                 False          False   \n",
       "radius_se                              False          False   \n",
       "texture_se                             False          False   \n",
       "perimeter_se                           False          False   \n",
       "area_se                                False          False   \n",
       "smoothness_se                          False          False   \n",
       "compactness_se                         False          False   \n",
       "concavity_se                           False          False   \n",
       "concave points_se                      False          False   \n",
       "symmetry_se                            False          False   \n",
       "fractal_dimension_se                   False          False   \n",
       "radius_worst                           False          False   \n",
       "texture_worst                          False          False   \n",
       "perimeter_worst                        False          False   \n",
       "area_worst                             False          False   \n",
       "smoothness_worst                       False          False   \n",
       "compactness_worst                      False          False   \n",
       "concavity_worst                        False          False   \n",
       "concave points_worst                    True          False   \n",
       "symmetry_worst                         False          False   \n",
       "fractal_dimension_worst                False          False   \n",
       "\n",
       "                         fractal_dimension_mean  ...  radius_worst  \\\n",
       "radius_mean                               False  ...          True   \n",
       "texture_mean                              False  ...         False   \n",
       "perimeter_mean                            False  ...          True   \n",
       "area_mean                                 False  ...          True   \n",
       "smoothness_mean                           False  ...         False   \n",
       "compactness_mean                          False  ...         False   \n",
       "concavity_mean                            False  ...         False   \n",
       "concave points_mean                       False  ...         False   \n",
       "symmetry_mean                             False  ...         False   \n",
       "fractal_dimension_mean                     True  ...         False   \n",
       "radius_se                                 False  ...         False   \n",
       "texture_se                                False  ...         False   \n",
       "perimeter_se                              False  ...         False   \n",
       "area_se                                   False  ...         False   \n",
       "smoothness_se                             False  ...         False   \n",
       "compactness_se                            False  ...         False   \n",
       "concavity_se                              False  ...         False   \n",
       "concave points_se                         False  ...         False   \n",
       "symmetry_se                               False  ...         False   \n",
       "fractal_dimension_se                      False  ...         False   \n",
       "radius_worst                              False  ...          True   \n",
       "texture_worst                             False  ...         False   \n",
       "perimeter_worst                           False  ...          True   \n",
       "area_worst                                False  ...          True   \n",
       "smoothness_worst                          False  ...         False   \n",
       "compactness_worst                         False  ...         False   \n",
       "concavity_worst                           False  ...         False   \n",
       "concave points_worst                      False  ...         False   \n",
       "symmetry_worst                            False  ...         False   \n",
       "fractal_dimension_worst                   False  ...         False   \n",
       "\n",
       "                         texture_worst  perimeter_worst  area_worst  \\\n",
       "radius_mean                      False             True        True   \n",
       "texture_mean                      True            False       False   \n",
       "perimeter_mean                   False             True        True   \n",
       "area_mean                        False             True        True   \n",
       "smoothness_mean                  False            False       False   \n",
       "compactness_mean                 False            False       False   \n",
       "concavity_mean                   False            False       False   \n",
       "concave points_mean              False            False       False   \n",
       "symmetry_mean                    False            False       False   \n",
       "fractal_dimension_mean           False            False       False   \n",
       "radius_se                        False            False       False   \n",
       "texture_se                       False            False       False   \n",
       "perimeter_se                     False            False       False   \n",
       "area_se                          False            False       False   \n",
       "smoothness_se                    False            False       False   \n",
       "compactness_se                   False            False       False   \n",
       "concavity_se                     False            False       False   \n",
       "concave points_se                False            False       False   \n",
       "symmetry_se                      False            False       False   \n",
       "fractal_dimension_se             False            False       False   \n",
       "radius_worst                     False             True        True   \n",
       "texture_worst                     True            False       False   \n",
       "perimeter_worst                  False             True        True   \n",
       "area_worst                       False             True        True   \n",
       "smoothness_worst                 False            False       False   \n",
       "compactness_worst                False            False       False   \n",
       "concavity_worst                  False            False       False   \n",
       "concave points_worst             False            False       False   \n",
       "symmetry_worst                   False            False       False   \n",
       "fractal_dimension_worst          False            False       False   \n",
       "\n",
       "                         smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "radius_mean                         False              False            False   \n",
       "texture_mean                        False              False            False   \n",
       "perimeter_mean                      False              False            False   \n",
       "area_mean                           False              False            False   \n",
       "smoothness_mean                     False              False            False   \n",
       "compactness_mean                    False              False            False   \n",
       "concavity_mean                      False              False            False   \n",
       "concave points_mean                 False              False            False   \n",
       "symmetry_mean                       False              False            False   \n",
       "fractal_dimension_mean              False              False            False   \n",
       "radius_se                           False              False            False   \n",
       "texture_se                          False              False            False   \n",
       "perimeter_se                        False              False            False   \n",
       "area_se                             False              False            False   \n",
       "smoothness_se                       False              False            False   \n",
       "compactness_se                      False              False            False   \n",
       "concavity_se                        False              False            False   \n",
       "concave points_se                   False              False            False   \n",
       "symmetry_se                         False              False            False   \n",
       "fractal_dimension_se                False              False            False   \n",
       "radius_worst                        False              False            False   \n",
       "texture_worst                       False              False            False   \n",
       "perimeter_worst                     False              False            False   \n",
       "area_worst                          False              False            False   \n",
       "smoothness_worst                     True              False            False   \n",
       "compactness_worst                   False               True            False   \n",
       "concavity_worst                     False              False             True   \n",
       "concave points_worst                False              False            False   \n",
       "symmetry_worst                      False              False            False   \n",
       "fractal_dimension_worst             False              False            False   \n",
       "\n",
       "                         concave points_worst  symmetry_worst  \\\n",
       "radius_mean                             False           False   \n",
       "texture_mean                            False           False   \n",
       "perimeter_mean                          False           False   \n",
       "area_mean                               False           False   \n",
       "smoothness_mean                         False           False   \n",
       "compactness_mean                        False           False   \n",
       "concavity_mean                          False           False   \n",
       "concave points_mean                      True           False   \n",
       "symmetry_mean                           False           False   \n",
       "fractal_dimension_mean                  False           False   \n",
       "radius_se                               False           False   \n",
       "texture_se                              False           False   \n",
       "perimeter_se                            False           False   \n",
       "area_se                                 False           False   \n",
       "smoothness_se                           False           False   \n",
       "compactness_se                          False           False   \n",
       "concavity_se                            False           False   \n",
       "concave points_se                       False           False   \n",
       "symmetry_se                             False           False   \n",
       "fractal_dimension_se                    False           False   \n",
       "radius_worst                            False           False   \n",
       "texture_worst                           False           False   \n",
       "perimeter_worst                         False           False   \n",
       "area_worst                              False           False   \n",
       "smoothness_worst                        False           False   \n",
       "compactness_worst                       False           False   \n",
       "concavity_worst                         False           False   \n",
       "concave points_worst                     True           False   \n",
       "symmetry_worst                          False            True   \n",
       "fractal_dimension_worst                 False           False   \n",
       "\n",
       "                         fractal_dimension_worst  \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                     True  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = data[cols_name[1:]].corr()\n",
    "mask = corr_matrix >= 0.9\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are many 'True' values presents\n",
    "- Let's get all the columns pairs which are highly corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('radius_mean', 'perimeter_mean'),\n",
       " ('radius_mean', 'area_mean'),\n",
       " ('radius_mean', 'radius_worst'),\n",
       " ('radius_mean', 'perimeter_worst'),\n",
       " ('radius_mean', 'area_worst'),\n",
       " ('texture_mean', 'texture_worst'),\n",
       " ('perimeter_mean', 'area_mean'),\n",
       " ('perimeter_mean', 'radius_worst'),\n",
       " ('perimeter_mean', 'perimeter_worst'),\n",
       " ('perimeter_mean', 'area_worst'),\n",
       " ('area_mean', 'radius_worst'),\n",
       " ('area_mean', 'perimeter_worst'),\n",
       " ('area_mean', 'area_worst'),\n",
       " ('concavity_mean', 'concave points_mean'),\n",
       " ('concave points_mean', 'concave points_worst'),\n",
       " ('radius_se', 'perimeter_se'),\n",
       " ('radius_se', 'area_se'),\n",
       " ('perimeter_se', 'area_se'),\n",
       " ('radius_worst', 'perimeter_worst'),\n",
       " ('radius_worst', 'area_worst'),\n",
       " ('perimeter_worst', 'area_worst')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_pairs = []\n",
    "for column1 in cols_name[1:]:\n",
    "    for column2 in cols_name[1:]:\n",
    "        if corr_matrix[column1][column2] >= 0.9 and column1!=column2:\n",
    "            pair = (column1, column2)\n",
    "            if pair not in corr_pairs and pair[::-1] not in corr_pairs:\n",
    "                corr_pairs.append(pair)\n",
    "                \n",
    "corr_pairs                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can notice that the above mentioned pairs are highly correlated with pearson correlation value >= 0.9. \n",
    "- We can eliminate the columns that are highly correlated based on the correlation of the column with the target data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perimeter_worst : 0.7829141371737585\n",
      "radius_worst : 0.7764537785950386\n",
      "texture_mean : 0.4151852998452039\n",
      "concave points_worst : 0.7935660171412691\n",
      "perimeter_se : 0.5561407034314826\n",
      "texture_worst : 0.45690282139679794\n",
      "area_mean : 0.7089838365853892\n",
      "area_worst : 0.7338250349210506\n",
      "concave points_mean : 0.7766138400204348\n",
      "perimeter_mean : 0.7426355297258322\n",
      "radius_mean : 0.7300285113754558\n",
      "radius_se : 0.5671338208247172\n",
      "area_se : 0.5482359402780236\n",
      "concavity_mean : 0.6963597071719051\n"
     ]
    }
   ],
   "source": [
    "cols = list(set([col for i in corr_pairs for col in i]))\n",
    "\n",
    "for col in cols:\n",
    "    print('{} : {}'.format(col, data[col].corr(data['diagnosis'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_col = [col for col in cols if data[col].corr(data['diagnosis'])>=0.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['perimeter_worst',\n",
       " 'radius_worst',\n",
       " 'concave points_worst',\n",
       " 'area_mean',\n",
       " 'area_worst',\n",
       " 'concave points_mean',\n",
       " 'perimeter_mean',\n",
       " 'radius_mean']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We will keep only columns that have corr>=0.7 with 'diagnosis' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 25)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in cols:\n",
    "    if col not in list_col:\n",
    "        data.drop(columns=col, inplace = True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>texture_se</th>\n",
       "      <th>...</th>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <th>radius_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>1</td>\n",
       "      <td>17.99</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>25.38</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>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>24.99</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>1</td>\n",
       "      <td>19.69</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>23.57</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>1</td>\n",
       "      <td>11.42</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>14.91</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>1</td>\n",
       "      <td>20.29</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>22.54</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 × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  radius_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0          1        17.99          122.80     1001.0          0.11840   \n",
       "1          1        20.57          132.90     1326.0          0.08474   \n",
       "2          1        19.69          130.00     1203.0          0.10960   \n",
       "3          1        11.42           77.58      386.1          0.14250   \n",
       "4          1        20.29          135.10     1297.0          0.10030   \n",
       "\n",
       "   compactness_mean  concave points_mean  symmetry_mean  \\\n",
       "0           0.27760              0.14710         0.2419   \n",
       "1           0.07864              0.07017         0.1812   \n",
       "2           0.15990              0.12790         0.2069   \n",
       "3           0.28390              0.10520         0.2597   \n",
       "4           0.13280              0.10430         0.1809   \n",
       "\n",
       "   fractal_dimension_mean  texture_se  ...  fractal_dimension_se  \\\n",
       "0                 0.07871      0.9053  ...              0.006193   \n",
       "1                 0.05667      0.7339  ...              0.003532   \n",
       "2                 0.05999      0.7869  ...              0.004571   \n",
       "3                 0.09744      1.1560  ...              0.009208   \n",
       "4                 0.05883      0.7813  ...              0.005115   \n",
       "\n",
       "   radius_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0         25.38           184.60      2019.0            0.1622   \n",
       "1         24.99           158.80      1956.0            0.1238   \n",
       "2         23.57           152.50      1709.0            0.1444   \n",
       "3         14.91            98.87       567.7            0.2098   \n",
       "4         22.54           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  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[data.columns[1:]]\n",
    "y = data['diagnosis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without handle Outliers and Data Imbalanced\n",
    "\n",
    "### Since we have algorithms available which are not sensitive to outliers and/or data imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random-forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test Data: 94.73684210526315%\n",
      "Precision Score: 0.9512195121951219\n",
      "Recall Score: 0.9069767441860465\n",
      "F1 Score: 0.9285714285714286\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96        71\n",
      "           1       0.95      0.91      0.93        43\n",
      "\n",
      "    accuracy                           0.95       114\n",
      "   macro avg       0.95      0.94      0.94       114\n",
      "weighted avg       0.95      0.95      0.95       114\n",
      "\n",
      "\n",
      "Accuracy on Whole Data: 98.94551845342706%\n",
      "Precision Score: 0.9904761904761905\n",
      "Recall Score: 0.9811320754716981\n",
      "F1 Score: 0.9857819905213271\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       357\n",
      "           1       0.99      0.98      0.99       212\n",
      "\n",
      "    accuracy                           0.99       569\n",
      "   macro avg       0.99      0.99      0.99       569\n",
      "weighted avg       0.99      0.99      0.99       569\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_pipeline = make_pipeline(StandardScaler(), RandomForestClassifier(random_state = 18))\n",
    "rf_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Accuray On Test Data\n",
    "predictions = rf_pipeline.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\"Accuracy on Test Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y_test, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y_test, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y_test, predictions)}\")\n",
    "plot_confusion_matrix(rf_pipeline, X_test, y_test)\n",
    "plt.title(\"Confusion Matrix for Test Data\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, predictions))\n",
    "\n",
    "print()\n",
    "\n",
    "# Accuray On Whole Data\n",
    "predictions = rf_pipeline.predict(X.values)\n",
    "accuracy = accuracy_score(y, predictions)\n",
    "print(f\"Accuracy on Whole Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y, predictions)}\")\n",
    "plot_confusion_matrix(rf_pipeline, X.values, y)\n",
    "plt.title(\"Confusion Matrix for Whole Data\")\n",
    "plt.show()\n",
    "print(classification_report(y, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rkshi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:27:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Accuracy on Test Data: 94.73684210526315%\n",
      "Precision Score: 0.9512195121951219\n",
      "Recall Score: 0.9069767441860465\n",
      "F1 Score: 0.9285714285714286\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96        71\n",
      "           1       0.95      0.91      0.93        43\n",
      "\n",
      "    accuracy                           0.95       114\n",
      "   macro avg       0.95      0.94      0.94       114\n",
      "weighted avg       0.95      0.95      0.95       114\n",
      "\n",
      "\n",
      "Accuracy on Whole Data: 98.94551845342706%\n",
      "Precision Score: 0.9904761904761905\n",
      "Recall Score: 0.9811320754716981\n",
      "F1 Score: 0.9857819905213271\n"
     ]
    },
    {
     "data": {
      "image/png": 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AzDX88P6FXD7naUbuvo0rvjgOgJFjtnH9Qwu5fM4zfPzCF7j4k3uzeWN9NPd7i4GDm/mPK5fwvfPHsWVT7b+kPU3rib55plqr5jcr1wMiIuKKiJgWEdNG71bfH6ahuzRz0Ds38dDvhzFidBMNDdCnD5xw2ss8PT9rCfQfEAxPAw+T3v4q4yZu5YXnBtQybCvR0Df4jyuX8LubRnDfbbvWOpwey13ULjwgoh6tW9vApvVZYm58Vcy7ZxgT9m1k7ao3xm/+cNsuTNz/tdfXb04N1ZXP9+eFxf3ZY6+tOz1ua0/wmUuXsezZgdx0hUdPO9I6iloPLbhqjqI+BExKD4d4geyJ1B+u4v5q4uVV/bjk03vR0iJaWuDID67jsPdu4Guf2os/LRiElB3POftrWW/98T8O5Uf/tQcNfaGhT3D2xcsZPqI+u+ZFM2X6Zo499RWeWziQy+dko9w//MpYHvrd8BpH1vPUyyhq1RJcRDRJOgu4g+w0kasjYkG19lcrb5n8GpfPeeZN5ed+a2m76x9x4nqOOHF9tcOyLljw4FCOG3dQrcPo8SJEU29PcAARcStwazX3YWY7X0/ofubhKxnMrCL1dCVDfbQzzaxH6Y5BBkkTJP1e0pOSFkj6dCq/UNILkuan6f0l25wnaZGkpyUd11mcbsGZWUW68YaXTcA5ETFP0jDgEUlz0rKvR8QlpSunK6FmAFOAccCdkvYr9/Bnt+DMrGLdcR5cRKyMiHnp9UbgSdq5GKDEScCNEdEYEYuBRWRXTHXICc7MKhIBTS19ck3AqNYrldJ0Rnt1SppI9pT7B1LRWZIek3S1pBGpLNfVUaXcRTWzilXQRV0TEdPKrSBpKPBz4J8jYoOk7wJfIhvP+BJwKfAP5Lw6qpQTnJlVpDsfOiOpH1ly+3FE3AQQEatKlv8A+HWarfjqKHdRzaxiEco1lSNJwFXAkxFxWUn52JLVTgGeSK9vBmZIGpCukJoEPFhuH27BmVnFuulC+ncBHwEelzQ/lX2B7N6RU8m6n0uAjwNExAJJs4GFZCOwZ5YbQQUnODOrUET3nOgbEffS/nG1Dq9+iohZwKy8+3CCM7MKiWY/NtDMiqqz42s9hROcmVWknq5FdYIzs8pEdhyuHjjBmVnFesLtyPNwgjOzioQHGcysyNxFNbPC8iiqmRVShBOcmRWYTxMxs8LyMTgzK6RAtHgU1cyKqk4acE5wZlYhDzKYWaHVSRPOCc7MKlb3LThJ36JMno6Is6sSkZn1aAG0tNR5ggMe3mlRmFn9CKDeW3ARcW3pvKQhEbG5+iGZWU9XL+fBdXoyi6R3SlpI9tRpJB0k6fKqR2ZmPVfknGosz9l63wCOA9YCRMSjwJHVDMrMerJ8jwzsCQMRuUZRI2JZ9gjD15V9VJeZFVwPaJ3lkacFt0zS4UBI6i/ps6Tuqpn1QgHRolxTOZImSPq9pCclLZD06VQ+UtIcSc+m/0eUbHOepEWSnpZ0XGeh5klwnwDOBMYDLwBT07yZ9VrKOZXVBJwTEW8DDgPOlDQZ+DxwV0RMAu5K86RlM4ApwPHA5ZIayu2g0y5qRKwBTutsPTPrRbqhixoRK4GV6fVGSU+SNaROAo5Kq10L3A38ayq/MSIagcWSFgHTgfs72keeUdS3SLpF0kuSVkv6laS3dP3HMrO6l38UdZSkh0umM9qrTtJE4GDgAWD3lPxak+CYtNp4YFnJZstTWYfyDDL8BPgOcEqanwHcALwjx7ZmVjSVnei7JiKmlVtB0lDg58A/R8SGNgOa263aQTQdynMMThFxXUQ0pen6zio1s2KLyDd1RlI/suT244i4KRWvkjQ2LR8LrE7ly4EJJZvvCawoV3+HCS6NZIwEfi/p85ImStpb0rnAbzoP3cwKq0X5pjKUNdWuAp6MiMtKFt0MzEyvZwK/KimfIWmApH2AScCD5fZRrov6CFlLrTXKj5csC+BLZaM3s8JS9/Th3gV8BHhc0vxU9gXgYmC2pNOBpcCpABGxQNJsYCHZCOyZEVH2nNxy16Lus+Pxm1nhdNNlWBFxLx2fS3JMB9vMAmbl3UeuKxkkHQhMBgaW7OhHeXdiZkWi+r+bSCtJF5CdkzIZuBU4AbgXcIIz663qZJgxzyjqX5E1F1+MiI8CBwEDqhqVmfVsLTmnGsvTRX01IlokNUkaTjZk6xN9zXqrItzwssTDknYFfkA2srqJToZmzazYumkUteryXIv6yfTye5JuB4ZHxGPVDcvMerR6T3CSDim3LCLmVSckM7PuUa4Fd2mZZQEc3c2x8Mxjgzlu/MHdXa1V0aLrptY6BKtA43/8oVvqqfsuakS8Z2cGYmZ1Iuj0Mqyewg9+NrPK1XsLzsysI3XfRTUz61CdJLg8d/SVpL+VdH6a30vS9OqHZmY9VoGei3o58E7gQ2l+I9kdfs2sF1Lkn2otTxf1HRFxiKT/A4iIVyT1r3JcZtaTFWgUdVt6NFcASBpNj7iM1sxqpSe0zvLI00X9JvALYIykWWS3SvrPqkZlZj1bnRyDy3Mt6o8lPUJ2yyQBJ0eEn2xv1lv1kONreeS54eVewBbgltKyiFhazcDMrAcrSoIje4JW68NnBgL7AE8DU6oYl5n1YKqTo/B5uqh/Vjqf7jLy8Q5WNzPrMfIMMmwn3Sbp0CrEYmb1opsGGSRdLWm1pCdKyi6U9IKk+Wl6f8my8yQtkvS0pOM6qz/PMbjPlMz2AQ4BXuo8dDMrpO4dZLgG+DZvfojV1yPiktICSZOBGWSHx8YBd0rar9yzUfO04IaVTAPIjsmdlDd6MyugbmrBRcRc4OWcez0JuDEiGiNiMbAIKHvZaNkWXDrBd2hEfC5nAGbWG1R/FPUsSX8HPAycExGvAOOBP5asszyVdajDFpykvqnp1+Gty82s9xHZKGqeCRgl6eGS6Ywcu/gu8FZgKrCSN+4u3t71YWVTbbkW3INkyW2+pJuBnwGbX6814qYcgZpZ0VR2DG5NREyrqPqIVa2vJf0A+HWaXQ5MKFl1T2BFubrynAc3ElhL9gyG1vPhAnCCM+utqthFlTQ2Ilam2VOA1hHWm4GfSLqMbJBhEp08wrRcghuTRlCf4I3E1qpOzmM2s6ropgwg6QbgKLKu7HLgAuAoSVPTXpaQzruNiAWSZgMLgSbgzHIjqFA+wTUAQ+lCv9fMiq27ThOJiA+1U3xVmfVnAbPy1l8uwa2MiIvyVmRmvUidNHHKJbj6uKOdme1cUYxrUY/ZaVGYWX2p9xZcROQ9u9jMepnC3A/OzOxNnODMrJB6yO3I83CCM7OKCHdRzazAnODMrLic4MyssJzgzKyQivTYQDOzN3GCM7OiKsKlWmZm7XIX1cyKySf6mlmhOcGZWRH5SgYzKzS11EeGc4Izs8r4GJyZFZm7qGZWXE5wZlZU9dKC61PrAMysDkXOqROSrpa0WtITJWUjJc2R9Gz6f0TJsvMkLZL0tKTjOqvfCc7MKpOeqpVnyuEa4Pg2ZZ8H7oqIScBdaR5Jk4EZwJS0zeWSGspV7gRnZhVpPQ8uz9SZiJgLtH3A1UnAten1tcDJJeU3RkRjRCwGFgHTy9XvBGdmlYvIN8EoSQ+XTGfkqH33iFiZ7SZWAmNS+XhgWcl6y1NZhzzIYGYVq2CQYU1ETOuu3bZTVjYSJ7gq69Mn+NZtz7D2xX6cP/MttQ7HgL5rtzLm+8/Td/02QmLDe3Zj/XFj6LOpiT2+vYS+a7bSNKo/L35qIi1D+kJTMOaqpQxYsgW1BBvfNZJX/mKPWv8YtVP9E31XSRobESsljQVWp/LlwISS9fYEVpSrqGpd1PZGR3qjkz/2EsueHVDrMKxENIi1Hx7P0q9OZvkF+7HLnWvo98KrjLhlFVumDGXpJZPZMmUoI25ZBcDQB19B21pY9pW3seyiAxj++7X0famxxj9FbXXjIEN7bgZmptczgV+VlM+QNEDSPsAk4MFyFVXzGNw1vHl0pFcZNXYr04/ZwG037FbrUKxE8679aJw4GIAY1MDWcQPp+/I2hsxbz8Yjst/VxiN2Y8gj67MNBH0aW6A50NYWoq9oGVR28K7wuivBSboBuB/YX9JySacDFwPvlfQs8N40T0QsAGYDC4HbgTMjorlc/VXrokbEXEkTq1V/PfjEF1/gyi+PY/DQsr8Dq6G+LzUy4PktvLbvEBo2NNG8az8gS4ING5oA2HToCIY8sp59PvUEamxhzWnjaRnai4/uBK0DCDteVcSHOlh0TAfrzwJm5a2/5qOoks5oHWHZRnGa/e84dj3r1vRl0eODax2KdUCvNbPHNxez5rQ9iTItsoHPbYY+YvE3D+T5yyaz622r6bu6OJ/Vruiu00SqreZ/hiLiCuAKgOEa2QPeku4xedpmDnvfBg49egH9BwSDhzVz7jef52tn713r0AygKRj7zcVsOnwkmw/dFYDm4X1pWLcta72t20bz8OzrMfQPr7Dl7cOhr2jepR+v7TeEgYu3sGlMLz62Wiff1Jq34IrqhxeP42+nTWHmYVP4yif35tH7hjm59RQRjLnyebaOG8i6E8a8Xrz5kF0Yds9aAIbds5bNh+wCQNOo/gxauBEi0GvNDFy0ha1jB9Yk9J6gO0/0rbaat+DMdraBz2xm+H2v0DhhIBP+7SkA1p46llc+sDt7fHsxw//3ZZp268eLn9oHgPXHjmL3K5Yy4bynUMCGI0eyda9BtfwRaivCN7xMoyNHkZ3JvBy4ICKuqtb+erLH7h/GY/cPq3UYlry2/1AWXXdwu8tWnDfpTWUxsIEXz96n2mHVl/rIb1UdRe1odMTM6lxP6H7m4S6qmVUmgN7eRTWzAquP/OYEZ2aVcxfVzAqr14+imllB+bGBZlZU2Ym+9ZHhnODMrHJdvxXSTuUEZ2YVcwvOzIrJx+DMrLh8LaqZFZm7qGZWSLFDz1vYqZzgzKxybsGZWWHVR35zgjOzyqmlPvqoTnBmVpnAJ/qaWTGJ6LYTfSUtATYCzUBTREyTNBL4KTARWAL8dUS80pX6/dAZM6tcRL4pn/dExNSImJbmPw/cFRGTgLvSfJc4wZlZ5bo3wbV1EnBten0tcHJXK3KCM7PKtB6DyzNlD516uGQ6o53afivpkZJlu0fESoD0/xi6yMfgzKxiFYyirinperbnXRGxQtIYYI6kp3Y8uje4BWdmFcrZPc3RRY2IFen/1cAvgOnAKkljAdL/q7saqROcmVUm6JYEJ2mIpGGtr4H3AU8ANwMz02ozgV91NVR3Uc2sct1zHtzuwC8kQZaLfhIRt0t6CJgt6XRgKXBqV3fgBGdmFeuO8+Ai4jngoHbK1wLH7PAOcIIzs67wxfZmVkgR0Fwf12o5wZlZ5dyCM7PCcoIzs0IKwM9kMLNiCggfgzOzIgo8yGBmBeZjcGZWWE5wZlZMO3Svt53KCc7MKhOAHzpjZoXlFpyZFZMv1TKzogoInwdnZoXlKxnMrLB8DM7MCinCo6hmVmBuwZlZMQXR3FzrIHJxgjOzyvh2SWZWaD5NxMyKKIBwC87MCil8w0szK7B6GWRQ9KDhXkkvAc/XOo4qGAWsqXUQVpGi/s72jojRO1KBpNvJ3p881kTE8Tuyvx3RoxJcUUl6OCKm1ToOy8+/s2LoU+sAzMyqxQnOzArLCW7nuKLWAVjF/DsrAB+DM7PCcgvOzArLCc7MCssJrookHS/paUmLJH2+1vFY5yRdLWm1pCdqHYvtOCe4KpHUAHwHOAGYDHxI0uTaRmU5XAPU7MRU615OcNUzHVgUEc9FxFbgRuCkGsdknYiIucDLtY7DuocTXPWMB5aVzC9PZWa2kzjBVY/aKfM5OWY7kRNc9SwHJpTM7wmsqFEsZr2SE1z1PARMkrSPpP7ADODmGsdk1qs4wVVJRDQBZwF3AE8CsyNiQW2jss5IugG4H9hf0nJJp9c6Jus6X6plZoXlFpyZFZYTnJkVlhOcmRWWE5yZFZYTnJkVlhNcHZHULGm+pCck/UzS4B2o6xpJf5VeX1nuRgCSjpJ0eBf2sUTSm56+1FF5m3U2VbivCyV9ttIYrdic4OrLqxExNSIOBLYCnyhdmO5gUrGI+FhELCyzylFAxQnOrNac4OrXPcC+qXX1e0k/AR6X1CDpv+eezBMAAAKdSURBVCQ9JOkxSR8HUObbkhZK+g0wprUiSXdLmpZeHy9pnqRHJd0laSJZIv2X1Ho8QtJoST9P+3hI0rvStrtJ+q2k/5P0fdq/Hnc7kn4p6RFJCySd0WbZpSmWuySNTmVvlXR72uYeSQd0x5tpxeQn29chSX3J7jN3eyqaDhwYEYtTklgfEYdKGgDcJ+m3wMHA/sCfAbsDC4Gr29Q7GvgBcGSqa2REvCzpe8CmiLgkrfcT4OsRca+kvciu1ngbcAFwb0RcJOlEYLuE1YF/SPsYBDwk6ecRsRYYAsyLiHMknZ/qPovsYTCfiIhnJb0DuBw4ugtvo/UCTnD1ZZCk+en1PcBVZF3HByNicSp/H/D21uNrwC7AJOBI4IaIaAZWSPpdO/UfBsxtrSsiOrov2rHAZOn1BtpwScPSPv4ybfsbSa/k+JnOlnRKej0hxboWaAF+msqvB26SNDT9vD8r2feAHPuwXsoJrr68GhFTSwvSF31zaRHwqYi4o81676fz2zUpxzqQHdp4Z0S82k4sua/9k3QUWbJ8Z0RskXQ3MLCD1SPtd13b98CsIz4GVzx3AP8kqR+ApP0kDQHmAjPSMbqxwHva2fZ+4N2S9knbjkzlG4FhJev9lqy7SFqvNeHMBU5LZScAIzqJdRfglZTcDiBrQbbqA7S2Qj9M1vXdACyWdGrahyQd1Mk+rBdzgiueK8mOr81LD075PllL/RfAs8DjwHeB/227YUS8RHbc7CZJj/JGF/EW4JTWQQbgbGBaGsRYyBujuV8EjpQ0j6yrvLSTWG8H+kp6DPgS8MeSZZuBKZIeITvGdlEqPw04PcW3AN8G3srw3UTMrLDcgjOzwnKCM7PCcoIzs8JygjOzwnKCM7PCcoIzs8JygjOzwvr/eDH9wJGuz58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       357\n",
      "           1       0.99      0.98      0.99       212\n",
      "\n",
      "    accuracy                           0.99       569\n",
      "   macro avg       0.99      0.99      0.99       569\n",
      "weighted avg       0.99      0.99      0.99       569\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_pipeline = make_pipeline(StandardScaler(), XGBClassifier(random_state = 18))\n",
    "xgb_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Accuray On Test Data\n",
    "predictions = xgb_pipeline.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\"Accuracy on Test Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y_test, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y_test, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y_test, predictions)}\")\n",
    "plot_confusion_matrix(xgb_pipeline, X_test, y_test)\n",
    "plt.title(\"Confusion Matrix for Test Data\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, predictions))\n",
    "\n",
    "print()\n",
    "\n",
    "# Accuray On Whole Data\n",
    "predictions = xgb_pipeline.predict(X.values)\n",
    "accuracy = accuracy_score(y, predictions)\n",
    "print(f\"Accuracy on Whole Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y, predictions)}\")\n",
    "plot_confusion_matrix(xgb_pipeline, X.values, y)\n",
    "plt.title(\"Confusion Matrix for Whole Data\")\n",
    "plt.show()\n",
    "print(classification_report(y, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optional : Way to deal with outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are going to use Maths...!!\n",
    "\n",
    "- Get Q1 and Q3 of every columns\n",
    "- Find out IQR = Q3-Q1\n",
    "- Calculate lower_bound and upper_bound\n",
    "- If datapoint's values fall below lower_bound => Replace it with lower_bound itself\n",
    "- If datapoint's values fall below upper_bound => Replace it with upper_bound itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_outlier(data):\n",
    "        data_copy = data.copy()\n",
    "        data = data._get_numeric_data()\n",
    "\n",
    "\n",
    "        q1 = data.quantile(0.25)\n",
    "        q3 = data.quantile(0.75)\n",
    "\n",
    "        iqr = q3 - q1\n",
    "\n",
    "        lower_bound = q1 -(1.5 * iqr) \n",
    "        upper_bound = q3 +(1.5 * iqr)\n",
    "\n",
    "\n",
    "        for col in df.columns:\n",
    "            for i in range(0,len(data[col])):\n",
    "                if data[col][i] < lower_bound[col]:            \n",
    "                    data[col][i] = lower_bound[col]\n",
    "\n",
    "                if data[col][i] > upper_bound[col]:            \n",
    "                    data[col][i] = upper_bound[col]    \n",
    "\n",
    "\n",
    "        for col in data.columns:\n",
    "            data_copy[col] = data[col]\n",
    "\n",
    "        return(data_copy)\n",
    "\n",
    "X = handle_outlier(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random-forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test Data: 94.73684210526315%\n",
      "Precision Score: 0.9512195121951219\n",
      "Recall Score: 0.9069767441860465\n",
      "F1 Score: 0.9285714285714286\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96        71\n",
      "           1       0.95      0.91      0.93        43\n",
      "\n",
      "    accuracy                           0.95       114\n",
      "   macro avg       0.95      0.94      0.94       114\n",
      "weighted avg       0.95      0.95      0.95       114\n",
      "\n",
      "\n",
      "Accuracy on Whole Data: 98.94551845342706%\n",
      "Precision Score: 0.9904761904761905\n",
      "Recall Score: 0.9811320754716981\n",
      "F1 Score: 0.9857819905213271\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       357\n",
      "           1       0.99      0.98      0.99       212\n",
      "\n",
      "    accuracy                           0.99       569\n",
      "   macro avg       0.99      0.99      0.99       569\n",
      "weighted avg       0.99      0.99      0.99       569\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_pipeline = make_pipeline(StandardScaler(), RandomForestClassifier(random_state = 18))\n",
    "rf_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Accuray On Test Data\n",
    "predictions = rf_pipeline.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\"Accuracy on Test Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y_test, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y_test, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y_test, predictions)}\")\n",
    "plot_confusion_matrix(rf_pipeline, X_test, y_test)\n",
    "plt.title(\"Confusion Matrix for Test Data\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, predictions))\n",
    "\n",
    "print()\n",
    "\n",
    "# Accuray On Whole Data\n",
    "predictions = rf_pipeline.predict(X.values)\n",
    "accuracy = accuracy_score(y, predictions)\n",
    "print(f\"Accuracy on Whole Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y, predictions)}\")\n",
    "plot_confusion_matrix(rf_pipeline, X.values, y)\n",
    "plt.title(\"Confusion Matrix for Whole Data\")\n",
    "plt.show()\n",
    "print(classification_report(y, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:27:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Accuracy on Test Data: 93.85964912280701%\n",
      "Precision Score: 0.95\n",
      "Recall Score: 0.8837209302325582\n",
      "F1 Score: 0.9156626506024096\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rkshi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95        71\n",
      "           1       0.95      0.88      0.92        43\n",
      "\n",
      "    accuracy                           0.94       114\n",
      "   macro avg       0.94      0.93      0.93       114\n",
      "weighted avg       0.94      0.94      0.94       114\n",
      "\n",
      "\n",
      "Accuracy on Whole Data: 98.76977152899823%\n",
      "Precision Score: 0.9904306220095693\n",
      "Recall Score: 0.9764150943396226\n",
      "F1 Score: 0.9833729216152018\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       357\n",
      "           1       0.99      0.98      0.98       212\n",
      "\n",
      "    accuracy                           0.99       569\n",
      "   macro avg       0.99      0.99      0.99       569\n",
      "weighted avg       0.99      0.99      0.99       569\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_pipeline = make_pipeline(StandardScaler(), XGBClassifier(random_state = 18))\n",
    "xgb_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Accuray On Test Data\n",
    "predictions = xgb_pipeline.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\"Accuracy on Test Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y_test, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y_test, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y_test, predictions)}\")\n",
    "plot_confusion_matrix(xgb_pipeline, X_test, y_test)\n",
    "plt.title(\"Confusion Matrix for Test Data\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, predictions))\n",
    "\n",
    "print()\n",
    "\n",
    "# Accuray On Whole Data\n",
    "predictions = xgb_pipeline.predict(X.values)\n",
    "accuracy = accuracy_score(y, predictions)\n",
    "print(f\"Accuracy on Whole Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y, predictions)}\")\n",
    "plot_confusion_matrix(xgb_pipeline, X.values, y)\n",
    "plt.title(\"Confusion Matrix for Whole Data\")\n",
    "plt.show()\n",
    "print(classification_report(y, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 286, 1: 169})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter = Counter(y_train)\n",
    "counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optional : Way to handle imbalanced dataset\n",
    "\n",
    "### Since we don't have much data available, We can only Upsample the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[data.columns[1:]]\n",
    "y = data['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 286, 1: 286})\n"
     ]
    }
   ],
   "source": [
    "upsample = SMOTE()\n",
    "X_train, y_train = upsample.fit_resample(X_train, y_train)\n",
    "counter = Counter(y_train)\n",
    "print(counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random-forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test Data: 97.36842105263158%\n",
      "Precision Score: 0.9545454545454546\n",
      "Recall Score: 0.9767441860465116\n",
      "F1 Score: 0.9655172413793104\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98        71\n",
      "           1       0.95      0.98      0.97        43\n",
      "\n",
      "    accuracy                           0.97       114\n",
      "   macro avg       0.97      0.97      0.97       114\n",
      "weighted avg       0.97      0.97      0.97       114\n",
      "\n",
      "\n",
      "Accuracy on Whole Data: 99.47275922671353%\n",
      "Precision Score: 0.9906103286384976\n",
      "Recall Score: 0.9952830188679245\n",
      "F1 Score: 0.9929411764705881\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00       357\n",
      "           1       0.99      1.00      0.99       212\n",
      "\n",
      "    accuracy                           0.99       569\n",
      "   macro avg       0.99      0.99      0.99       569\n",
      "weighted avg       0.99      0.99      0.99       569\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_pipeline = make_pipeline(StandardScaler(), RandomForestClassifier(random_state = 18))\n",
    "rf_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Accuray On Test Data\n",
    "predictions = rf_pipeline.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\"Accuracy on Test Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y_test, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y_test, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y_test, predictions)}\")\n",
    "plot_confusion_matrix(rf_pipeline, X_test, y_test)\n",
    "plt.title(\"Confusion Matrix for Test Data\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, predictions))\n",
    "\n",
    "print()\n",
    "\n",
    "# Accuray On Whole Data\n",
    "predictions = rf_pipeline.predict(X.values)\n",
    "accuracy = accuracy_score(y, predictions)\n",
    "print(f\"Accuracy on Whole Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y, predictions)}\")\n",
    "plot_confusion_matrix(rf_pipeline, X.values, y)\n",
    "plt.title(\"Confusion Matrix for Whole Data\")\n",
    "plt.show()\n",
    "print(classification_report(y, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:27:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Accuracy on Test Data: 94.73684210526315%\n",
      "Precision Score: 0.9302325581395349\n",
      "Recall Score: 0.9302325581395349\n",
      "F1 Score: 0.9302325581395349\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rkshi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96        71\n",
      "           1       0.93      0.93      0.93        43\n",
      "\n",
      "    accuracy                           0.95       114\n",
      "   macro avg       0.94      0.94      0.94       114\n",
      "weighted avg       0.95      0.95      0.95       114\n",
      "\n",
      "\n",
      "Accuracy on Whole Data: 98.94551845342706%\n",
      "Precision Score: 0.9858490566037735\n",
      "Recall Score: 0.9858490566037735\n",
      "F1 Score: 0.9858490566037735\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       357\n",
      "           1       0.99      0.99      0.99       212\n",
      "\n",
      "    accuracy                           0.99       569\n",
      "   macro avg       0.99      0.99      0.99       569\n",
      "weighted avg       0.99      0.99      0.99       569\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_pipeline = make_pipeline(StandardScaler(), XGBClassifier(random_state = 18))\n",
    "xgb_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Accuray On Test Data\n",
    "predictions = xgb_pipeline.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\"Accuracy on Test Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y_test, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y_test, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y_test, predictions)}\")\n",
    "plot_confusion_matrix(xgb_pipeline, X_test, y_test)\n",
    "plt.title(\"Confusion Matrix for Test Data\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, predictions))\n",
    "\n",
    "print()\n",
    "\n",
    "# Accuray On Whole Data\n",
    "predictions = xgb_pipeline.predict(X.values)\n",
    "accuracy = accuracy_score(y, predictions)\n",
    "print(f\"Accuracy on Whole Data: {accuracy*100}%\")\n",
    "print(f\"Precision Score: {precision_score(y, predictions)}\")\n",
    "print(f\"Recall Score: {recall_score(y, predictions)}\")\n",
    "print(f\"F1 Score: {f1_score(y, predictions)}\")\n",
    "plot_confusion_matrix(xgb_pipeline, X.values, y)\n",
    "plt.title(\"Confusion Matrix for Whole Data\")\n",
    "plt.show()\n",
    "print(classification_report(y, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion :\n",
    "### Random forest performed well compare to XGBoost\n",
    "### Handling outliers didn't help well\n",
    "### By just handling data imbalancing we got very good Accuracy."
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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