Machine learning- Breast Cancer Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/ingledarshan/AIML-B2/main/data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 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",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     NaN\n",
       "1     NaN\n",
       "2     NaN\n",
       "3     NaN\n",
       "4     NaN\n",
       "       ..\n",
       "564   NaN\n",
       "565   NaN\n",
       "566   NaN\n",
       "567   NaN\n",
       "568   NaN\n",
       "Name: Unnamed: 32, Length: 569, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Unnamed: 32']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Unnamed: 32\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('id', axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
     ]
    }
   ],
   "source": [
    "l= list(df.columns)\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_mean = l[1:11]\n",
    "\n",
    "features_se = l[11:21]\n",
    "\n",
    "features_worst =l[21:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']\n"
     ]
    }
   ],
   "source": [
    "print(features_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se']\n"
     ]
    }
   ],
   "source": [
    "print(features_se)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
     ]
    }
   ],
   "source": [
    "print(features_worst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>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>0</th>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.8</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.6</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>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.9</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.8</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38           122.8     1001.0   \n",
       "1         M        20.57         17.77           132.9     1326.0   \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",
       "\n",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33            184.6   \n",
       "1         0.1812  ...         24.99          23.41            158.8   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['diagnosis'],label=\"count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>count</th>\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",
       "      <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>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>0.062798</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>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>0.007060</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>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>0.049960</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>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>0.057700</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>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>0.061540</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>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>0.066120</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>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>0.097440</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 × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      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  fractal_dimension_mean  ...  radius_worst  \\\n",
       "count     569.000000              569.000000  ...    569.000000   \n",
       "mean        0.181162                0.062798  ...     16.269190   \n",
       "std         0.027414                0.007060  ...      4.833242   \n",
       "min         0.106000                0.049960  ...      7.930000   \n",
       "25%         0.161900                0.057700  ...     13.010000   \n",
       "50%         0.179200                0.061540  ...     14.970000   \n",
       "75%         0.195700                0.066120  ...     18.790000   \n",
       "max         0.304000                0.097440  ...     36.040000   \n",
       "\n",
       "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
       "count     569.000000       569.000000   569.000000        569.000000   \n",
       "mean       25.677223       107.261213   880.583128          0.132369   \n",
       "std         6.146258        33.602542   569.356993          0.022832   \n",
       "min        12.020000        50.410000   185.200000          0.071170   \n",
       "25%        21.080000        84.110000   515.300000          0.116600   \n",
       "50%        25.410000        97.660000   686.500000          0.131300   \n",
       "75%        29.720000       125.400000  1084.000000          0.146000   \n",
       "max        49.540000       251.200000  4254.000000          0.222600   \n",
       "\n",
       "       compactness_worst  concavity_worst  concave points_worst  \\\n",
       "count         569.000000       569.000000            569.000000   \n",
       "mean            0.254265         0.272188              0.114606   \n",
       "std             0.157336         0.208624              0.065732   \n",
       "min             0.027290         0.000000              0.000000   \n",
       "25%             0.147200         0.114500              0.064930   \n",
       "50%             0.211900         0.226700              0.099930   \n",
       "75%             0.339100         0.382900              0.161400   \n",
       "max             1.058000         1.252000              0.291000   \n",
       "\n",
       "       symmetry_worst  fractal_dimension_worst  \n",
       "count      569.000000               569.000000  \n",
       "mean         0.290076                 0.083946  \n",
       "std          0.061867                 0.018061  \n",
       "min          0.156500                 0.055040  \n",
       "25%          0.250400                 0.071460  \n",
       "50%          0.282200                 0.080040  \n",
       "75%          0.317900                 0.092080  \n",
       "max          0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>1.000000</td>\n",
       "      <td>0.323782</td>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.170581</td>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.147741</td>\n",
       "      <td>-0.311631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.007066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>0.323782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>...</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.119205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.969476</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.051019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.003738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>0.170581</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.499316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.687382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.514930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_mean</th>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.368661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>0.147741</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185728</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.438413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <td>-0.311631</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.253691</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>0.767297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_se</th>\n",
       "      <td>0.679090</td>\n",
       "      <td>0.275869</td>\n",
       "      <td>0.691765</td>\n",
       "      <td>0.732562</td>\n",
       "      <td>0.301467</td>\n",
       "      <td>0.497473</td>\n",
       "      <td>0.631925</td>\n",
       "      <td>0.698050</td>\n",
       "      <td>0.303379</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.715065</td>\n",
       "      <td>0.194799</td>\n",
       "      <td>0.719684</td>\n",
       "      <td>0.751548</td>\n",
       "      <td>0.141919</td>\n",
       "      <td>0.287103</td>\n",
       "      <td>0.380585</td>\n",
       "      <td>0.531062</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.049559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_se</th>\n",
       "      <td>-0.097317</td>\n",
       "      <td>0.386358</td>\n",
       "      <td>-0.086761</td>\n",
       "      <td>-0.066280</td>\n",
       "      <td>0.068406</td>\n",
       "      <td>0.046205</td>\n",
       "      <td>0.076218</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.128053</td>\n",
       "      <td>0.164174</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.111690</td>\n",
       "      <td>0.409003</td>\n",
       "      <td>-0.102242</td>\n",
       "      <td>-0.083195</td>\n",
       "      <td>-0.073658</td>\n",
       "      <td>-0.092439</td>\n",
       "      <td>-0.068956</td>\n",
       "      <td>-0.119638</td>\n",
       "      <td>-0.128215</td>\n",
       "      <td>-0.045655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_se</th>\n",
       "      <td>0.674172</td>\n",
       "      <td>0.281673</td>\n",
       "      <td>0.693135</td>\n",
       "      <td>0.726628</td>\n",
       "      <td>0.296092</td>\n",
       "      <td>0.548905</td>\n",
       "      <td>0.660391</td>\n",
       "      <td>0.710650</td>\n",
       "      <td>0.313893</td>\n",
       "      <td>0.039830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.697201</td>\n",
       "      <td>0.200371</td>\n",
       "      <td>0.721031</td>\n",
       "      <td>0.730713</td>\n",
       "      <td>0.130054</td>\n",
       "      <td>0.341919</td>\n",
       "      <td>0.418899</td>\n",
       "      <td>0.554897</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.085433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_se</th>\n",
       "      <td>0.735864</td>\n",
       "      <td>0.259845</td>\n",
       "      <td>0.744983</td>\n",
       "      <td>0.800086</td>\n",
       "      <td>0.246552</td>\n",
       "      <td>0.455653</td>\n",
       "      <td>0.617427</td>\n",
       "      <td>0.690299</td>\n",
       "      <td>0.223970</td>\n",
       "      <td>-0.090170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.757373</td>\n",
       "      <td>0.196497</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>0.125389</td>\n",
       "      <td>0.283257</td>\n",
       "      <td>0.385100</td>\n",
       "      <td>0.538166</td>\n",
       "      <td>0.074126</td>\n",
       "      <td>0.017539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_se</th>\n",
       "      <td>-0.222600</td>\n",
       "      <td>0.006614</td>\n",
       "      <td>-0.202694</td>\n",
       "      <td>-0.166777</td>\n",
       "      <td>0.332375</td>\n",
       "      <td>0.135299</td>\n",
       "      <td>0.098564</td>\n",
       "      <td>0.027653</td>\n",
       "      <td>0.187321</td>\n",
       "      <td>0.401964</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.230691</td>\n",
       "      <td>-0.074743</td>\n",
       "      <td>-0.217304</td>\n",
       "      <td>-0.182195</td>\n",
       "      <td>0.314457</td>\n",
       "      <td>-0.055558</td>\n",
       "      <td>-0.058298</td>\n",
       "      <td>-0.102007</td>\n",
       "      <td>-0.107342</td>\n",
       "      <td>0.101480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_se</th>\n",
       "      <td>0.206000</td>\n",
       "      <td>0.191975</td>\n",
       "      <td>0.250744</td>\n",
       "      <td>0.212583</td>\n",
       "      <td>0.318943</td>\n",
       "      <td>0.738722</td>\n",
       "      <td>0.670279</td>\n",
       "      <td>0.490424</td>\n",
       "      <td>0.421659</td>\n",
       "      <td>0.559837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204607</td>\n",
       "      <td>0.143003</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>0.199371</td>\n",
       "      <td>0.227394</td>\n",
       "      <td>0.678780</td>\n",
       "      <td>0.639147</td>\n",
       "      <td>0.483208</td>\n",
       "      <td>0.277878</td>\n",
       "      <td>0.590973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_se</th>\n",
       "      <td>0.194204</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.228082</td>\n",
       "      <td>0.207660</td>\n",
       "      <td>0.248396</td>\n",
       "      <td>0.570517</td>\n",
       "      <td>0.691270</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.342627</td>\n",
       "      <td>0.446630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186904</td>\n",
       "      <td>0.100241</td>\n",
       "      <td>0.226680</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.168481</td>\n",
       "      <td>0.484858</td>\n",
       "      <td>0.662564</td>\n",
       "      <td>0.440472</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>0.439329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_se</th>\n",
       "      <td>0.376169</td>\n",
       "      <td>0.163851</td>\n",
       "      <td>0.407217</td>\n",
       "      <td>0.372320</td>\n",
       "      <td>0.380676</td>\n",
       "      <td>0.642262</td>\n",
       "      <td>0.683260</td>\n",
       "      <td>0.615634</td>\n",
       "      <td>0.393298</td>\n",
       "      <td>0.341198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.358127</td>\n",
       "      <td>0.086741</td>\n",
       "      <td>0.394999</td>\n",
       "      <td>0.342271</td>\n",
       "      <td>0.215351</td>\n",
       "      <td>0.452888</td>\n",
       "      <td>0.549592</td>\n",
       "      <td>0.602450</td>\n",
       "      <td>0.143116</td>\n",
       "      <td>0.310655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_se</th>\n",
       "      <td>-0.104321</td>\n",
       "      <td>0.009127</td>\n",
       "      <td>-0.081629</td>\n",
       "      <td>-0.072497</td>\n",
       "      <td>0.200774</td>\n",
       "      <td>0.229977</td>\n",
       "      <td>0.178009</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.449137</td>\n",
       "      <td>0.345007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.128121</td>\n",
       "      <td>-0.077473</td>\n",
       "      <td>-0.103753</td>\n",
       "      <td>-0.110343</td>\n",
       "      <td>-0.012662</td>\n",
       "      <td>0.060255</td>\n",
       "      <td>0.037119</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>0.389402</td>\n",
       "      <td>0.078079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <td>-0.042641</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.005523</td>\n",
       "      <td>-0.019887</td>\n",
       "      <td>0.283607</td>\n",
       "      <td>0.507318</td>\n",
       "      <td>0.449301</td>\n",
       "      <td>0.257584</td>\n",
       "      <td>0.331786</td>\n",
       "      <td>0.688132</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037488</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>-0.022736</td>\n",
       "      <td>0.170568</td>\n",
       "      <td>0.390159</td>\n",
       "      <td>0.379975</td>\n",
       "      <td>0.215204</td>\n",
       "      <td>0.111094</td>\n",
       "      <td>0.591328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_worst</th>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.969476</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.185728</td>\n",
       "      <td>-0.253691</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.093492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_worst</th>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.219122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_worst</th>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.138957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_worst</th>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.079647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_worst</th>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.617624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_worst</th>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.810455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_worst</th>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.686511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_worst</th>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>0.511114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_worst</th>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <td>0.007066</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>0.767297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>1.000000</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                 1.000000      0.323782        0.997855   0.987357   \n",
       "texture_mean                0.323782      1.000000        0.329533   0.321086   \n",
       "perimeter_mean              0.997855      0.329533        1.000000   0.986507   \n",
       "area_mean                   0.987357      0.321086        0.986507   1.000000   \n",
       "smoothness_mean             0.170581     -0.023389        0.207278   0.177028   \n",
       "compactness_mean            0.506124      0.236702        0.556936   0.498502   \n",
       "concavity_mean              0.676764      0.302418        0.716136   0.685983   \n",
       "concave points_mean         0.822529      0.293464        0.850977   0.823269   \n",
       "symmetry_mean               0.147741      0.071401        0.183027   0.151293   \n",
       "fractal_dimension_mean     -0.311631     -0.076437       -0.261477  -0.283110   \n",
       "radius_se                   0.679090      0.275869        0.691765   0.732562   \n",
       "texture_se                 -0.097317      0.386358       -0.086761  -0.066280   \n",
       "perimeter_se                0.674172      0.281673        0.693135   0.726628   \n",
       "area_se                     0.735864      0.259845        0.744983   0.800086   \n",
       "smoothness_se              -0.222600      0.006614       -0.202694  -0.166777   \n",
       "compactness_se              0.206000      0.191975        0.250744   0.212583   \n",
       "concavity_se                0.194204      0.143293        0.228082   0.207660   \n",
       "concave points_se           0.376169      0.163851        0.407217   0.372320   \n",
       "symmetry_se                -0.104321      0.009127       -0.081629  -0.072497   \n",
       "fractal_dimension_se       -0.042641      0.054458       -0.005523  -0.019887   \n",
       "radius_worst                0.969539      0.352573        0.969476   0.962746   \n",
       "texture_worst               0.297008      0.912045        0.303038   0.287489   \n",
       "perimeter_worst             0.965137      0.358040        0.970387   0.959120   \n",
       "area_worst                  0.941082      0.343546        0.941550   0.959213   \n",
       "smoothness_worst            0.119616      0.077503        0.150549   0.123523   \n",
       "compactness_worst           0.413463      0.277830        0.455774   0.390410   \n",
       "concavity_worst             0.526911      0.301025        0.563879   0.512606   \n",
       "concave points_worst        0.744214      0.295316        0.771241   0.722017   \n",
       "symmetry_worst              0.163953      0.105008        0.189115   0.143570   \n",
       "fractal_dimension_worst     0.007066      0.119205        0.051019   0.003738   \n",
       "\n",
       "                         smoothness_mean  compactness_mean  concavity_mean  \\\n",
       "radius_mean                     0.170581          0.506124        0.676764   \n",
       "texture_mean                   -0.023389          0.236702        0.302418   \n",
       "perimeter_mean                  0.207278          0.556936        0.716136   \n",
       "area_mean                       0.177028          0.498502        0.685983   \n",
       "smoothness_mean                 1.000000          0.659123        0.521984   \n",
       "compactness_mean                0.659123          1.000000        0.883121   \n",
       "concavity_mean                  0.521984          0.883121        1.000000   \n",
       "concave points_mean             0.553695          0.831135        0.921391   \n",
       "symmetry_mean                   0.557775          0.602641        0.500667   \n",
       "fractal_dimension_mean          0.584792          0.565369        0.336783   \n",
       "radius_se                       0.301467          0.497473        0.631925   \n",
       "texture_se                      0.068406          0.046205        0.076218   \n",
       "perimeter_se                    0.296092          0.548905        0.660391   \n",
       "area_se                         0.246552          0.455653        0.617427   \n",
       "smoothness_se                   0.332375          0.135299        0.098564   \n",
       "compactness_se                  0.318943          0.738722        0.670279   \n",
       "concavity_se                    0.248396          0.570517        0.691270   \n",
       "concave points_se               0.380676          0.642262        0.683260   \n",
       "symmetry_se                     0.200774          0.229977        0.178009   \n",
       "fractal_dimension_se            0.283607          0.507318        0.449301   \n",
       "radius_worst                    0.213120          0.535315        0.688236   \n",
       "texture_worst                   0.036072          0.248133        0.299879   \n",
       "perimeter_worst                 0.238853          0.590210        0.729565   \n",
       "area_worst                      0.206718          0.509604        0.675987   \n",
       "smoothness_worst                0.805324          0.565541        0.448822   \n",
       "compactness_worst               0.472468          0.865809        0.754968   \n",
       "concavity_worst                 0.434926          0.816275        0.884103   \n",
       "concave points_worst            0.503053          0.815573        0.861323   \n",
       "symmetry_worst                  0.394309          0.510223        0.409464   \n",
       "fractal_dimension_worst         0.499316          0.687382        0.514930   \n",
       "\n",
       "                         concave points_mean  symmetry_mean  \\\n",
       "radius_mean                         0.822529       0.147741   \n",
       "texture_mean                        0.293464       0.071401   \n",
       "perimeter_mean                      0.850977       0.183027   \n",
       "area_mean                           0.823269       0.151293   \n",
       "smoothness_mean                     0.553695       0.557775   \n",
       "compactness_mean                    0.831135       0.602641   \n",
       "concavity_mean                      0.921391       0.500667   \n",
       "concave points_mean                 1.000000       0.462497   \n",
       "symmetry_mean                       0.462497       1.000000   \n",
       "fractal_dimension_mean              0.166917       0.479921   \n",
       "radius_se                           0.698050       0.303379   \n",
       "texture_se                          0.021480       0.128053   \n",
       "perimeter_se                        0.710650       0.313893   \n",
       "area_se                             0.690299       0.223970   \n",
       "smoothness_se                       0.027653       0.187321   \n",
       "compactness_se                      0.490424       0.421659   \n",
       "concavity_se                        0.439167       0.342627   \n",
       "concave points_se                   0.615634       0.393298   \n",
       "symmetry_se                         0.095351       0.449137   \n",
       "fractal_dimension_se                0.257584       0.331786   \n",
       "radius_worst                        0.830318       0.185728   \n",
       "texture_worst                       0.292752       0.090651   \n",
       "perimeter_worst                     0.855923       0.219169   \n",
       "area_worst                          0.809630       0.177193   \n",
       "smoothness_worst                    0.452753       0.426675   \n",
       "compactness_worst                   0.667454       0.473200   \n",
       "concavity_worst                     0.752399       0.433721   \n",
       "concave points_worst                0.910155       0.430297   \n",
       "symmetry_worst                      0.375744       0.699826   \n",
       "fractal_dimension_worst             0.368661       0.438413   \n",
       "\n",
       "                         fractal_dimension_mean  ...  radius_worst  \\\n",
       "radius_mean                           -0.311631  ...      0.969539   \n",
       "texture_mean                          -0.076437  ...      0.352573   \n",
       "perimeter_mean                        -0.261477  ...      0.969476   \n",
       "area_mean                             -0.283110  ...      0.962746   \n",
       "smoothness_mean                        0.584792  ...      0.213120   \n",
       "compactness_mean                       0.565369  ...      0.535315   \n",
       "concavity_mean                         0.336783  ...      0.688236   \n",
       "concave points_mean                    0.166917  ...      0.830318   \n",
       "symmetry_mean                          0.479921  ...      0.185728   \n",
       "fractal_dimension_mean                 1.000000  ...     -0.253691   \n",
       "radius_se                              0.000111  ...      0.715065   \n",
       "texture_se                             0.164174  ...     -0.111690   \n",
       "perimeter_se                           0.039830  ...      0.697201   \n",
       "area_se                               -0.090170  ...      0.757373   \n",
       "smoothness_se                          0.401964  ...     -0.230691   \n",
       "compactness_se                         0.559837  ...      0.204607   \n",
       "concavity_se                           0.446630  ...      0.186904   \n",
       "concave points_se                      0.341198  ...      0.358127   \n",
       "symmetry_se                            0.345007  ...     -0.128121   \n",
       "fractal_dimension_se                   0.688132  ...     -0.037488   \n",
       "radius_worst                          -0.253691  ...      1.000000   \n",
       "texture_worst                         -0.051269  ...      0.359921   \n",
       "perimeter_worst                       -0.205151  ...      0.993708   \n",
       "area_worst                            -0.231854  ...      0.984015   \n",
       "smoothness_worst                       0.504942  ...      0.216574   \n",
       "compactness_worst                      0.458798  ...      0.475820   \n",
       "concavity_worst                        0.346234  ...      0.573975   \n",
       "concave points_worst                   0.175325  ...      0.787424   \n",
       "symmetry_worst                         0.334019  ...      0.243529   \n",
       "fractal_dimension_worst                0.767297  ...      0.093492   \n",
       "\n",
       "                         texture_worst  perimeter_worst  area_worst  \\\n",
       "radius_mean                   0.297008         0.965137    0.941082   \n",
       "texture_mean                  0.912045         0.358040    0.343546   \n",
       "perimeter_mean                0.303038         0.970387    0.941550   \n",
       "area_mean                     0.287489         0.959120    0.959213   \n",
       "smoothness_mean               0.036072         0.238853    0.206718   \n",
       "compactness_mean              0.248133         0.590210    0.509604   \n",
       "concavity_mean                0.299879         0.729565    0.675987   \n",
       "concave points_mean           0.292752         0.855923    0.809630   \n",
       "symmetry_mean                 0.090651         0.219169    0.177193   \n",
       "fractal_dimension_mean       -0.051269        -0.205151   -0.231854   \n",
       "radius_se                     0.194799         0.719684    0.751548   \n",
       "texture_se                    0.409003        -0.102242   -0.083195   \n",
       "perimeter_se                  0.200371         0.721031    0.730713   \n",
       "area_se                       0.196497         0.761213    0.811408   \n",
       "smoothness_se                -0.074743        -0.217304   -0.182195   \n",
       "compactness_se                0.143003         0.260516    0.199371   \n",
       "concavity_se                  0.100241         0.226680    0.188353   \n",
       "concave points_se             0.086741         0.394999    0.342271   \n",
       "symmetry_se                  -0.077473        -0.103753   -0.110343   \n",
       "fractal_dimension_se         -0.003195        -0.001000   -0.022736   \n",
       "radius_worst                  0.359921         0.993708    0.984015   \n",
       "texture_worst                 1.000000         0.365098    0.345842   \n",
       "perimeter_worst               0.365098         1.000000    0.977578   \n",
       "area_worst                    0.345842         0.977578    1.000000   \n",
       "smoothness_worst              0.225429         0.236775    0.209145   \n",
       "compactness_worst             0.360832         0.529408    0.438296   \n",
       "concavity_worst               0.368366         0.618344    0.543331   \n",
       "concave points_worst          0.359755         0.816322    0.747419   \n",
       "symmetry_worst                0.233027         0.269493    0.209146   \n",
       "fractal_dimension_worst       0.219122         0.138957    0.079647   \n",
       "\n",
       "                         smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "radius_mean                      0.119616           0.413463         0.526911   \n",
       "texture_mean                     0.077503           0.277830         0.301025   \n",
       "perimeter_mean                   0.150549           0.455774         0.563879   \n",
       "area_mean                        0.123523           0.390410         0.512606   \n",
       "smoothness_mean                  0.805324           0.472468         0.434926   \n",
       "compactness_mean                 0.565541           0.865809         0.816275   \n",
       "concavity_mean                   0.448822           0.754968         0.884103   \n",
       "concave points_mean              0.452753           0.667454         0.752399   \n",
       "symmetry_mean                    0.426675           0.473200         0.433721   \n",
       "fractal_dimension_mean           0.504942           0.458798         0.346234   \n",
       "radius_se                        0.141919           0.287103         0.380585   \n",
       "texture_se                      -0.073658          -0.092439        -0.068956   \n",
       "perimeter_se                     0.130054           0.341919         0.418899   \n",
       "area_se                          0.125389           0.283257         0.385100   \n",
       "smoothness_se                    0.314457          -0.055558        -0.058298   \n",
       "compactness_se                   0.227394           0.678780         0.639147   \n",
       "concavity_se                     0.168481           0.484858         0.662564   \n",
       "concave points_se                0.215351           0.452888         0.549592   \n",
       "symmetry_se                     -0.012662           0.060255         0.037119   \n",
       "fractal_dimension_se             0.170568           0.390159         0.379975   \n",
       "radius_worst                     0.216574           0.475820         0.573975   \n",
       "texture_worst                    0.225429           0.360832         0.368366   \n",
       "perimeter_worst                  0.236775           0.529408         0.618344   \n",
       "area_worst                       0.209145           0.438296         0.543331   \n",
       "smoothness_worst                 1.000000           0.568187         0.518523   \n",
       "compactness_worst                0.568187           1.000000         0.892261   \n",
       "concavity_worst                  0.518523           0.892261         1.000000   \n",
       "concave points_worst             0.547691           0.801080         0.855434   \n",
       "symmetry_worst                   0.493838           0.614441         0.532520   \n",
       "fractal_dimension_worst          0.617624           0.810455         0.686511   \n",
       "\n",
       "                         concave points_worst  symmetry_worst  \\\n",
       "radius_mean                          0.744214        0.163953   \n",
       "texture_mean                         0.295316        0.105008   \n",
       "perimeter_mean                       0.771241        0.189115   \n",
       "area_mean                            0.722017        0.143570   \n",
       "smoothness_mean                      0.503053        0.394309   \n",
       "compactness_mean                     0.815573        0.510223   \n",
       "concavity_mean                       0.861323        0.409464   \n",
       "concave points_mean                  0.910155        0.375744   \n",
       "symmetry_mean                        0.430297        0.699826   \n",
       "fractal_dimension_mean               0.175325        0.334019   \n",
       "radius_se                            0.531062        0.094543   \n",
       "texture_se                          -0.119638       -0.128215   \n",
       "perimeter_se                         0.554897        0.109930   \n",
       "area_se                              0.538166        0.074126   \n",
       "smoothness_se                       -0.102007       -0.107342   \n",
       "compactness_se                       0.483208        0.277878   \n",
       "concavity_se                         0.440472        0.197788   \n",
       "concave points_se                    0.602450        0.143116   \n",
       "symmetry_se                         -0.030413        0.389402   \n",
       "fractal_dimension_se                 0.215204        0.111094   \n",
       "radius_worst                         0.787424        0.243529   \n",
       "texture_worst                        0.359755        0.233027   \n",
       "perimeter_worst                      0.816322        0.269493   \n",
       "area_worst                           0.747419        0.209146   \n",
       "smoothness_worst                     0.547691        0.493838   \n",
       "compactness_worst                    0.801080        0.614441   \n",
       "concavity_worst                      0.855434        0.532520   \n",
       "concave points_worst                 1.000000        0.502528   \n",
       "symmetry_worst                       0.502528        1.000000   \n",
       "fractal_dimension_worst              0.511114        0.537848   \n",
       "\n",
       "                         fractal_dimension_worst  \n",
       "radius_mean                             0.007066  \n",
       "texture_mean                            0.119205  \n",
       "perimeter_mean                          0.051019  \n",
       "area_mean                               0.003738  \n",
       "smoothness_mean                         0.499316  \n",
       "compactness_mean                        0.687382  \n",
       "concavity_mean                          0.514930  \n",
       "concave points_mean                     0.368661  \n",
       "symmetry_mean                           0.438413  \n",
       "fractal_dimension_mean                  0.767297  \n",
       "radius_se                               0.049559  \n",
       "texture_se                             -0.045655  \n",
       "perimeter_se                            0.085433  \n",
       "area_se                                 0.017539  \n",
       "smoothness_se                           0.101480  \n",
       "compactness_se                          0.590973  \n",
       "concavity_se                            0.439329  \n",
       "concave points_se                       0.310655  \n",
       "symmetry_se                             0.078079  \n",
       "fractal_dimension_se                    0.591328  \n",
       "radius_worst                            0.093492  \n",
       "texture_worst                           0.219122  \n",
       "perimeter_worst                         0.138957  \n",
       "area_worst                              0.079647  \n",
       "smoothness_worst                        0.617624  \n",
       "compactness_worst                       0.810455  \n",
       "concavity_worst                         0.686511  \n",
       "concave points_worst                    0.511114  \n",
       "symmetry_worst                          0.537848  \n",
       "fractal_dimension_worst                 1.000000  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr =df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 30)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(corr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>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>0</th>\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>0.2419</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>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>0.1812</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>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>0.2069</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>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>0.2597</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>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>0.1809</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 × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         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",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diagnosis']=df['diagnosis'].map({'M':1, 'B':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></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",
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       "      <th>concavity_mean</th>\n",
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       "      <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",
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       "      <th>concavity_worst</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17.99</td>\n",
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       "      <td>0.2419</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
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       "      <td>1956.0</td>\n",
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       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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>0.2597</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>1</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>0.1809</td>\n",
       "      <td>...</td>\n",
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       "      <td>152.20</td>\n",
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      "text/plain": [
       "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0          1        17.99         10.38          122.80     1001.0   \n",
       "1          1        20.57         17.77          132.90     1326.0   \n",
       "2          1        19.69         21.25          130.00     1203.0   \n",
       "3          1        11.42         20.38           77.58      386.1   \n",
       "4          1        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",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['diagnosis'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
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       "      <td>0.2069</td>\n",
       "      <td>0.05999</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
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       "      <td>0.1374</td>\n",
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       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   fractal_dimension_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('diagnosis',axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['diagnosis']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 30)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>32</th>\n",
       "      <td>17.02</td>\n",
       "      <td>23.98</td>\n",
       "      <td>112.8</td>\n",
       "      <td>899.3</td>\n",
       "      <td>0.1197</td>\n",
       "      <td>0.1496</td>\n",
       "      <td>0.2417</td>\n",
       "      <td>0.1203</td>\n",
       "      <td>0.2248</td>\n",
       "      <td>0.06382</td>\n",
       "      <td>...</td>\n",
       "      <td>20.88</td>\n",
       "      <td>32.09</td>\n",
       "      <td>136.1</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>0.1634</td>\n",
       "      <td>0.3559</td>\n",
       "      <td>0.5588</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.08482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "32        17.02         23.98           112.8      899.3           0.1197   \n",
       "\n",
       "    compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "32            0.1496          0.2417               0.1203         0.2248   \n",
       "\n",
       "    fractal_dimension_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "32                 0.06382  ...         20.88          32.09            136.1   \n",
       "\n",
       "    area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "32      1344.0            0.1634             0.3559           0.5588   \n",
       "\n",
       "    concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "32                0.1847           0.353                  0.08482  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.83311284,  1.13883428,  0.86877301, ...,  1.05029244,\n",
       "         0.97337694,  0.03664092],\n",
       "       [-0.48252749, -0.51787097, -0.55239084, ..., -1.30240538,\n",
       "        -1.57020768, -1.42671788],\n",
       "       [-0.47392853, -1.50762182, -0.5428333 , ..., -1.3252734 ,\n",
       "        -0.97842219, -0.79776053],\n",
       "       ...,\n",
       "       [-0.72043195, -1.50524832, -0.729413  , ..., -0.45734184,\n",
       "        -0.21019405,  0.19388026],\n",
       "       [ 1.34905023,  0.66176013,  1.32171704, ...,  0.84117043,\n",
       "         0.15674418, -0.0597686 ],\n",
       "       [-0.03824807,  0.10398687, -0.03212852, ...,  1.02020294,\n",
       "         0.42531174,  1.22339622]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137    0\n",
       "178    0\n",
       "370    1\n",
       "515    0\n",
       "505    0\n",
       "      ..\n",
       "500    0\n",
       "110    0\n",
       "364    0\n",
       "128    0\n",
       "230    1\n",
       "Name: diagnosis, Length: 171, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9707602339181286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9707602339181286\n"
     ]
    }
   ],
   "source": [
    "lr_acc = accuracy_score(y_test, y_pred)\n",
    "print(lr_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>logistic Regression Method</td>\n",
       "      <td>0.97076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy\n",
       "0  logistic Regression Method   0.97076"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempresults = pd.DataFrame({'Algorithm': ['logistic Regression Method'], 'Accuracy':[lr_acc]})\n",
    "results = pd.concat( [results,tempresults])\n",
    "results = results[['Algorithm', 'Accuracy']]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dtc.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9473684210526315\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9473684210526315\n"
     ]
    }
   ],
   "source": [
    "dtc_acc = accuracy_score(y_test, y_pred)\n",
    "print(dtc_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>logistic Regression Method</td>\n",
       "      <td>0.970760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree Classifier Method</td>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Algorithm  Accuracy\n",
       "0       logistic Regression Method  0.970760\n",
       "0  Decision Tree Classifier Method  0.947368"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempresults = pd.DataFrame({'Algorithm': ['Decision Tree Classifier Method'], 'Accuracy':[dtc_acc]})\n",
    "results = pd.concat( [results,tempresults])\n",
    "results = results[['Algorithm', 'Accuracy']]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rfc.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9590643274853801\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9590643274853801\n"
     ]
    }
   ],
   "source": [
    "rfc_acc = accuracy_score(y_test, y_pred)\n",
    "print(rfc_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>logistic Regression Method</td>\n",
       "      <td>0.970760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree Classifier Method</td>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random forest Classifier Method</td>\n",
       "      <td>0.959064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Algorithm  Accuracy\n",
       "0       logistic Regression Method  0.970760\n",
       "0  Decision Tree Classifier Method  0.947368\n",
       "0  Random forest Classifier Method  0.959064"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempresults = pd.DataFrame({'Algorithm': ['Random forest Classifier Method'], 'Accuracy':[rfc_acc]})\n",
    "results = pd.concat( [results,tempresults])\n",
    "results = results[['Algorithm', 'Accuracy']]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vector classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "svc = svm.SVC()\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svc.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9766081871345029\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9766081871345029\n"
     ]
    }
   ],
   "source": [
    "svc_acc = accuracy_score(y_test, y_pred)\n",
    "print(svc_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>logistic Regression Method</td>\n",
       "      <td>0.970760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree Classifier Method</td>\n",
       "      <td>0.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random forest Classifier Method</td>\n",
       "      <td>0.959064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Classifier Method</td>\n",
       "      <td>0.976608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Algorithm  Accuracy\n",
       "0        logistic Regression Method  0.970760\n",
       "0   Decision Tree Classifier Method  0.947368\n",
       "0   Random forest Classifier Method  0.959064\n",
       "0  Support Vector Classifier Method  0.976608"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempresults = pd.DataFrame({'Algorithm': ['Support Vector Classifier Method'], 'Accuracy':[svc_acc]})\n",
    "results = pd.concat( [results,tempresults])\n",
    "results = results[['Algorithm', 'Accuracy']]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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