Breast Cancer Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "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": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/ingledarshan/AIML-B2/main/data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Unnamed: 32']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Unnamed: 32\", axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0  ...         25.38          17.33           184.60      2019.0   \n",
       "1  ...         24.99          23.41           158.80      1956.0   \n",
       "2  ...         23.57          25.53           152.50      1709.0   \n",
       "3  ...         14.91          26.50            98.87       567.7   \n",
       "4  ...         22.54          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  \n",
       "0          0.4601                  0.11890  \n",
       "1          0.2750                  0.08902  \n",
       "2          0.3613                  0.08758  \n",
       "3          0.6638                  0.17300  \n",
       "4          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.drop('id', axis=1, inplace=True)\n",
    "#df = df.drop('id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>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": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "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": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_mean = l[1:11]\n",
    "\n",
    "features_se = l[11:20]\n",
    "\n",
    "features_worst = l[21:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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": 234,
   "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']\n"
     ]
    }
   ],
   "source": [
    "print(features_se)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "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": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we seprated features here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].unique()\n",
    "#M = Malignant , B=Benign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "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": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].value_counts()\n",
    "#here it find how many B and M is there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "scrolled": true
   },
   "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": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "#here we get summary of all the numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation plot\n",
    "corr =df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 30)"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.shape\n",
    "#only numerical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corr);\n",
    "#using heatmap easily spot the corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "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": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diagnosis'] = df['diagnosis'].map({'M':1, 'B':0})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
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       "      <th>perimeter_mean</th>\n",
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       "      <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",
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       "      <td>0.1812</td>\n",
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       "      <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",
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       "      <td>11.42</td>\n",
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       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
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       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
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       "   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": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['diagnosis'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>132.90</td>\n",
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       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
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       "      <td>386.1</td>\n",
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       "      <td>0.09744</td>\n",
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       "      <td>0.05883</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "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": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('diagnosis', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['diagnosis']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 30)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 30)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398,)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171,)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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>258</th>\n",
       "      <td>15.66</td>\n",
       "      <td>23.2</td>\n",
       "      <td>110.2</td>\n",
       "      <td>773.5</td>\n",
       "      <td>0.1109</td>\n",
       "      <td>0.3114</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.1377</td>\n",
       "      <td>0.2495</td>\n",
       "      <td>0.08104</td>\n",
       "      <td>...</td>\n",
       "      <td>19.85</td>\n",
       "      <td>31.64</td>\n",
       "      <td>143.7</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>0.1504</td>\n",
       "      <td>0.5172</td>\n",
       "      <td>0.6181</td>\n",
       "      <td>0.2462</td>\n",
       "      <td>0.3277</td>\n",
       "      <td>0.1019</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",
       "258        15.66          23.2           110.2      773.5           0.1109   \n",
       "\n",
       "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "258            0.3114          0.3176               0.1377         0.2495   \n",
       "\n",
       "     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n",
       "258                 0.08104  ...         19.85          31.64   \n",
       "\n",
       "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "258            143.7      1226.0            0.1504             0.5172   \n",
       "\n",
       "     concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "258           0.6181                0.2462          0.3277   \n",
       "\n",
       "     fractal_dimension_worst  \n",
       "258                   0.1019  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.40244821,  1.03913845,  0.70421518, ...,  1.92337429,\n",
       "         0.50489338,  0.97925082],\n",
       "       [ 0.42172357, -1.94231849,  0.40921713, ...,  0.42289666,\n",
       "        -0.58280404, -0.14233008],\n",
       "       [ 0.3886801 ,  0.08367154,  0.29759625, ..., -0.46134521,\n",
       "        -0.39245699, -0.89712221],\n",
       "       ...,\n",
       "       [-0.09595753, -0.96684181, -0.141313  , ..., -0.24964416,\n",
       "         0.01845093, -0.85078511],\n",
       "       [ 0.62273804,  0.26626077,  0.66833704, ...,  1.28901395,\n",
       "         0.16800932,  0.60464615],\n",
       "       [-0.67697196, -1.25698359, -0.72931585, ..., -0.50264549,\n",
       "         0.23901179, -0.97583943]])"
      ]
     },
     "execution_count": 261,
     "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": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "230    1\n",
       "505    0\n",
       "353    1\n",
       "315    0\n",
       "194    1\n",
       "      ..\n",
       "132    1\n",
       "508    0\n",
       "246    0\n",
       "472    0\n",
       "348    0\n",
       "Name: diagnosis, Length: 171, dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9941520467836257\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9941520467836257\n"
     ]
    }
   ],
   "source": [
    "lr_acc = accuracy_score(y_test, y_pred)\n",
    "print(lr_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "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": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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>0.994152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy\n",
       "0  Logistic Regression Method  0.994152"
      ]
     },
     "execution_count": 269,
     "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 270,
     "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": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dtc.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.935672514619883\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.935672514619883\n"
     ]
    }
   ],
   "source": [
    "dtc_acc = accuracy_score(y_test, y_pred)\n",
    "print(dtc_acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.994152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision tree Classifier Method</td>\n",
       "      <td>0.935673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Algorithm  Accuracy\n",
       "0       Logistic Regression Method  0.994152\n",
       "0  Decision tree Classifier Method  0.935673"
      ]
     },
     "execution_count": 274,
     "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": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 275,
     "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": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rfc.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "rfc_acc = accuracy_score(y_test, y_pred)\n",
    "print(rfc_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>0.994152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision tree Classifier Method</td>\n",
       "      <td>0.935673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Classifier Method</td>\n",
       "      <td>0.964912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Algorithm  Accuracy\n",
       "0       Logistic Regression Method  0.994152\n",
       "0  Decision tree Classifier Method  0.935673\n",
       "0  Random Forest Classifier Method  0.964912"
      ]
     },
     "execution_count": 279,
     "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": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 280,
     "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": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svc.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "svc_acc = accuracy_score(y_test, y_pred)\n",
    "print(svc_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>0.994152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision tree Classifier Method</td>\n",
       "      <td>0.935673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Classifier Method</td>\n",
       "      <td>0.964912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Classifier Method</td>\n",
       "      <td>0.982456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Algorithm  Accuracy\n",
       "0        Logistic Regression Method  0.994152\n",
       "0   Decision tree Classifier Method  0.935673\n",
       "0   Random Forest Classifier Method  0.964912\n",
       "0  Support Vector Classifier Method  0.982456"
      ]
     },
     "execution_count": 284,
     "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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