Cancer-prediction-using-statistical-models-by-Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cancer prediction using statistical models by Python\n",
    "### Md Mehadi Hasan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "\n",
    "import numpy as np                        # python library for arrays and matrices\n",
    "import pandas as pd                     # Big data analysis and manipulation tool\n",
    "import matplotlib.pyplot as plt   # interactive visualization library in Python.\n",
    "import seaborn as sns             # data visualization library based on matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the data\n",
    "\n",
    "df = pd.read_csv('cancer_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimension of the dataset\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the head of the data\n",
    "\n",
    "df.head()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 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": [
    "# to see the row informations\n",
    "\n",
    "df.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           0\n",
       "diagnosis                    0\n",
       "radius_mean                  0\n",
       "texture_mean                 0\n",
       "perimeter_mean               0\n",
       "area_mean                    0\n",
       "smoothness_mean              0\n",
       "compactness_mean             0\n",
       "concavity_mean               0\n",
       "concave points_mean          0\n",
       "symmetry_mean                0\n",
       "fractal_dimension_mean       0\n",
       "radius_se                    0\n",
       "texture_se                   0\n",
       "perimeter_se                 0\n",
       "area_se                      0\n",
       "smoothness_se                0\n",
       "compactness_se               0\n",
       "concavity_se                 0\n",
       "concave points_se            0\n",
       "symmetry_se                  0\n",
       "fractal_dimension_se         0\n",
       "radius_worst                 0\n",
       "texture_worst                0\n",
       "perimeter_worst              0\n",
       "area_worst                   0\n",
       "smoothness_worst             0\n",
       "compactness_worst            0\n",
       "concavity_worst              0\n",
       "concave points_worst         0\n",
       "symmetry_worst               0\n",
       "fractal_dimension_worst      0\n",
       "Unnamed: 32                569\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count the number of empty/null  (NAN,NaN,NA) values in each column \n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number and rows and columns in the datasets\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the column with missing values (33th column)\n",
    "\n",
    "df=df.dropna(axis=1)\n",
    "\n",
    "# axis=1 : Drop columns which contain missing value.\n",
    "# axix=0 : Drop rows which contain missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After droping 1 column see the the counts of the rows and columns in the datasets\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 32 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      "dtypes: float64(30), int64(1), object(1)\n",
      "memory usage: 142.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# To see which column removed again check the df.info and found 33th column removed\n",
    "\n",
    "df.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.690000e+02</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
       "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
       "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
       "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
       "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
       "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
       "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
       "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
       "\n",
       "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description/statistical summary of the refined datasets\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get a count of the number of Malignant(M) and Benign (B) cells\n",
    "\n",
    "df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2077559df40>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization of the count of diagnosis (barplot)\n",
    "\n",
    "sns.countplot(df['diagnosis'],label='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           int64\n",
       "diagnosis                   object\n",
       "radius_mean                float64\n",
       "texture_mean               float64\n",
       "perimeter_mean             float64\n",
       "area_mean                  float64\n",
       "smoothness_mean            float64\n",
       "compactness_mean           float64\n",
       "concavity_mean             float64\n",
       "concave points_mean        float64\n",
       "symmetry_mean              float64\n",
       "fractal_dimension_mean     float64\n",
       "radius_se                  float64\n",
       "texture_se                 float64\n",
       "perimeter_se               float64\n",
       "area_se                    float64\n",
       "smoothness_se              float64\n",
       "compactness_se             float64\n",
       "concavity_se               float64\n",
       "concave points_se          float64\n",
       "symmetry_se                float64\n",
       "fractal_dimension_se       float64\n",
       "radius_worst               float64\n",
       "texture_worst              float64\n",
       "perimeter_worst            float64\n",
       "area_worst                 float64\n",
       "smoothness_worst           float64\n",
       "compactness_worst          float64\n",
       "concavity_worst            float64\n",
       "concave points_worst       float64\n",
       "symmetry_worst             float64\n",
       "fractal_dimension_worst    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at the data type to see which column need to be encoded\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label encoding (Convert the value of M and B into 1 and 0)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_Y = LabelEncoder()\n",
    "df.iloc[:,1] = labelencoder_Y.fit_transform(df.iloc[:,1].values)\n",
    "df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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>1</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>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</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>1</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>1</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</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          1        17.99         10.38          122.80     1001.0   \n",
       "1    842517          1        20.57         17.77          132.90     1326.0   \n",
       "2  84300903          1        19.69         21.25          130.00     1203.0   \n",
       "3  84348301          1        11.42         20.38           77.58      386.1   \n",
       "4  84358402          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",
       "   ...  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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the default head of the data\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x20775593d60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 595.5x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pair plot\n",
    "\n",
    "sns.pairplot(df.iloc[:,1:5], hue=\"diagnosis\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>diagnosis</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.730029</td>\n",
       "      <td>0.415185</td>\n",
       "      <td>0.742636</td>\n",
       "      <td>0.708984</td>\n",
       "      <td>0.358560</td>\n",
       "      <td>0.596534</td>\n",
       "      <td>0.696360</td>\n",
       "      <td>0.776614</td>\n",
       "      <td>0.330499</td>\n",
       "      <td>...</td>\n",
       "      <td>0.776454</td>\n",
       "      <td>0.456903</td>\n",
       "      <td>0.782914</td>\n",
       "      <td>0.733825</td>\n",
       "      <td>0.421465</td>\n",
       "      <td>0.590998</td>\n",
       "      <td>0.659610</td>\n",
       "      <td>0.793566</td>\n",
       "      <td>0.416294</td>\n",
       "      <td>0.323872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_mean</th>\n",
       "      <td>0.730029</td>\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>...</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>0.415185</td>\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>...</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>0.742636</td>\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>...</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>0.708984</td>\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>...</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>0.358560</td>\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>...</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>0.596534</td>\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>...</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>0.696360</td>\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>...</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>0.776614</td>\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>...</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>0.330499</td>\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>...</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>-0.012838</td>\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>...</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>0.567134</td>\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>...</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>-0.008303</td>\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>...</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>0.556141</td>\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>...</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>0.548236</td>\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>...</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>-0.067016</td>\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>...</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>0.292999</td>\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>...</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>0.253730</td>\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>...</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>0.408042</td>\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>...</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>-0.006522</td>\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>...</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>0.077972</td>\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>...</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>0.776454</td>\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>...</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>0.456903</td>\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>...</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>0.782914</td>\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>...</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>0.733825</td>\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>...</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>0.421465</td>\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>...</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>0.590998</td>\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>...</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>0.659610</td>\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>...</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>0.793566</td>\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>...</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>0.416294</td>\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>...</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>0.323872</td>\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>...</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>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
       "diagnosis                 1.000000     0.730029      0.415185        0.742636   \n",
       "radius_mean               0.730029     1.000000      0.323782        0.997855   \n",
       "texture_mean              0.415185     0.323782      1.000000        0.329533   \n",
       "perimeter_mean            0.742636     0.997855      0.329533        1.000000   \n",
       "area_mean                 0.708984     0.987357      0.321086        0.986507   \n",
       "smoothness_mean           0.358560     0.170581     -0.023389        0.207278   \n",
       "compactness_mean          0.596534     0.506124      0.236702        0.556936   \n",
       "concavity_mean            0.696360     0.676764      0.302418        0.716136   \n",
       "concave points_mean       0.776614     0.822529      0.293464        0.850977   \n",
       "symmetry_mean             0.330499     0.147741      0.071401        0.183027   \n",
       "fractal_dimension_mean   -0.012838    -0.311631     -0.076437       -0.261477   \n",
       "radius_se                 0.567134     0.679090      0.275869        0.691765   \n",
       "texture_se               -0.008303    -0.097317      0.386358       -0.086761   \n",
       "perimeter_se              0.556141     0.674172      0.281673        0.693135   \n",
       "area_se                   0.548236     0.735864      0.259845        0.744983   \n",
       "smoothness_se            -0.067016    -0.222600      0.006614       -0.202694   \n",
       "compactness_se            0.292999     0.206000      0.191975        0.250744   \n",
       "concavity_se              0.253730     0.194204      0.143293        0.228082   \n",
       "concave points_se         0.408042     0.376169      0.163851        0.407217   \n",
       "symmetry_se              -0.006522    -0.104321      0.009127       -0.081629   \n",
       "fractal_dimension_se      0.077972    -0.042641      0.054458       -0.005523   \n",
       "radius_worst              0.776454     0.969539      0.352573        0.969476   \n",
       "texture_worst             0.456903     0.297008      0.912045        0.303038   \n",
       "perimeter_worst           0.782914     0.965137      0.358040        0.970387   \n",
       "area_worst                0.733825     0.941082      0.343546        0.941550   \n",
       "smoothness_worst          0.421465     0.119616      0.077503        0.150549   \n",
       "compactness_worst         0.590998     0.413463      0.277830        0.455774   \n",
       "concavity_worst           0.659610     0.526911      0.301025        0.563879   \n",
       "concave points_worst      0.793566     0.744214      0.295316        0.771241   \n",
       "symmetry_worst            0.416294     0.163953      0.105008        0.189115   \n",
       "fractal_dimension_worst   0.323872     0.007066      0.119205        0.051019   \n",
       "\n",
       "                         area_mean  smoothness_mean  compactness_mean  \\\n",
       "diagnosis                 0.708984         0.358560          0.596534   \n",
       "radius_mean               0.987357         0.170581          0.506124   \n",
       "texture_mean              0.321086        -0.023389          0.236702   \n",
       "perimeter_mean            0.986507         0.207278          0.556936   \n",
       "area_mean                 1.000000         0.177028          0.498502   \n",
       "smoothness_mean           0.177028         1.000000          0.659123   \n",
       "compactness_mean          0.498502         0.659123          1.000000   \n",
       "concavity_mean            0.685983         0.521984          0.883121   \n",
       "concave points_mean       0.823269         0.553695          0.831135   \n",
       "symmetry_mean             0.151293         0.557775          0.602641   \n",
       "fractal_dimension_mean   -0.283110         0.584792          0.565369   \n",
       "radius_se                 0.732562         0.301467          0.497473   \n",
       "texture_se               -0.066280         0.068406          0.046205   \n",
       "perimeter_se              0.726628         0.296092          0.548905   \n",
       "area_se                   0.800086         0.246552          0.455653   \n",
       "smoothness_se            -0.166777         0.332375          0.135299   \n",
       "compactness_se            0.212583         0.318943          0.738722   \n",
       "concavity_se              0.207660         0.248396          0.570517   \n",
       "concave points_se         0.372320         0.380676          0.642262   \n",
       "symmetry_se              -0.072497         0.200774          0.229977   \n",
       "fractal_dimension_se     -0.019887         0.283607          0.507318   \n",
       "radius_worst              0.962746         0.213120          0.535315   \n",
       "texture_worst             0.287489         0.036072          0.248133   \n",
       "perimeter_worst           0.959120         0.238853          0.590210   \n",
       "area_worst                0.959213         0.206718          0.509604   \n",
       "smoothness_worst          0.123523         0.805324          0.565541   \n",
       "compactness_worst         0.390410         0.472468          0.865809   \n",
       "concavity_worst           0.512606         0.434926          0.816275   \n",
       "concave points_worst      0.722017         0.503053          0.815573   \n",
       "symmetry_worst            0.143570         0.394309          0.510223   \n",
       "fractal_dimension_worst   0.003738         0.499316          0.687382   \n",
       "\n",
       "                         concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "diagnosis                      0.696360             0.776614       0.330499   \n",
       "radius_mean                    0.676764             0.822529       0.147741   \n",
       "texture_mean                   0.302418             0.293464       0.071401   \n",
       "perimeter_mean                 0.716136             0.850977       0.183027   \n",
       "area_mean                      0.685983             0.823269       0.151293   \n",
       "smoothness_mean                0.521984             0.553695       0.557775   \n",
       "compactness_mean               0.883121             0.831135       0.602641   \n",
       "concavity_mean                 1.000000             0.921391       0.500667   \n",
       "concave points_mean            0.921391             1.000000       0.462497   \n",
       "symmetry_mean                  0.500667             0.462497       1.000000   \n",
       "fractal_dimension_mean         0.336783             0.166917       0.479921   \n",
       "radius_se                      0.631925             0.698050       0.303379   \n",
       "texture_se                     0.076218             0.021480       0.128053   \n",
       "perimeter_se                   0.660391             0.710650       0.313893   \n",
       "area_se                        0.617427             0.690299       0.223970   \n",
       "smoothness_se                  0.098564             0.027653       0.187321   \n",
       "compactness_se                 0.670279             0.490424       0.421659   \n",
       "concavity_se                   0.691270             0.439167       0.342627   \n",
       "concave points_se              0.683260             0.615634       0.393298   \n",
       "symmetry_se                    0.178009             0.095351       0.449137   \n",
       "fractal_dimension_se           0.449301             0.257584       0.331786   \n",
       "radius_worst                   0.688236             0.830318       0.185728   \n",
       "texture_worst                  0.299879             0.292752       0.090651   \n",
       "perimeter_worst                0.729565             0.855923       0.219169   \n",
       "area_worst                     0.675987             0.809630       0.177193   \n",
       "smoothness_worst               0.448822             0.452753       0.426675   \n",
       "compactness_worst              0.754968             0.667454       0.473200   \n",
       "concavity_worst                0.884103             0.752399       0.433721   \n",
       "concave points_worst           0.861323             0.910155       0.430297   \n",
       "symmetry_worst                 0.409464             0.375744       0.699826   \n",
       "fractal_dimension_worst        0.514930             0.368661       0.438413   \n",
       "\n",
       "                         ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "diagnosis                ...      0.776454       0.456903         0.782914   \n",
       "radius_mean              ...      0.969539       0.297008         0.965137   \n",
       "texture_mean             ...      0.352573       0.912045         0.358040   \n",
       "perimeter_mean           ...      0.969476       0.303038         0.970387   \n",
       "area_mean                ...      0.962746       0.287489         0.959120   \n",
       "smoothness_mean          ...      0.213120       0.036072         0.238853   \n",
       "compactness_mean         ...      0.535315       0.248133         0.590210   \n",
       "concavity_mean           ...      0.688236       0.299879         0.729565   \n",
       "concave points_mean      ...      0.830318       0.292752         0.855923   \n",
       "symmetry_mean            ...      0.185728       0.090651         0.219169   \n",
       "fractal_dimension_mean   ...     -0.253691      -0.051269        -0.205151   \n",
       "radius_se                ...      0.715065       0.194799         0.719684   \n",
       "texture_se               ...     -0.111690       0.409003        -0.102242   \n",
       "perimeter_se             ...      0.697201       0.200371         0.721031   \n",
       "area_se                  ...      0.757373       0.196497         0.761213   \n",
       "smoothness_se            ...     -0.230691      -0.074743        -0.217304   \n",
       "compactness_se           ...      0.204607       0.143003         0.260516   \n",
       "concavity_se             ...      0.186904       0.100241         0.226680   \n",
       "concave points_se        ...      0.358127       0.086741         0.394999   \n",
       "symmetry_se              ...     -0.128121      -0.077473        -0.103753   \n",
       "fractal_dimension_se     ...     -0.037488      -0.003195        -0.001000   \n",
       "radius_worst             ...      1.000000       0.359921         0.993708   \n",
       "texture_worst            ...      0.359921       1.000000         0.365098   \n",
       "perimeter_worst          ...      0.993708       0.365098         1.000000   \n",
       "area_worst               ...      0.984015       0.345842         0.977578   \n",
       "smoothness_worst         ...      0.216574       0.225429         0.236775   \n",
       "compactness_worst        ...      0.475820       0.360832         0.529408   \n",
       "concavity_worst          ...      0.573975       0.368366         0.618344   \n",
       "concave points_worst     ...      0.787424       0.359755         0.816322   \n",
       "symmetry_worst           ...      0.243529       0.233027         0.269493   \n",
       "fractal_dimension_worst  ...      0.093492       0.219122         0.138957   \n",
       "\n",
       "                         area_worst  smoothness_worst  compactness_worst  \\\n",
       "diagnosis                  0.733825          0.421465           0.590998   \n",
       "radius_mean                0.941082          0.119616           0.413463   \n",
       "texture_mean               0.343546          0.077503           0.277830   \n",
       "perimeter_mean             0.941550          0.150549           0.455774   \n",
       "area_mean                  0.959213          0.123523           0.390410   \n",
       "smoothness_mean            0.206718          0.805324           0.472468   \n",
       "compactness_mean           0.509604          0.565541           0.865809   \n",
       "concavity_mean             0.675987          0.448822           0.754968   \n",
       "concave points_mean        0.809630          0.452753           0.667454   \n",
       "symmetry_mean              0.177193          0.426675           0.473200   \n",
       "fractal_dimension_mean    -0.231854          0.504942           0.458798   \n",
       "radius_se                  0.751548          0.141919           0.287103   \n",
       "texture_se                -0.083195         -0.073658          -0.092439   \n",
       "perimeter_se               0.730713          0.130054           0.341919   \n",
       "area_se                    0.811408          0.125389           0.283257   \n",
       "smoothness_se             -0.182195          0.314457          -0.055558   \n",
       "compactness_se             0.199371          0.227394           0.678780   \n",
       "concavity_se               0.188353          0.168481           0.484858   \n",
       "concave points_se          0.342271          0.215351           0.452888   \n",
       "symmetry_se               -0.110343         -0.012662           0.060255   \n",
       "fractal_dimension_se      -0.022736          0.170568           0.390159   \n",
       "radius_worst               0.984015          0.216574           0.475820   \n",
       "texture_worst              0.345842          0.225429           0.360832   \n",
       "perimeter_worst            0.977578          0.236775           0.529408   \n",
       "area_worst                 1.000000          0.209145           0.438296   \n",
       "smoothness_worst           0.209145          1.000000           0.568187   \n",
       "compactness_worst          0.438296          0.568187           1.000000   \n",
       "concavity_worst            0.543331          0.518523           0.892261   \n",
       "concave points_worst       0.747419          0.547691           0.801080   \n",
       "symmetry_worst             0.209146          0.493838           0.614441   \n",
       "fractal_dimension_worst    0.079647          0.617624           0.810455   \n",
       "\n",
       "                         concavity_worst  concave points_worst  \\\n",
       "diagnosis                       0.659610              0.793566   \n",
       "radius_mean                     0.526911              0.744214   \n",
       "texture_mean                    0.301025              0.295316   \n",
       "perimeter_mean                  0.563879              0.771241   \n",
       "area_mean                       0.512606              0.722017   \n",
       "smoothness_mean                 0.434926              0.503053   \n",
       "compactness_mean                0.816275              0.815573   \n",
       "concavity_mean                  0.884103              0.861323   \n",
       "concave points_mean             0.752399              0.910155   \n",
       "symmetry_mean                   0.433721              0.430297   \n",
       "fractal_dimension_mean          0.346234              0.175325   \n",
       "radius_se                       0.380585              0.531062   \n",
       "texture_se                     -0.068956             -0.119638   \n",
       "perimeter_se                    0.418899              0.554897   \n",
       "area_se                         0.385100              0.538166   \n",
       "smoothness_se                  -0.058298             -0.102007   \n",
       "compactness_se                  0.639147              0.483208   \n",
       "concavity_se                    0.662564              0.440472   \n",
       "concave points_se               0.549592              0.602450   \n",
       "symmetry_se                     0.037119             -0.030413   \n",
       "fractal_dimension_se            0.379975              0.215204   \n",
       "radius_worst                    0.573975              0.787424   \n",
       "texture_worst                   0.368366              0.359755   \n",
       "perimeter_worst                 0.618344              0.816322   \n",
       "area_worst                      0.543331              0.747419   \n",
       "smoothness_worst                0.518523              0.547691   \n",
       "compactness_worst               0.892261              0.801080   \n",
       "concavity_worst                 1.000000              0.855434   \n",
       "concave points_worst            0.855434              1.000000   \n",
       "symmetry_worst                  0.532520              0.502528   \n",
       "fractal_dimension_worst         0.686511              0.511114   \n",
       "\n",
       "                         symmetry_worst  fractal_dimension_worst  \n",
       "diagnosis                      0.416294                 0.323872  \n",
       "radius_mean                    0.163953                 0.007066  \n",
       "texture_mean                   0.105008                 0.119205  \n",
       "perimeter_mean                 0.189115                 0.051019  \n",
       "area_mean                      0.143570                 0.003738  \n",
       "smoothness_mean                0.394309                 0.499316  \n",
       "compactness_mean               0.510223                 0.687382  \n",
       "concavity_mean                 0.409464                 0.514930  \n",
       "concave points_mean            0.375744                 0.368661  \n",
       "symmetry_mean                  0.699826                 0.438413  \n",
       "fractal_dimension_mean         0.334019                 0.767297  \n",
       "radius_se                      0.094543                 0.049559  \n",
       "texture_se                    -0.128215                -0.045655  \n",
       "perimeter_se                   0.109930                 0.085433  \n",
       "area_se                        0.074126                 0.017539  \n",
       "smoothness_se                 -0.107342                 0.101480  \n",
       "compactness_se                 0.277878                 0.590973  \n",
       "concavity_se                   0.197788                 0.439329  \n",
       "concave points_se              0.143116                 0.310655  \n",
       "symmetry_se                    0.389402                 0.078079  \n",
       "fractal_dimension_se           0.111094                 0.591328  \n",
       "radius_worst                   0.243529                 0.093492  \n",
       "texture_worst                  0.233027                 0.219122  \n",
       "perimeter_worst                0.269493                 0.138957  \n",
       "area_worst                     0.209146                 0.079647  \n",
       "smoothness_worst               0.493838                 0.617624  \n",
       "compactness_worst              0.614441                 0.810455  \n",
       "concavity_worst                0.532520                 0.686511  \n",
       "concave points_worst           0.502528                 0.511114  \n",
       "symmetry_worst                 1.000000                 0.537848  \n",
       "fractal_dimension_worst        0.537848                 1.000000  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the correlation of the 32 columns to one another\n",
    "\n",
    "df.iloc[:,1:32].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20775dfd9d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#visualizing the correlation by using heatmap\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df.iloc[:,1:15].corr(),annot=True,fmt='.0%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the dataset into independent (X) and dependent (Y) datasets/variables\n",
    "\n",
    "X=df.iloc[:,2:31].values\n",
    "\n",
    "Y=df.iloc[:,1].values  #diagnosis values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 1\n",
      " 0 1 0 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0\n",
      " 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1\n",
      " 0 1 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0\n",
      " 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0\n",
      " 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1\n",
      " 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
      " 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0\n",
      " 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1\n",
      " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 1 1 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the dataset into 80% training set  and 20% testing set \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size =0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scale the data (Feature Scaling)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function Creationg for 3 different Models\n",
    "\n",
    "def models(X_train , Y_train):\n",
    "    \n",
    "    #Logistic Regression\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    log = LogisticRegression(random_state=0)\n",
    "    log.fit(X_train, Y_train)\n",
    "    \n",
    "    #Decision Tree\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    tree = DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
    "    tree.fit(X_train, Y_train)\n",
    "    \n",
    "    #Random Forest Classifier\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    forest = RandomForestClassifier(n_estimators=10, criterion='entropy',random_state=0)\n",
    "    forest.fit(X_train,Y_train)\n",
    "    \n",
    "    # Print the model accuracy on the training data\n",
    "    print('[0]Logistic Regression trainning accuracy:',log.score(X_train,Y_train))\n",
    "    print('[1]Decision trainning accuracy:',tree.score(X_train,Y_train))\n",
    "    print('[2]Random Forest classifier trainning accuracy:',forest.score(X_train,Y_train))\n",
    "    \n",
    "    return log,tree,forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]Logistic Regression trainning accuracy: 0.9912087912087912\n",
      "[1]Decision trainning accuracy: 1.0\n",
      "[2]Random Forest classifier trainning accuracy: 0.9978021978021978\n"
     ]
    }
   ],
   "source": [
    "#Getting all the models\n",
    "model = models(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model  0\n",
      "[[66  1]\n",
      " [ 3 44]]\n",
      "Accuracy of model  0.9649122807017544\n",
      "Model  1\n",
      "[[64  3]\n",
      " [ 4 43]]\n",
      "Accuracy of model  0.9385964912280702\n",
      "Model  2\n",
      "[[67  0]\n",
      " [ 3 44]]\n",
      "Accuracy of model  0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "#testing the model on the test data (confusion matrix)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "for i in range(len(model)):\n",
    "    print('Model ', i)\n",
    "    cm = confusion_matrix(Y_test,model[i].predict(X_test))\n",
    "    print(cm)\n",
    "    TP = cm[0][0]\n",
    "    TN = cm[1][1]\n",
    "    FP = cm[0][1]\n",
    "    FN = cm[1][0]\n",
    "    Accuracy = (TP + TN)/(TP+TN+FN+FP)\n",
    "    print('Accuracy of model ', Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model  0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97        67\n",
      "           1       0.98      0.94      0.96        47\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.97      0.96      0.96       114\n",
      "weighted avg       0.97      0.96      0.96       114\n",
      "\n",
      "Accuracy :  0.9649122807017544\n",
      "Model  1\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.96      0.95        67\n",
      "           1       0.93      0.91      0.92        47\n",
      "\n",
      "    accuracy                           0.94       114\n",
      "   macro avg       0.94      0.94      0.94       114\n",
      "weighted avg       0.94      0.94      0.94       114\n",
      "\n",
      "Accuracy :  0.9385964912280702\n",
      "Model  2\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98        67\n",
      "           1       1.00      0.94      0.97        47\n",
      "\n",
      "    accuracy                           0.97       114\n",
      "   macro avg       0.98      0.97      0.97       114\n",
      "weighted avg       0.97      0.97      0.97       114\n",
      "\n",
      "Accuracy :  0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "#Another way to get metrices of the models\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for i in range(len(model)):\n",
    "    print('Model ', i)\n",
    "    print( classification_report(Y_test,model[i].predict(X_test)))\n",
    "    print('Accuracy : ', accuracy_score(Y_test,model[i].predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Predictions: \n",
      "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
      " 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 0\n",
      " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
      " 1 1 0]\n",
      "Actual Result :   \n",
      "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1\n",
      " 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0\n",
      " 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "#Predictions by random forest classifier model (third model as its accuracy is highest)\n",
    "\n",
    "pred = model[2].predict(X_test)\n",
    "print('Model Predictions: ')\n",
    "print(pred)\n",
    "print('Actual Result :   ')\n",
    "print(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cancer_Prediction_Model.joblib']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dumping the Model\n",
    "\n",
    "from joblib import dump\n",
    "dump(model[2],\"Cancer_Prediction_Model.joblib\")"
   ]
  }
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