MohdAzamSayeed/cancer diagnosis for teaching.ipynb Secret

## cancer diagnosis for teaching.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1: Loading Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "import time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0  ...         25.38          17.33           184.60      2019.0   \n",
       "1  ...         24.99          23.41           158.80      1956.0   \n",
       "2  ...         23.57          25.53           152.50      1709.0   \n",
       "3  ...         14.91          26.50            98.87       567.7   \n",
       "4  ...         22.54          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  \n",
       "0          0.4601                  0.11890  \n",
       "1          0.2750                  0.08902  \n",
       "2          0.3613                  0.08758  \n",
       "3          0.6638                  0.17300  \n",
       "4          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"data2.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
      "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
      "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
      "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
      "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
      "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
      "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
      "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
      "       'symmetry_worst', 'fractal_dimension_worst'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    " col=data.columns\n",
    " print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.diagnosis\n",
    "x = data.drop([\"id\",\"diagnosis\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Separate Target from Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</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>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <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 × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   fractal_dimension_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Plot Diagnosis Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y,label =\"Count\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of Benign Tumors 357\n",
      "No. of Malignant Tumors 212\n"
     ]
    }
   ],
   "source": [
    "B, M = y.value_counts()\n",
    "print(\"No. of Benign Tumors\", B)\n",
    "print(\"No. of Malignant Tumors\", M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  fractal_dimension_mean  ...  radius_worst  \\\n",
       "count     569.000000              569.000000  ...    569.000000   \n",
       "mean        0.181162                0.062798  ...     16.269190   \n",
       "std         0.027414                0.007060  ...      4.833242   \n",
       "min         0.106000                0.049960  ...      7.930000   \n",
       "25%         0.161900                0.057700  ...     13.010000   \n",
       "50%         0.179200                0.061540  ...     14.970000   \n",
       "75%         0.195700                0.066120  ...     18.790000   \n",
       "max         0.304000                0.097440  ...     36.040000   \n",
       "\n",
       "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
       "count     569.000000       569.000000   569.000000        569.000000   \n",
       "mean       25.677223       107.261213   880.583128          0.132369   \n",
       "std         6.146258        33.602542   569.356993          0.022832   \n",
       "min        12.020000        50.410000   185.200000          0.071170   \n",
       "25%        21.080000        84.110000   515.300000          0.116600   \n",
       "50%        25.410000        97.660000   686.500000          0.131300   \n",
       "75%        29.720000       125.400000  1084.000000          0.146000   \n",
       "max        49.540000       251.200000  4254.000000          0.222600   \n",
       "\n",
       "       compactness_worst  concavity_worst  concave points_worst  \\\n",
       "count         569.000000       569.000000            569.000000   \n",
       "mean            0.254265         0.272188              0.114606   \n",
       "std             0.157336         0.208624              0.065732   \n",
       "min             0.027290         0.000000              0.000000   \n",
       "25%             0.147200         0.114500              0.064930   \n",
       "50%             0.211900         0.226700              0.099930   \n",
       "75%             0.339100         0.382900              0.161400   \n",
       "max             1.058000         1.252000              0.291000   \n",
       "\n",
       "       symmetry_worst  fractal_dimension_worst  \n",
       "count      569.000000               569.000000  \n",
       "mean         0.290076                 0.083946  \n",
       "std          0.061867                 0.018061  \n",
       "min          0.156500                 0.055040  \n",
       "25%          0.250400                 0.071460  \n",
       "50%          0.282200                 0.080040  \n",
       "75%          0.317900                 0.092080  \n",
       "max          0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<h2 align=center> Data Visualization </h2>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Visualizing Standardized Data with Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           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",
      "564    926424         M        21.56         22.39          142.00     1479.0   \n",
      "565    926682         M        20.13         28.25          131.20     1261.0   \n",
      "566    926954         M        16.60         28.08          108.30      858.1   \n",
      "567    927241         M        20.60         29.33          140.10     1265.0   \n",
      "568     92751         B         7.76         24.54           47.92      181.0   \n",
      "\n",
      "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
      "0            0.11840           0.27760         0.30010              0.14710   \n",
      "1            0.08474           0.07864         0.08690              0.07017   \n",
      "2            0.10960           0.15990         0.19740              0.12790   \n",
      "3            0.14250           0.28390         0.24140              0.10520   \n",
      "4            0.10030           0.13280         0.19800              0.10430   \n",
      "..               ...               ...             ...                  ...   \n",
      "564          0.11100           0.11590         0.24390              0.13890   \n",
      "565          0.09780           0.10340         0.14400              0.09791   \n",
      "566          0.08455           0.10230         0.09251              0.05302   \n",
      "567          0.11780           0.27700         0.35140              0.15200   \n",
      "568          0.05263           0.04362         0.00000              0.00000   \n",
      "\n",
      "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
      "0    ...        25.380          17.33           184.60      2019.0   \n",
      "1    ...        24.990          23.41           158.80      1956.0   \n",
      "2    ...        23.570          25.53           152.50      1709.0   \n",
      "3    ...        14.910          26.50            98.87       567.7   \n",
      "4    ...        22.540          16.67           152.20      1575.0   \n",
      "..   ...           ...            ...              ...         ...   \n",
      "564  ...        25.450          26.40           166.10      2027.0   \n",
      "565  ...        23.690          38.25           155.00      1731.0   \n",
      "566  ...        18.980          34.12           126.70      1124.0   \n",
      "567  ...        25.740          39.42           184.60      1821.0   \n",
      "568  ...         9.456          30.37            59.16       268.6   \n",
      "\n",
      "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
      "0             0.16220            0.66560           0.7119   \n",
      "1             0.12380            0.18660           0.2416   \n",
      "2             0.14440            0.42450           0.4504   \n",
      "3             0.20980            0.86630           0.6869   \n",
      "4             0.13740            0.20500           0.4000   \n",
      "..                ...                ...              ...   \n",
      "564           0.14100            0.21130           0.4107   \n",
      "565           0.11660            0.19220           0.3215   \n",
      "566           0.11390            0.30940           0.3403   \n",
      "567           0.16500            0.86810           0.9387   \n",
      "568           0.08996            0.06444           0.0000   \n",
      "\n",
      "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
      "0                  0.2654          0.4601                  0.11890  \n",
      "1                  0.1860          0.2750                  0.08902  \n",
      "2                  0.2430          0.3613                  0.08758  \n",
      "3                  0.2575          0.6638                  0.17300  \n",
      "4                  0.1625          0.2364                  0.07678  \n",
      "..                    ...             ...                      ...  \n",
      "564                0.2216          0.2060                  0.07115  \n",
      "565                0.1628          0.2572                  0.06637  \n",
      "566                0.1418          0.2218                  0.07820  \n",
      "567                0.2650          0.4087                  0.12400  \n",
      "568                0.0000          0.2871                  0.07039  \n",
      "\n",
      "[569 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_dia = y\n",
    "data = x\n",
    "data_n_2 = (data - data.mean()) / (data.std())              \n",
    "data = pd.concat([y,data_n_2.iloc[:,0:10]],axis=1)\n",
    "data = pd.melt(data,id_vars=\"diagnosis\",\n",
    "                    var_name=\"features\",\n",
    "                    value_name='value')\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.violinplot(x=\"features\", y=\"value\", hue=\"diagnosis\", data=data, split=True, inner=\"points\")\n",
    "plt.xticks(rotation=45);\n",
    "# inner{“box”, “quartile”, “point”, “stick”, None}, optional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Violin Plots and Box Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([y,data_n_2.iloc[:,10:20]],axis=1)\n",
    "data = pd.melt(data,id_vars=\"diagnosis\",\n",
    "                    var_name=\"features\",\n",
    "                    value_name='value')\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.violinplot(x=\"features\", y=\"value\", hue=\"diagnosis\", data=data,split=False, inner=\"quartile\")\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([y,data_n_2.iloc[:,20:31]],axis=1)\n",
    "data = pd.melt(data,id_vars=\"diagnosis\",\n",
    "                    var_name=\"features\",\n",
    "                    value_name='value')\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot(x=\"features\", y=\"value\", hue=\"diagnosis\", data=data, saturation=1, fliersize=\"5\", linewidth=3)\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>1.885031</td>\n",
       "      <td>-1.358098</td>\n",
       "      <td>2.301575</td>\n",
       "      <td>1.999478</td>\n",
       "      <td>1.306537</td>\n",
       "      <td>2.614365</td>\n",
       "      <td>2.107672</td>\n",
       "      <td>2.294058</td>\n",
       "      <td>2.748204</td>\n",
       "      <td>1.935312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>1.804340</td>\n",
       "      <td>-0.368879</td>\n",
       "      <td>1.533776</td>\n",
       "      <td>1.888827</td>\n",
       "      <td>-0.375282</td>\n",
       "      <td>-0.430066</td>\n",
       "      <td>-0.146620</td>\n",
       "      <td>1.086129</td>\n",
       "      <td>-0.243675</td>\n",
       "      <td>0.280943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>1.510541</td>\n",
       "      <td>-0.023953</td>\n",
       "      <td>1.346291</td>\n",
       "      <td>1.455004</td>\n",
       "      <td>0.526944</td>\n",
       "      <td>1.081980</td>\n",
       "      <td>0.854222</td>\n",
       "      <td>1.953282</td>\n",
       "      <td>1.151242</td>\n",
       "      <td>0.201214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>-0.281217</td>\n",
       "      <td>0.133866</td>\n",
       "      <td>-0.249720</td>\n",
       "      <td>-0.549538</td>\n",
       "      <td>3.391291</td>\n",
       "      <td>3.889975</td>\n",
       "      <td>1.987839</td>\n",
       "      <td>2.173873</td>\n",
       "      <td>6.040726</td>\n",
       "      <td>4.930672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>1.297434</td>\n",
       "      <td>-1.465481</td>\n",
       "      <td>1.337363</td>\n",
       "      <td>1.219651</td>\n",
       "      <td>0.220362</td>\n",
       "      <td>-0.313119</td>\n",
       "      <td>0.612640</td>\n",
       "      <td>0.728618</td>\n",
       "      <td>-0.867590</td>\n",
       "      <td>-0.396751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>M</td>\n",
       "      <td>1.899514</td>\n",
       "      <td>0.117596</td>\n",
       "      <td>1.751022</td>\n",
       "      <td>2.013529</td>\n",
       "      <td>0.378033</td>\n",
       "      <td>-0.273077</td>\n",
       "      <td>0.663928</td>\n",
       "      <td>1.627719</td>\n",
       "      <td>-1.358963</td>\n",
       "      <td>-0.708467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>M</td>\n",
       "      <td>1.535369</td>\n",
       "      <td>2.045599</td>\n",
       "      <td>1.420690</td>\n",
       "      <td>1.493644</td>\n",
       "      <td>-0.690623</td>\n",
       "      <td>-0.394473</td>\n",
       "      <td>0.236365</td>\n",
       "      <td>0.733182</td>\n",
       "      <td>-0.531387</td>\n",
       "      <td>-0.973122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>M</td>\n",
       "      <td>0.560868</td>\n",
       "      <td>1.373645</td>\n",
       "      <td>0.578492</td>\n",
       "      <td>0.427529</td>\n",
       "      <td>-0.808876</td>\n",
       "      <td>0.350427</td>\n",
       "      <td>0.326479</td>\n",
       "      <td>0.413705</td>\n",
       "      <td>-1.103578</td>\n",
       "      <td>-0.318129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>M</td>\n",
       "      <td>1.959515</td>\n",
       "      <td>2.235958</td>\n",
       "      <td>2.301575</td>\n",
       "      <td>1.651717</td>\n",
       "      <td>1.429169</td>\n",
       "      <td>3.901415</td>\n",
       "      <td>3.194794</td>\n",
       "      <td>2.287972</td>\n",
       "      <td>1.917396</td>\n",
       "      <td>2.217684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>B</td>\n",
       "      <td>-1.409652</td>\n",
       "      <td>0.763518</td>\n",
       "      <td>-1.431475</td>\n",
       "      <td>-1.074867</td>\n",
       "      <td>-1.857384</td>\n",
       "      <td>-1.206491</td>\n",
       "      <td>-1.304683</td>\n",
       "      <td>-1.743529</td>\n",
       "      <td>-0.048096</td>\n",
       "      <td>-0.750546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0           M      1.885031      -1.358098         2.301575    1.999478   \n",
       "1           M      1.804340      -0.368879         1.533776    1.888827   \n",
       "2           M      1.510541      -0.023953         1.346291    1.455004   \n",
       "3           M     -0.281217       0.133866        -0.249720   -0.549538   \n",
       "4           M      1.297434      -1.465481         1.337363    1.219651   \n",
       "..        ...           ...            ...              ...         ...   \n",
       "564         M      1.899514       0.117596         1.751022    2.013529   \n",
       "565         M      1.535369       2.045599         1.420690    1.493644   \n",
       "566         M      0.560868       1.373645         0.578492    0.427529   \n",
       "567         M      1.959515       2.235958         2.301575    1.651717   \n",
       "568         B     -1.409652       0.763518        -1.431475   -1.074867   \n",
       "\n",
       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0            1.306537           2.614365         2.107672   \n",
       "1           -0.375282          -0.430066        -0.146620   \n",
       "2            0.526944           1.081980         0.854222   \n",
       "3            3.391291           3.889975         1.987839   \n",
       "4            0.220362          -0.313119         0.612640   \n",
       "..                ...                ...              ...   \n",
       "564          0.378033          -0.273077         0.663928   \n",
       "565         -0.690623          -0.394473         0.236365   \n",
       "566         -0.808876           0.350427         0.326479   \n",
       "567          1.429169           3.901415         3.194794   \n",
       "568         -1.857384          -1.206491        -1.304683   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                2.294058        2.748204                 1.935312  \n",
       "1                1.086129       -0.243675                 0.280943  \n",
       "2                1.953282        1.151242                 0.201214  \n",
       "3                2.173873        6.040726                 4.930672  \n",
       "4                0.728618       -0.867590                -0.396751  \n",
       "..                    ...             ...                      ...  \n",
       "564              1.627719       -1.358963                -0.708467  \n",
       "565              0.733182       -0.531387                -0.973122  \n",
       "566              0.413705       -1.103578                -0.318129  \n",
       "567              2.287972        1.917396                 2.217684  \n",
       "568             -1.743529       -0.048096                -0.750546  \n",
       "\n",
       "[569 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([y,data_n_2.iloc[:,20:31]],axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>features</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>radius_worst</td>\n",
       "      <td>1.885031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>radius_worst</td>\n",
       "      <td>1.804340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>radius_worst</td>\n",
       "      <td>1.510541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>radius_worst</td>\n",
       "      <td>-0.281217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>radius_worst</td>\n",
       "      <td>1.297434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5685</th>\n",
       "      <td>M</td>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>-0.708467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5686</th>\n",
       "      <td>M</td>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>-0.973122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5687</th>\n",
       "      <td>M</td>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>-0.318129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5688</th>\n",
       "      <td>M</td>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>2.217684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5689</th>\n",
       "      <td>B</td>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>-0.750546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5690 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     diagnosis                 features     value\n",
       "0            M             radius_worst  1.885031\n",
       "1            M             radius_worst  1.804340\n",
       "2            M             radius_worst  1.510541\n",
       "3            M             radius_worst -0.281217\n",
       "4            M             radius_worst  1.297434\n",
       "...        ...                      ...       ...\n",
       "5685         M  fractal_dimension_worst -0.708467\n",
       "5686         M  fractal_dimension_worst -0.973122\n",
       "5687         M  fractal_dimension_worst -0.318129\n",
       "5688         M  fractal_dimension_worst  2.217684\n",
       "5689         B  fractal_dimension_worst -0.750546\n",
       "\n",
       "[5690 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.melt(data,id_vars=\"diagnosis\",\n",
    "                    var_name=\"features\",\n",
    "                    value_name='value')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',\n",
       "       'smoothness_worst', 'compactness_worst', 'concavity_worst',\n",
       "       'concave points_worst', 'symmetry_worst',\n",
       "       'fractal_dimension_worst'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['features'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fractal_dimension_worst    569\n",
       "area_worst                 569\n",
       "texture_worst              569\n",
       "smoothness_worst           569\n",
       "compactness_worst          569\n",
       "concave points_worst       569\n",
       "symmetry_worst             569\n",
       "perimeter_worst            569\n",
       "concavity_worst            569\n",
       "radius_worst               569\n",
       "Name: features, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['features'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Using Joint Plots for Feature Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "sns.jointplot(x.loc[:,'concavity_worst'],\n",
    "              x.loc[:,'concave points_worst'],\n",
    "              kind=\"hex\",\n",
    "              color=\"g\")\n",
    ";\n",
    "# kind{ “scatter” | “reg” | “resid” | “kde” | “hex” }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "###  Observing the Distribution of Values and their Variance with Swarm Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#sns.set(style=\"whitegrid\", palette=\"muted\")\n",
    "data_dia = y\n",
    "data = x\n",
    "data_n_2 = (data - data.mean()) / (data.std())  \n",
    "data = pd.concat([y,data_n_2.iloc[:,0:10]],axis=1)\n",
    "data = pd.melt(data,id_vars=\"diagnosis\",\n",
    "                    var_name=\"features\",\n",
    "                    value_name='value')\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.swarmplot(x=\"features\", y=\"value\", hue=\"diagnosis\", data=data,size=8)\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Observing all Pair-wise Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#correlation map\n",
    "f,ax = plt.subplots(figsize=(18, 18))\n",
    "sns.heatmap(x.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2: Dropping Correlated Columns from Feature Matrix\n",
    "***\n",
    "Note: If you are starting the notebook from this task, you can run cells from all the previous tasks in the kernel by going to the top menu and Kernel > Restart and Run All\\n\",\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''['perimeter_mean','radius_mean','compactness_mean',\n",
    "              'concave points_mean','radius_se','perimeter_se',\n",
    "              'radius_worst','perimeter_worst','compactness_worst',\n",
    "              'concave points_worst','compactness_se','concave points_se',\n",
    "              'texture_worst','area_worst']'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3: Classification using XGBoost (minimal feature selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import f1_score,confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"white\")\n",
    "df = x.loc[:,['radius_worst','perimeter_worst','area_worst']]\n",
    "g = sns.PairGrid(df, diag_sharey=False)\n",
    "g.map_lower(sns.kdeplot, cmap=\"Blues_d\")\n",
    "g.map_upper(plt.scatter)\n",
    "g.map_diag(sns.kdeplot, lw=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4: Univariate Feature Selection and XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Recursive Feature Elimination with Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6: Feature Extraction using Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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