Breast Cancer Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "198d72e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score,confusion_matrix,accuracy_score,precision_score\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.feature_selection import RFECV\n",
    "import itertools\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7144df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.ipynb_checkpoints', '.virtual_documents', 'Advertising-class', 'Alireza_Bakhshifar_400671009.ipynb', 'Auto-mpg_reg', 'Breast-cancer-wisconsin-class', 'breast-final_csv.csv', 'Ecommerce Purchases.csv', 'Forestfire-reg', 'Guid.docx', 'Guid.pdf', 'IRIS-class', 'Loan_Data-class', 'Project (1).rar', 'USA_Housing-reg']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "li = os.listdir()\n",
    "print(li)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a44145",
   "metadata": {},
   "source": [
    "# Business Understanding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8df0a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('breast-final_csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d867b8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Clump_Thickness', 'Cell_Size_Uniformity', 'Cell_Shape_Uniformity',\n",
       "       'Marginal_Adhesion', 'Single_Epi_Cell_Size', 'Bare_Nuclei',\n",
       "       'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8ad897b",
   "metadata": {},
   "source": [
    "### Features descriptions\n",
    "**Clump_Thickness**  :   indicating grouping of cancer cells in multilayer<br>\n",
    "**Cell_Size_Uniforimity** : indicating metastasis to lymph nodes<br>\n",
    "__Cell_Shape_Uniformity__ : identifying cancerous cells of varying size<br>\n",
    "__Marginal_Adhesion__ : suggesting loss of adhesion, i.e., a sign of malignancy but the cancerous cells lose this property so \n",
    "this retention of adhesion is an indication of malignancy<br>\n",
    "__Single_Epi_Cell_Size__ :if the SECS become larger, it may be a malignant cell<br>\n",
    "__Bare_Nuclei__ :without cytoplasm coating, found in benign tumors<br>\n",
    "__Bland_Chromatin__ :usually found in benign cells<br>\n",
    "__Normal_Nucleoli__ :generally very small in benign cell<br>\n",
    "__Mitoses__ :the process in cell division by which the nucleus divides<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7dd748a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump_Thickness</th>\n",
       "      <th>Cell_Size_Uniformity</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epi_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clump_Thickness  Cell_Size_Uniformity  Cell_Shape_Uniformity  \\\n",
       "0                  5                     1                      1   \n",
       "1                  5                     4                      4   \n",
       "2                  3                     1                      1   \n",
       "3                  6                     8                      8   \n",
       "4                  4                     1                      1   \n",
       "..               ...                   ...                    ...   \n",
       "694                3                     1                      1   \n",
       "695                2                     1                      1   \n",
       "696                5                    10                     10   \n",
       "697                4                     8                      6   \n",
       "698                4                     8                      8   \n",
       "\n",
       "     Marginal_Adhesion  Single_Epi_Cell_Size  Bare_Nuclei  Bland_Chromatin  \\\n",
       "0                    1                     2          1.0                3   \n",
       "1                    5                     7         10.0                3   \n",
       "2                    1                     2          2.0                3   \n",
       "3                    1                     3          4.0                3   \n",
       "4                    3                     2          1.0                3   \n",
       "..                 ...                   ...          ...              ...   \n",
       "694                  1                     3          2.0                1   \n",
       "695                  1                     2          1.0                1   \n",
       "696                  3                     7          3.0                8   \n",
       "697                  4                     3          4.0               10   \n",
       "698                  5                     4          5.0               10   \n",
       "\n",
       "     Normal_Nucleoli  Mitoses      Class  \n",
       "0                  1        1     benign  \n",
       "1                  2        1     benign  \n",
       "2                  1        1     benign  \n",
       "3                  7        1     benign  \n",
       "4                  1        1     benign  \n",
       "..               ...      ...        ...  \n",
       "694                1        1     benign  \n",
       "695                1        1     benign  \n",
       "696               10        2  malignant  \n",
       "697                6        1  malignant  \n",
       "698                4        1  malignant  \n",
       "\n",
       "[699 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195efbed",
   "metadata": {},
   "source": [
    "# Data Understanding and Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a88ae088",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Clump_Thickness        699 non-null    int64  \n",
      " 1   Cell_Size_Uniformity   699 non-null    int64  \n",
      " 2   Cell_Shape_Uniformity  699 non-null    int64  \n",
      " 3   Marginal_Adhesion      699 non-null    int64  \n",
      " 4   Single_Epi_Cell_Size   699 non-null    int64  \n",
      " 5   Bare_Nuclei            683 non-null    float64\n",
      " 6   Bland_Chromatin        699 non-null    int64  \n",
      " 7   Normal_Nucleoli        699 non-null    int64  \n",
      " 8   Mitoses                699 non-null    int64  \n",
      " 9   Class                  699 non-null    object \n",
      "dtypes: float64(1), int64(8), object(1)\n",
      "memory usage: 54.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "805a8b7b",
   "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>Clump_Thickness</th>\n",
       "      <th>Cell_Size_Uniformity</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epi_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.417740</td>\n",
       "      <td>3.134478</td>\n",
       "      <td>3.207439</td>\n",
       "      <td>2.806867</td>\n",
       "      <td>3.216023</td>\n",
       "      <td>3.544656</td>\n",
       "      <td>3.437768</td>\n",
       "      <td>2.866953</td>\n",
       "      <td>1.589413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.815741</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>3.643857</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.715078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clump_Thickness  Cell_Size_Uniformity  Cell_Shape_Uniformity  \\\n",
       "count       699.000000            699.000000             699.000000   \n",
       "mean          4.417740              3.134478               3.207439   \n",
       "std           2.815741              3.051459               2.971913   \n",
       "min           1.000000              1.000000               1.000000   \n",
       "25%           2.000000              1.000000               1.000000   \n",
       "50%           4.000000              1.000000               1.000000   \n",
       "75%           6.000000              5.000000               5.000000   \n",
       "max          10.000000             10.000000              10.000000   \n",
       "\n",
       "       Marginal_Adhesion  Single_Epi_Cell_Size  Bare_Nuclei  Bland_Chromatin  \\\n",
       "count         699.000000            699.000000   683.000000       699.000000   \n",
       "mean            2.806867              3.216023     3.544656         3.437768   \n",
       "std             2.855379              2.214300     3.643857         2.438364   \n",
       "min             1.000000              1.000000     1.000000         1.000000   \n",
       "25%             1.000000              2.000000     1.000000         2.000000   \n",
       "50%             1.000000              2.000000     1.000000         3.000000   \n",
       "75%             4.000000              4.000000     6.000000         5.000000   \n",
       "max            10.000000             10.000000    10.000000        10.000000   \n",
       "\n",
       "       Normal_Nucleoli     Mitoses  \n",
       "count       699.000000  699.000000  \n",
       "mean          2.866953    1.589413  \n",
       "std           3.053634    1.715078  \n",
       "min           1.000000    1.000000  \n",
       "25%           1.000000    1.000000  \n",
       "50%           1.000000    1.000000  \n",
       "75%           4.000000    1.000000  \n",
       "max          10.000000   10.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cd14683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.heatmap(df.isnull(), cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91b34247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clump_Thickness           0\n",
       "Cell_Size_Uniformity      0\n",
       "Cell_Shape_Uniformity     0\n",
       "Marginal_Adhesion         0\n",
       "Single_Epi_Cell_Size      0\n",
       "Bare_Nuclei              16\n",
       "Bland_Chromatin           0\n",
       "Normal_Nucleoli           0\n",
       "Mitoses                   0\n",
       "Class                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e350c3e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    683.000000\n",
       "mean       3.544656\n",
       "std        3.643857\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        6.000000\n",
       "max       10.000000\n",
       "Name: Bare_Nuclei, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bare_Nuclei'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50816811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Bare_Nuclei', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(data=df , x=df['Bare_Nuclei'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed4af1f",
   "metadata": {},
   "source": [
    "We fill NA values with 1 because based on descriptive statistics of column Bare_Nuclei it won't differ that much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "81d122d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bare_Nuclei'] = df['Bare_Nuclei'].fillna(value=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eef4a15f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clump_Thickness          0\n",
       "Cell_Size_Uniformity     0\n",
       "Cell_Shape_Uniformity    0\n",
       "Marginal_Adhesion        0\n",
       "Single_Epi_Cell_Size     0\n",
       "Bare_Nuclei              0\n",
       "Bland_Chromatin          0\n",
       "Normal_Nucleoli          0\n",
       "Mitoses                  0\n",
       "Class                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253df75f",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd79deb0",
   "metadata": {},
   "source": [
    "# Benign vs. Malignant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1d42e73b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign 458\n",
      "Malignant 241\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(data=df , x='Class')\n",
    "B , M = df[\"Class\"].value_counts()\n",
    "print(\"Benign\",B)\n",
    "print(\"Malignant\",M)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d960281",
   "metadata": {},
   "source": [
    "# Clump Thickness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66893b87",
   "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>Clump_Thickness</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>142</td>\n",
       "      <td>46</td>\n",
       "      <td>96</td>\n",
       "      <td>68</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Clump_Thickness   1   2   3   4   5   6   7   8   9   10\n",
       "Class                                                   \n",
       "benign           142  46  96  68  85  16   1   4   0   0\n",
       "malignant          3   4  12  12  45  18  22  42  14  69"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indicating grouping of cancer cells in multilayer\n",
    "df_clump=pd.crosstab(df['Class'],df['Clump_Thickness'])\n",
    "df_clump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f2052fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Clump_Thickness', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(data=df,x='Clump_Thickness',hue='Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f079c2c",
   "metadata": {},
   "source": [
    "# Cell Size Uniformity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7c1c666",
   "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>Cell_Size_Uniformity</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>380</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cell_Size_Uniformity   1   2   3   4   5   6   7   8   9   10\n",
       "Class                                                        \n",
       "benign                380  37  27   9   0   2   1   1   1   0\n",
       "malignant               4   8  25  31  30  25  18  28   5  67"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indicating metastasis to lymph nodes\n",
    "df_cell_size=pd.crosstab(df['Class'],df['Cell_Size_Uniformity'])\n",
    "df_cell_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23b6f0e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cell_Size_Uniformity', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(data=df,x='Cell_Size_Uniformity',hue='Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7cada4",
   "metadata": {},
   "source": [
    "# Cell Shape Uniforimity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a637eb61",
   "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>Cell_Shape_Uniformity</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>351</td>\n",
       "      <td>52</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cell_Shape_Uniformity   1   2   3   4   5   6   7   8   9   10\n",
       "Class                                                         \n",
       "benign                 351  52  33  13   3   3   2   1   0   0\n",
       "malignant                2   7  23  31  31  27  28  27   7  58"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identifying cancerous cells of varying size\n",
    "df_cell_size=pd.crosstab(df['Class'],df['Cell_Shape_Uniformity'])\n",
    "df_cell_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5212ef43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cell_Shape_Uniformity', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.countplot(x=\"Cell_Shape_Uniformity\", data=df , hue='Class' ,palette='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a15c4cf3",
   "metadata": {},
   "source": [
    "# Marginal Adhesion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74c66b13",
   "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>Marginal_Adhesion</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>375</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Marginal_Adhesion   1   2   3   4   5   6   7   8   9   10\n",
       "Class                                                     \n",
       "benign             375  37  31   5   4   4   0   0   1   1\n",
       "malignant           32  21  27  28  19  18  13  25   4  54"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# suggesting loss of adhesion, i.e., a sign of malignancy but the cancerous cells lose this property so this\n",
    "#retention of adhesion is an indication of malignancy\n",
    "marg_adh = pd.crosstab(df['Class'],df['Marginal_Adhesion'])\n",
    "marg_adh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0dcef924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marginal_Adhesion', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(data=df , x='Marginal_Adhesion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f4a54f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marginal_Adhesion', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(x=\"Marginal_Adhesion\", data=df , hue='Class' ,palette='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9174dca7",
   "metadata": {},
   "source": [
    "# Single Epi Cell Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb0501bf",
   "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>Single_Epi_Cell_Size</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>46</td>\n",
       "      <td>363</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Single_Epi_Cell_Size  1    2   3   4   5   6   7   8   9   10\n",
       "Class                                                        \n",
       "benign                46  363  29   7   5   2   3   2   0   1\n",
       "malignant              1   23  43  41  34  39   9  19   2  30"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if the SECS become larger, it may be a malignant cell\n",
    "sing_epi = pd.crosstab(df['Class'],df['Single_Epi_Cell_Size'])\n",
    "sing_epi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b7589d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Single_Epi_Cell_Size', ylabel='count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(data=df , x='Single_Epi_Cell_Size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c8e8217c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Single_Epi_Cell_Size', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(x=\"Single_Epi_Cell_Size\", data=df , hue='Class' ,palette='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf0a8f7c",
   "metadata": {},
   "source": [
    "# Bare Nuclei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dbabe98e",
   "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>Bare_Nuclei</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>401</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Bare_Nuclei  1.0   2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   10.0\n",
       "Class                                                                  \n",
       "benign        401    21    14     6    10     0     1     2     0     3\n",
       "malignant      17     9    14    13    20     4     7    19     9   129"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# without cytoplasm coating, found in benign tumors\n",
    "ba_nu = pd.crosstab(df['Class'],df['Bare_Nuclei'])\n",
    "ba_nu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "097e755f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Bare_Nuclei', ylabel='Count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.histplot(df['Bare_Nuclei'], color='darkred', bins=20 ,legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6d7eb8d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Bare_Nuclei', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAF2CAYAAACh02S2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAxeUlEQVR4nO3dfUBUZd7/8c/A8FSYTw1iZJbmptG9YroVrUK2K6hIGtFd2kpZPrWpaakpEKaZT7GxuZptrVlam5GpmDeiluWm2Jrsrq67VmZiioYDiQLyNMz5/dGv2UXUsMMM4rxff825rnPO9T1Xhh8P15xjMQzDEAAAAICfzKepCwAAAACaO0I1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTrE1dQGM5caJcTieP3AYAAIB7+PhY1Lr15Wftu2RCtdNpEKoBAADQJFj+AQAAAJhEqAYAAABMIlQDAAAAJl0ya6oBAAC8TW2tQydO2OVwVDd1KZcUq9VfrVvb5Ovb8KhMqAYAAGimTpywKzDwMl1+eagsFktTl3NJMAxD5eWndOKEXVde2b7Bx7H8AwAAoJlyOKp1+eVXEKgbkcVi0eWXX3HBd/8J1QAAAM0Ygbrx/ZQ5ZfkHAAAALkhtba3effdtbd68UbW1tXI4anT77X00cuRYPf/8HF13XWcNGza8qcv0KEI1AAAALkh6+jyVlp7Siy8uUXBwsCoqKjRrVqrmz39WPj6+TV1ekyBUAwAAoMGOHTuqzZs3KCsrR5dfHixJCgoK0uTJ0/XPf+7W9u2fuPZdvz5LWVmr5XDU6NSpU/rNbx7S3Xcnqri4SLNnz9DJkyWSpMjI3ho16tFztjcHbl9TPX/+fE2bNk2SlJubq/j4eMXExCgjI8O1z759+5SQkKDY2FilpKTI4XC4uywAAAD8BF98sU/XXdfJFah/0Lbtlbrjjl+5tk+fPq3331+r9PQXtWzZnzVz5ly99NJCSdL776/VVVeF6bXX3tLixX/SkSPfqKys7JztzYFbQ/WOHTu0Zs0aSVJlZaWSk5P10ksvKTs7W3v37tXWrVslSVOmTFFaWpo2btwowzCUmZnpzrIAAADwE1ksPnI6jR/d77LLLtOCBRnKzd2mV19douXLX1NFxWlJ0q23Rmrr1i2aPHmCsrLe09ix4xUcHHzO9ubAbaG6pKREGRkZGjt2rCRpz5496tixozp06CCr1ar4+Hjl5OSooKBAlZWVioiIkCQlJCQoJyfHXWUBAADAhPDwm3To0EGdPl1ep91uP64pUx5XVVWVJOn48UKNGPGACgu/1c9/HlFnGUe3buHKzFynu+5K0LffHtOoUQ/q88/3nbO9OXDbmuq0tDRNmjRJx44dkyQdP35cNpvN1R8SEqLCwsJ67TabTYWFhRc8Xtu2Z/9XjKPKKWuAZ54c6MmxAAAAjh/3kdXq2ewRGtpOsbEDNG/es0pJSdPllwervLxML7wwX61atZLF4iNfX4v27/9crVq11iOPjJIkvf76UkmSxWLo5ZcXyzAMjRv3uPr27auvvz6gQ4e+1tatH561/aabwj16jZLk4+Mjm61Fg/d3S6h+99131b59e0VGRmr16tWSJKfTWeeZf4ZhyGKxnLP9QhUXl531VxE2Wwuld/nkLEc0vsn7+8huL/XIWAAAAE6nUw6H0+PjTpr0lN54Y6lGjnxIvr5W1dRUq0+fO/TII2O0YMFzqq011LPnrVq3bq3uvfdu+fhYFBFxs1q1aq1Dhw4pMfF+PffcMxo6NFF+fv66/vou6tu3n0pLT521vSmu0el01st1Pj6Wc97IdUuozs7Olt1u1+DBg3Xy5EmdPn1aBQUF8vX9zyNW7Ha7QkJCFBoaKrvd7movKipSSEiIO8oCAABAI7BarXrkkTF65JEx9fpSUp5xfZ4/P6NO39SpKa7PL7ywqN6xbdteedb25sAtoXrZsmWuz6tXr9bOnTs1c+ZMxcTE6NChQ7r66qu1fv163XPPPQoLC1NAQIDy8vLUs2dPZWVlKSoqyh1lAQAAAG7hsedUBwQEaN68eRo/fryqqqoUHR2t/v37S5LS09OVmpqqsrIyhYeHKykpyVNlAQAAAKZZDMP48WeiNAOsqQYAAN7m228PKTS0Y1OXcUk629yeb001j6oAAAAATCJUAwAAACYRqgEAAACTCNUAAACASR57+gcAAADcq2VwkPyDGj/eVVc4dLKs4kf3+9vfdum1117RokWvmBpv3rxnNWTIPera9UZT5/EkQjUAAMAlwj/I6pannk3e30cqa/TTntO0aU97brBGQqgGAABAozl5skRPPDFeRUXHdeONN+mJJ57S3/62S0uXviyHw6H27cP01FMpatmylRIT4xUbO1A7d+5QRUWlUlNnqmvXbho3brQefni0br65l15+eZE+/vhDtWzZSm3bXqnevaPUo0dPJSdPVqdOnfXll1+oTZu2evbZebriipZNdt2sqQYAAECjOXbsqCZNmqI33lip06dP6803X9fLLy/S7363SMuW/Vm33HKbliz5g2v/li1b6tVXl2vIkAStWPFanXNt2/YX7dnzD61Ykannn39R+/d/4er76qv9uu++B7RiRaaCg4O1adMGj13j2XCnGgAAAI2me/eb1aHDNZKkmJj+mj37GVksFk2YMFaS5HTW1rmjfOutt0uSOnW6Xlu3flTnXLt2/VV33vlr+fn5yc/PT336RLv6Wrduo5/9rKvr2FOnTrn1un4MoRoAAACNxtfX1/X5h7dd//zn3TV/foYkqaqqShUV//nSo7+/v+vzmS/69vHxOesbs8887mzHehrLPwAAANBo9uz5h7799ls5nU7l5Pyf7rtvmP71r3/qm28OSZJef/1PWrz49w06V69et2rr1i2qqalReXmZcnO3yWKxuLH6n4471QAAAGg0113XSXPnzlJxcZF69uylpKSH1aXLDUpLmy6ns1Y2Wzulpc1q0Lluv7239u7doxEjHtAVV1yhK6+0yd8/wM1X8NNYjKa+V95IiovLzvrrAZuthVseLXM2k/f3kd1e6pGxAAAAvv32kEJDO7q2m/o51Y1t7949Onz4Gw0YMEgOh0NjxozQ9Olpuv76Lm4f+8y5lSQfH4vatg0+6/7cqQYAALhEnCyr8OjzpN3tmms66rXXXtXKlW/JMJzq33+QRwL1T0GoBgAAwEXpiita6oUX/vDjO14E+KIiAAAAYBKhGgAAADCJUA0AAACYRKgGAAAATOKLigAAAJeIVi2s8gsMavTz1lRWqKTU0ejn/W+JifH6wx/+qAMH9uvzz/dp5Mixbh1Pko4eLdAbbyzV9Olpps9FqAYAALhE+AUGaUNiWKOfd8CqAqnUM+/i6N07Wr17R3tkrG+/PaaCgiONci5CNQAAABrF3/62S8uXvyY/Pz8dO3ZUv/xllIKCgvTJJ1tlGIbS01/URx99oJycbFVWVsjPz0/PPPOcrrnmWtc5srPf19//nqeUlGf0t7/t0u9//7x8fX0VHv5z5ed/rUWLXtG4caN1443h2r37HyopOaGJE6coMvKX+vrrr5SR8bwqKip04sR3Gj78IQ0ZkqilS/+ooiK7Dh/+RoWF32rQoMF68MFH9OKL6Tp6tEC/+918PfnkU6aunTXVAAAAaDT//ve/NHnydP3pTyu0enWmWrVqraVLV+j667vogw826S9/2apFi/6oFSsydfvtffTee5lnPY/D4dDs2TOUljZby5b9WVZr3XvBNTUO/fGPyzR+/BN69dUlkqT338/Sgw8+oj/9abkWLnxZixcvdO3/1Vf7lZGxWK+88rrefPMNlZaW6vHHJ+uGG7qZDtQSoRoAAACNqFOnzmrXLlSBgYFq2bKVevW6RZLUrl2oSktP6ZlnZuuDDzbp5ZcXafv2v6ii4uyvPz9w4Cu1atXa9QbFuLi76vTfemuka7zS0lOSpHHjJqq6ulorVizTq68uUUXFadf+N9/cS35+fmrduo2uuOIKlZc37qsnCdUAAABoNGfeUfb19XV9Pn68UGPGjFBZWaluu+12DRgQL8MwznoeHx8fGYbznOP4+/tLkiwWi+scaWnT9Je/fKRrr71Oo0f/9qz7n3lMYyFUAwAAwCM+//zfuvrqDrrvvgfUrduN+stfPpLTWXvWfa+99jqVlpbqwIGvJEmbN+fIYrGc9/yffbZTI0eOVZ8+d+jTT3MlSbW1Zz+/JPn6Ws/bfyH4oiIAAAA84he/uE1fffWlfvObe2UYhiIibtbXXx84675+fn56+ulnNXt2miwWH11zTUcFBASc9/wPPzxKjz46UgEB/urcuYvat79Kx44dPef+1157rcrKSvXss0/r6aefNXVtFqOx7303keLiMjmd9S/FZmuh9C6feKSGyfv7yG73zONmAAAAvv32kEJDO7q2m/Nzqs/kdDr18st/0IgRoxUUFKSVK9+U3W7X+PGTPDL+mXMrST4+FrVtG3zW/blTDQAAcIkoKXV47HnS7ubj46MWLVpq1KgkWa1+at++vaZNe7qpyzont4bqF198URs3bpTFYlFiYqJGjBih6dOnKy8vT0FB3/8raty4cerXr5/27dunlJQUlZeXq1evXpo5c2a9he4AAADwHsOHP6Thwx9q6jIaxG2pdefOnfr000+1bt06ORwODRw4UNHR0dq7d6/efPNNhYSE1Nl/ypQpmj17tiIiIpScnKzMzEwNGzbMXeUBAAAAjcZtT/+45ZZbtHz5clmtVhUXF6u2tlaBgYE6evSokpOTFR8fr4ULF8rpdKqgoECVlZWKiIiQJCUkJCgnJ8ddpQEAAFwyLpGvx11UfsqcuvWRen5+flq4cKHi4uIUGRkph8Oh2267TXPmzFFmZqZ27dqlVatW6fjx47LZbK7jbDabCgsL3VkaAABAs2e1+qu8/BTBuhEZhqHy8lOyWv1/fOf/4vZFyxMmTNCoUaM0duxY7dixQ4sXL3b1DR8+XGvXrlXnzp3rPHfQMIwffQ7hmc71TUxPs9laNHUJAADAS7RqFajDhw/Lbj/S1KVcUoKCAnX99dfJz8+vwce4LVQfOHBA1dXV6tatm4KCghQTE6Ps7Gy1atVKsbGxkr4Pz1arVaGhobLb7a5ji4qK6q25/jHne6SeJ/FIPQAA4EktWtjUgnt6ja6kpFJSZZ228z1Sz23LP44cOaLU1FRVV1erurpaH374oX7xi19ozpw5OnnypGpqavTOO++oX79+CgsLU0BAgPLy8iRJWVlZioqKcldpAAAAQKNy253q6Oho7dmzR0OGDJGvr69iYmI0btw4tW7dWkOHDpXD4VBMTIwGDRokSUpPT1dqaqrKysoUHh6upKQkd5UGAAAANCreqNiIeKMiAADApatJln8AAAAA3oJQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJbg3VL774ogYOHKi4uDgtW7ZMkpSbm6v4+HjFxMQoIyPDte++ffuUkJCg2NhYpaSkyOFwuLM0AAAAoNG4LVTv3LlTn376qdatW6f33ntPK1as0Oeff67k5GS99NJLys7O1t69e7V161ZJ0pQpU5SWlqaNGzfKMAxlZma6qzQAAACgUbktVN9yyy1avny5rFariouLVVtbq1OnTqljx47q0KGDrFar4uPjlZOTo4KCAlVWVioiIkKSlJCQoJycHHeVBgAAADQqty7/8PPz08KFCxUXF6fIyEgdP35cNpvN1R8SEqLCwsJ67TabTYWFhe4sDQAAAGg0VncPMGHCBI0aNUpjx45Vfn6+LBaLq88wDFksFjmdzrO2X4i2bYMbrWYzbLYWTV0CAAAAPMxtofrAgQOqrq5Wt27dFBQUpJiYGOXk5MjX19e1j91uV0hIiEJDQ2W3213tRUVFCgkJuaDxiovL5HQa9do9HXLt9lKPjgcAAADP8PGxnPNGrtuWfxw5ckSpqamqrq5WdXW1PvzwQ91///06ePCgDh06pNraWq1fv15RUVEKCwtTQECA8vLyJElZWVmKiopyV2kAAABAo3Lbnero6Gjt2bNHQ4YMka+vr2JiYhQXF6c2bdpo/PjxqqqqUnR0tPr37y9JSk9PV2pqqsrKyhQeHq6kpCR3lQYAAAA0KothGPXXTDRD51v+kd7lE4/UMHl/H5Z/AAAAXKKaZPkHAAAA4C0I1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYZHXnyRctWqQNGzZIkqKjozV16lRNnz5deXl5CgoKkiSNGzdO/fr10759+5SSkqLy8nL16tVLM2fOlNXq1vIAAACARuG21Jqbm6tt27ZpzZo1slgsGjlypDZv3qy9e/fqzTffVEhISJ39p0yZotmzZysiIkLJycnKzMzUsGHD3FUeAAAA0GjctvzDZrNp2rRp8vf3l5+fnzp37qyjR4/q6NGjSk5OVnx8vBYuXCin06mCggJVVlYqIiJCkpSQkKCcnBx3lQYAAAA0Krfdqe7SpYvrc35+vjZs2KC33npLO3fu1IwZM9SiRQuNGTNGq1atUpcuXWSz2Vz722w2FRYWuqs0AAAAoFG5fdHy/v37NWbMGE2dOlWdOnXS4sWLXX3Dhw/X2rVr1blzZ1ksFle7YRh1thuibdvgRqvZDJutRVOXAAAAAA9za6jOy8vThAkTlJycrLi4OH3xxRfKz89XbGyspO/Ds9VqVWhoqOx2u+u4oqKiemuuf0xxcZmcTqNeu6dDrt1e6tHxAAAA4Bk+PpZz3sh125rqY8eO6bHHHlN6erri4uIkfR+i58yZo5MnT6qmpkbvvPOO+vXrp7CwMAUEBCgvL0+SlJWVpaioKHeVBgAAADQqt92pXrp0qaqqqjRv3jxX2/3336/Ro0dr6NChcjgciomJ0aBBgyRJ6enpSk1NVVlZmcLDw5WUlOSu0gAAAIBGZTEMo/6aiWbofMs/0rt84pEaJu/vw/IPAACAS1STLP8AAAAAvAWhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAExqUKguLCys1/bVV181ejEAAABAc3TeUF1SUqKSkhKNGjVKJ0+edG0XFRVp3LhxnqoRAAAAuKhZz9f55JNPavv27ZKkW2+99T8HWa2KjY11b2UAAABAM3HeUL106VJJ0vTp0zV37lyPFAQAAAA0N+cN1T+YO3euCgoKdPLkSRmG4WoPDw93W2EAAABAc9GgUL1w4UItXbpUbdu2dbVZLBZ9+OGHbisMAAAAaC4aFKrXrl2rTZs2qV27du6uBwAAAGh2GvRIvfbt2xOoAQAAgHNo0J3qyMhILViwQL/61a8UGBjoamdNNQAAANDAUL169WpJUk5OjquNNdUAAADA9xoUqrds2eLuOgAAAIBmq0GhetmyZWdtHzFiRKMWAwAAADRHDQrVX375petzdXW1PvvsM0VGRrqtKAAAAKA5afDLX/5bYWGhUlJS3FIQAAAA0Nw06JF6Z2rXrp0KCgoauxYAAACgWbrgNdWGYWjv3r113q4IAAAAeLMLXlMtff8ymKlTp7qlIAAAAKC5uaA11QUFBXI4HOrYsWODTr5o0SJt2LBBkhQdHa2pU6cqNzdXc+fOVVVVlQYMGKBJkyZJkvbt26eUlBSVl5erV69emjlzpqzWBpUHAAAANKkGrak+dOiQ4uLiNGTIECUkJOjXv/61Dhw4cN5jcnNztW3bNq1Zs0Zr167Vv/71L61fv17Jycl66aWXlJ2drb1792rr1q2SpClTpigtLU0bN26UYRjKzMw0f3UAAACABzQoVM+aNUsjR47UZ599pry8PD366KOaOXPmeY+x2WyaNm2a/P395efnp86dOys/P18dO3ZUhw4dZLVaFR8fr5ycHBUUFKiyslIRERGSpISEhDpvbwQAAAAuZg0K1cXFxbr77rtd2/fcc49OnDhx3mO6dOniCsn5+fnasGGDLBaLbDaba5+QkBAVFhbq+PHjddptNpsKCwsv5DoAAACAJtOgRcu1tbUqKSlRq1atJEnfffddgwfYv3+/xowZo6lTp8rX11f5+fmuPsMwZLFY5HQ6ZbFY6rVfiLZtgy9of3ex2Vo0dQkAAADwsAaF6t/85je67777NGDAAFksFmVnZ+vBBx/80ePy8vI0YcIEJScnKy4uTjt37pTdbnf12+12hYSEKDQ0tE57UVGRQkJCLuhCiovL5HQa9do9HXLt9lKPjgcAAADP8PGxnPNGboOWf0RHR0uSampqdODAARUWFqpfv37nPebYsWN67LHHlJ6erri4OElS9+7ddfDgQR06dEi1tbVav369oqKiFBYWpoCAAOXl5UmSsrKyFBUV1eALBAAAAJpSg+5UT5s2TQ888ICSkpJUVVWlt99+W8nJyXr11VfPeczSpUtVVVWlefPmudruv/9+zZs3T+PHj1dVVZWio6PVv39/SVJ6erpSU1NVVlam8PBwJSUlmbw0AAAAwDMshmHUXzNxhsGDBysrK6tO25AhQ7R27Vp31XXBzrf8I73LJx6pYfL+Piz/AAAAuESZXv5RW1tb52kcRUVFakAWBwAAALxCg5Z/PPTQQxoyZIj69Okji8Wi3NxcXlMOAAAA/H8NCtWJiYm66aab9Omnn8rX11ePPPKIfvazn7m7NgAAAKBZaFColqSuXbuqa9eu7qwFAAAAaJYatKYaAAAAwLkRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACT3Bqqy8rKNGjQIB05ckSSNH36dMXExGjw4MEaPHiwNm/eLEnat2+fEhISFBsbq5SUFDkcDneWBQAAADQqt4Xq3bt3a+jQocrPz3e17d27V2+++aaysrKUlZWlfv36SZKmTJmitLQ0bdy4UYZhKDMz011lAQAAAI3ObaE6MzNTM2bMUEhIiCSpoqJCR48eVXJysuLj47Vw4UI5nU4VFBSosrJSERERkqSEhATl5OS4qywAAACg0VnddeLnnnuuznZRUZFuu+02zZgxQy1atNCYMWO0atUqdenSRTabzbWfzWZTYWGhu8oCAAAAGp3bQvWZOnTooMWLF7u2hw8frrVr16pz586yWCyudsMw6mw3VNu2wY1Sp1k2W4umLgEAAAAe5rFQ/cUXXyg/P1+xsbGSvg/PVqtVoaGhstvtrv2KiopcS0YuRHFxmZxOo167p0Ou3V7q0fEAAADgGT4+lnPeyPXYI/UMw9CcOXN08uRJ1dTU6J133lG/fv0UFhamgIAA5eXlSZKysrIUFRXlqbIAAAAA0zx2p7pr164aPXq0hg4dKofDoZiYGA0aNEiSlJ6ertTUVJWVlSk8PFxJSUmeKgsAAAAwzWIYRv01E83Q+ZZ/pHf5xCM1TN7fh+UfAAAAl6iLYvkHAAAAcKkiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJOsTV0AAAAA8GNatbDKLzDII2PVVFaopNRxQccQqgEAAHDR8wsM0obEMI+MNWBVgVRaekHHsPwDAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwya2huqysTIMGDdKRI0ckSbm5uYqPj1dMTIwyMjJc++3bt08JCQmKjY1VSkqKHA6HO8sCAAAAGpXbQvXu3bs1dOhQ5efnS5IqKyuVnJysl156SdnZ2dq7d6+2bt0qSZoyZYrS0tK0ceNGGYahzMxMd5UFAAAANDq3herMzEzNmDFDISEhkqQ9e/aoY8eO6tChg6xWq+Lj45WTk6OCggJVVlYqIiJCkpSQkKCcnBx3lQUAAAA0Oqu7Tvzcc8/V2T5+/LhsNptrOyQkRIWFhfXabTabCgsL3VUWAAAA0OjcFqrP5HQ6ZbFYXNuGYchisZyz/UK1bRvcKHWaZbO1aOoSAAAAYNKFZjqPherQ0FDZ7XbXtt1uV0hISL32oqIi15KRC1FcXCan06jX7umQa7eXenQ8AAAAb3AxZDofH8s5b+R67JF63bt318GDB3Xo0CHV1tZq/fr1ioqKUlhYmAICApSXlydJysrKUlRUlKfKAgAAAEzz2J3qgIAAzZs3T+PHj1dVVZWio6PVv39/SVJ6erpSU1NVVlam8PBwJSUleaosAAAAwDS3h+otW7a4PkdGRmrdunX19unatatWrVrl7lIAAAAAt+CNigAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAwiVANAAAAmESoBgAAAEwiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATLI2xaDDhw/Xd999J6v1++FnzZql8vJyzZ07V1VVVRowYIAmTZrUFKUBAAAAF8zjodowDOXn5+ujjz5yherKykr1799fK1asUPv27TVmzBht3bpV0dHRni4PAAAAuGAeD9Vff/21JOnhhx9WSUmJ/vd//1c/+9nP1LFjR3Xo0EGSFB8fr5ycHEI1AAAAmgWPr6k+deqUIiMjtXjxYr3++utauXKljh49KpvN5tonJCREhYWFni4NAAAA+Ek8fqe6R48e6tGjh2s7MTFRCxcuVM+ePV1thmHIYrFc0Hnbtg1utBrNsNlaNHUJAAAAMOlCM53HQ/WuXbtUU1OjyMhISd8H6LCwMNntdtc+drtdISEhF3Te4uIyOZ1GvXZPh1y7vdSj4wEAAHiDiyHT+fhYznkj1+PLP0pLS7VgwQJVVVWprKxMa9as0RNPPKGDBw/q0KFDqq2t1fr16xUVFeXp0gAAAICfxON3qvv27avdu3dryJAhcjqdGjZsmHr06KF58+Zp/PjxqqqqUnR0tPr37+/p0gAAAICfpEmeUz1x4kRNnDixTltkZKTWrVvXFOUAAAAApvBGRQAAAMAkQjUAAABgEqEaAAAAMIlQDQAAAJhEqAYAAABMIlQDAAAAJhGqAQAAAJMI1QAAAIBJhGoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgEAAACTCNUAAACASYRqAAAAwCRrUxcAz2kZHCT/IM/8J6+ucOhkWYVHxgIAAGhqhGov4h9kVXqXTzwy1uT9faQyjwwFNDutWljlFxjkkbFqKitUUurwyFgA4M0I1QDgYX6BQdqQGOaRsQasKpBKSz0yFtBY+IcnmiNCdSOqra6UzdbC7ePwAwAAcCnjH55ojgjVjcjXP9AjPwT4AQAAAHBxIVTDK3nqS5u1VZXyDQh0+zgSv8EAAKApEarhlTz1pc3J+/vwK8z/jzWSAIBLGaEagEewRhIAcCnj5S8AAACASdyphlvwJBQ0R558QRIANARL55oP/vaAW/AkFDRHnlxrDwANwdK55oNQDQDARYA7kkDzdlGF6vfff19LliyRw+HQgw8+qAceeKCpSwIueSx5AC4O3JEEmreL5m/SwsJCZWRkaPXq1fL399f999+vW2+9Vddff31TlwZc0ljygKbE3VkAl4qLJlTn5ubqtttuU6tWrSRJsbGxysnJ0bhx45q2MACA23B3Fmje+G3nf1w0s3D8+HHZbDbXdkhIiPbs2dPg4318LOfsuyIswFRtFyLIdrVHxjnf9Z4Pc/EfnpoLT82DxFz8N+biP37qXHgKc/EfF/NctLg8UP6BnosNF/NceNrFPBf+QVa9csdON1VT1+iPb2nyuTjf/FgMwzDcWVBDLVmyRFVVVZo4caIkKTMzU3v37tWsWbOatjAAAADgR1w0L38JDQ2V3W53bdvtdoWEhDRhRQAAAEDDXDSh+vbbb9eOHTv03XffqaKiQps2bVJUVFRTlwUAAAD8qItmTXW7du00adIkJSUlqaamRomJifr5z3/e1GUBAAAAP+qiWVMNAAAANFcXzfIPAAAAoLkiVAMAAAAmEaoBAAAAkwjVAAAAgEmEagAAAMAkQvVPUFZWpkGDBunIkSP1+vbt26eEhATFxsYqJSVFDoejCSr0jEWLFikuLk5xcXFasGBBvX5vmosXX3xRAwcOVFxcnJYtW1av35vm4gfz58/XtGnT6rV701wMHz5ccXFxGjx4sAYPHqzdu3fX6femudiyZYsSEhI0YMAAzZ49u16/t8zFu+++6/rzMHjwYPXs2bPem4O9ZS4kKSsry/X3yPz58+v1e9NcvPLKK4qNjVV8fLyWLFlSr98b5uLMfJWbm6v4+HjFxMQoIyPjrMccPXpUDzzwgPr3769HH31U5eXlniy5LgMX5B//+IcxaNAgIzw83Dh8+HC9/ri4OOPvf/+7YRiGMX36dOOtt97ycIWesX37duO+++4zqqqqjOrqaiMpKcnYtGlTnX28ZS7++te/Gvfff79RU1NjVFRUGH379jUOHDhQZx9vmYsf5ObmGrfeeqvx1FNP1evzlrlwOp1G7969jZqamnPu4y1z8c033xi9e/c2jh07ZlRXVxtDhw41Pv744zr7eMtc/Lcvv/zS6Nevn1FcXFyn3Vvm4vTp08YvfvELo7i42KipqTESExON7du319nHW+Zi+/btxqBBg4zS0lLD4XAYY8aMMTZu3Fhnn0t9Ls7MVxUVFUZ0dLTxzTffGDU1NcbDDz9c7+eGYRjG6NGjjfXr1xuGYRiLFi0yFixY4OnSXbhTfYEyMzM1Y8aMs75CvaCgQJWVlYqIiJAkJSQkKCcnx8MVeobNZtO0adPk7+8vPz8/de7cWUePHnX1e9Nc3HLLLVq+fLmsVquKi4tVW1uryy67zNXvTXMhSSUlJcrIyNDYsWPr9XnTXHz99deSpIcfflh33XWX3nzzzTr93jQXmzdv1sCBAxUaGio/Pz9lZGSoe/furn5vmov/9swzz2jSpElq06aNq82b5qK2tlZOp1MVFRVyOBxyOBwKCAhw9XvTXPz73/9W7969FRwcLF9fX/Xp00cffPCBq98b5uLMfLVnzx517NhRHTp0kNVqVXx8fL1rrqmp0WeffabY2FhJTT8vhOoL9Nxzz6lXr15n7Tt+/LhsNptr22azqbCw0FOleVSXLl1c/3Pn5+drw4YNio6OdvV701xIkp+fnxYuXKi4uDhFRkaqXbt2rj5vm4u0tDRNmjRJV1xxRb0+b5qLU6dOKTIyUosXL9brr7+ulStXavv27a5+b5qLQ4cOqba2VmPHjtXgwYP15z//WS1btnT1e9Nc/CA3N1eVlZUaMGBAnXZvmovg4GA9/vjjGjBggKKjoxUWFqabb77Z1e9NcxEeHq5t27appKREVVVV2rJli4qKilz93jAXZ+arM685JCSk3jWfOHFCwcHBslq/f0F4U88LoboROZ1OWSwW17ZhGHW2L0X79+/Xww8/rKlTp+raa691tXvjXEyYMEE7duzQsWPHlJmZ6Wr3prl499131b59e0VGRp6135vmokePHlqwYIFatGihNm3aKDExUVu3bnX1e9Nc1NbWaseOHZozZ47eeecd7dmzR2vWrHH1e9Nc/GDlypUaMWJEvXZvmovPP/9c7733nj766CN98skn8vHx0dKlS1393jQXkZGRSkhI0PDhwzVy5Ej17NlTfn5+rn5vmosfNOSaz9bWlPNCqG5EoaGhstvtru2ioqKzLhO5VOTl5emhhx7Sk08+qbvvvrtOnzfNxYEDB7Rv3z5JUlBQkGJiYvTFF1+4+r1pLrKzs7V9+3YNHjxYCxcu1JYtWzRnzhxXvzfNxa5du7Rjxw7XtmEYrrspknfNxZVXXqnIyEi1adNGgYGB+vWvf609e/a4+r1pLiSpurpan332me688856fd40F9u2bVNkZKTatm0rf39/JSQkaOfOna5+b5qLsrIyxcTE6P3339eKFSvk7++vDh06uPq9aS5+cOY12+32etfcpk0blZaWqra29pz7eBKhuhGFhYUpICBAeXl5kr7/VnNUVFQTV+Uex44d02OPPab09HTFxcXV6/emuThy5IhSU1NVXV2t6upqffjhh+rZs6er35vmYtmyZVq/fr2ysrI0YcIE3XnnnUpOTnb1e9NclJaWasGCBaqqqlJZWZnWrFmjfv36ufq9aS769u2rbdu26dSpU6qtrdUnn3yi8PBwV783zYUkffHFF7r22mvrfPfiB940F127dlVubq5Onz4twzC0ZcsW/c///I+r35vm4siRI/rtb38rh8Oh0tJSrVq1qs7SIG+aix90795dBw8edC0fW79+fb1r9vPzU69evZSdnS1JWrt2bZPOC6G6EYwaNUr//Oc/JUnp6emaO3eu+vfvr9OnTyspKamJq3OPpUuXqqqqSvPmzXM9Gurtt9/2yrmIjo7WHXfcoSFDhuiee+5Rjx49FBcX55VzcS7eOBd9+/ZVdHS068/FD382vHEuunfvrpEjR2rYsGEaOHCgrrrqKt1zzz1eOReSdPjwYYWGhtZp88a56N27t+Li4pSQkKC77rpLDodDo0eP9sq56Nq1q2JiYnTXXXcpMTFRDz74oHr27OmVc/GDgIAAzZs3T+PHj9fAgQPVqVMn9e/fX5KUkpKiDz/8UJI0Y8YMZWZmauDAgdq1a5cmTpzYZDVbDMMwmmx0AAAA4BLAnWoAAADAJEI1AAAAYBKhGgAAADCJUA0AAACYRKgGAAAATCJUAwAAACYRqgGgiRw5ckTdunVzPes9Pj5e9957r+sFD+4e+4YbbtC7775bp33p0qWaNm3aTz7vnXfe6Xqu7rm8/fbbeuWVV37yGABwMbL++C4AAHcJDAxUVlaWazs7O1vTp0/Xpk2b3D62j4+P5s+fr549e6pTp05uH+8HQ4cO9dhYAOAphGoAuIiUlJTIZrPJ6XRqzpw52r17t8rLy2UYhmbPnq2ePXtq2rRpKikp0eHDh3XHHXfo8ccfV3p6uj777DPV1tbqxhtvVGpqqoKDg887VmBgoEaMGKHJkydr5cqV8vf3r9M/bdo0denSRY888ki97YMHDyotLU3fffedfHx89Oijj2rgwIF1jt+yZYuWLFmimpoaBQYG6qmnnlKPHj30hz/8QSdOnFBaWlrjTh4ANCFCNQA0ocrKSg0ePFiSdOrUKdntdi1evFi7d+/W8ePH9c4778jHx0evvPKKXn31VfXs2dN13P/93/9JkhYtWiRfX1+tXr1aFotFL7zwgtLT0/XMM8/86PiPPvqoduzYoYyMDD311FMNrvuJJ55QYmKiHnjgAR07dkzDhw9XVFSUqz8/P18ZGRlavny5Wrdurf3792vEiBEeuQMPAE2BUA0ATejM5R+5ubl67LHHtG7dOk2cOFErV67U4cOH9de//lWXX365a78fwrUkffzxxyotLVVubq4kqaamRm3btm3Q+D4+Pnr++ec1ZMgQ9e7du0HHlJSU6PPPP9e9994rSWrfvr0++OCDOvts375dx48f10MPPeRqs1gs+uabbxo0BgA0N4RqALiI3H777brmmmu0a9cuvfTSSxoxYoR+9atfqVOnTlq3bp1rv8suu8z12el0Kjk5WdHR0ZKk8vJyVVVVNXjM9u3ba+bMmXrqqac0ZMgQV7vFYpFhGK7tmpoaSZLVanX1/+Drr7/WVVddVaemyMhI/f73v3e1HTt2TCEhIdq8eXODawOA5oKnfwDAReTgwYMqKChQdna2+vbtq2HDhummm27SBx98oNra2rMe07t3b7311luqrq6W0+nU008/rRdeeOGCxu3fv7+ioqL0xhtvuNpat26tvXv3SpIKCwu1c+dOSVJwcLDCw8O1du1aSd+H5aFDh6q0tNR1bGRkpLZv364DBw5IkrZu3aq77rpLlZWVF1QXADQX3KkGgCb032uqpe/v8M6aNUs33HCDnnzyScXHx8vhcOiXv/ylNm3aJKfTWe8cv/3tbzV//nzdfffdqq2tVbdu3X7SY/FSU1PrPM5v+PDhmjx5smJjY3X11Vfrtttuc/X97ne/08yZM7VixQpZLBY999xzstlsrv7rr79es2bN0hNPPCHDMGS1WrVkyZI6S1gA4FJiMf77d3sAAAAALhh3qgHgEjVx4kQdPHjwrH0ZGRkefTY1AFzquFMNAAAAmMQXFQEAAACTCNUAAACASYRqAAAAwCRCNQAAAGASoRoAAAAw6f8BD+1XzxZ+sncAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(x=\"Bare_Nuclei\", data=df , hue='Class' ,palette='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab963e3",
   "metadata": {},
   "source": [
    "# Bland Chromatin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bef98bbe",
   "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>Bland_Chromatin</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>150</td>\n",
       "      <td>159</td>\n",
       "      <td>129</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Bland_Chromatin   1    2    3   4   5   6   7   8   9   10\n",
       "Class                                                     \n",
       "benign           150  159  129   8   4   1   7   0   0   0\n",
       "malignant          2    7   36  32  30   9  66  28  11  20"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# usually found in benign cells\n",
    "bl_ch = pd.crosstab(df['Class'],df['Bland_Chromatin'])\n",
    "bl_ch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "90cd13f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='Bland_Chromatin'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEJCAYAAACdePCvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAaxElEQVR4nO3dfVRUdeLH8c8MT4oIKGK11ioYaibWZuKilqZuLRWlrubDUctKytYSezBFTV0qpfRQu61bnPXnbi75S0pRLE3UMh/aPFmrPSjaJgppRCjPKDAzvz/8OUlpDMqdC9736xzPYe7Mvd8PAvO5D3PvtblcLpcAAJZjNzsAAMAcFAAAWBQFAAAWRQEAgEVRAABgURQAAFgUBQAAFuVrdoCGOnGiQk4npy4AgCfsdpvatGl1zueaXQE4nS4KAAAaAbuAAMCiKAAAsCgKAAAsytACKC8v15133qn8/HxJ0s6dOxUfH69bb71VqampRg4NAKiHYQWwZ88ejRkzRrm5uZKkkydPKikpSUuWLNG7776rL774Qlu3bjVqeABAPQwrgJUrV2ru3Llq3769JGnv3r3q2LGjrrrqKvn6+io+Pl4bNmwwangAQD0M+xjoc889V+fx999/r/DwcPfj9u3bq6CgwKjhAVyAHTs+1Pbt5m6Zl5QUS5JCQkJNzSFJ/fsPUL9+N5sdwzBeOw/A6XTKZrO5H7tcrjqPPRUWFtSYsQCcJTi4pfz8fEzNUFpaIklq1y7M1BzS6f+P8PDWZscwjNcK4PLLL1dhYaH7cWFhoXv3UEMUFZVzIhhgkOjo3oqO7m1qhpSUZEnS448nmZrjjMLCMrMjXBS73XbeFWevfQz0uuuu06FDh3T48GE5HA6tW7dON9986W5aAUBT57UtgICAAC1cuFCPPvqoTp06pQEDBuj3v/+9t4YHAPyE4QWwZcsW99exsbFau3at0UMCADzAmcAAYFEUAABYFAUAABZFAQCARVEAAGBRFAAAWBQFAAAWRQEAgEVRAABgURQAAFgUBQAAFkUBAIBFUQAAYFEUAABYFAUAABZFAQCARVEAAGBRFAAAWBQFAAAWRQEAgEVRAABgURQAAFgUBQAAFkUBAIBFUQAAYFEUAABYFAUAABZFAQCARVEAAGBRFAAAWBQFAAAWRQEAgEVRAABgURQAAFgUBQAAFmVKAaxZs0Z33HGH7rjjDqWkpJgRAQAsz+sFUFVVpeeee07Lly/XmjVr9Mknn2jnzp3ejgEAluf1AnA4HHI6naqqqlJtba1qa2sVEBDg7RgAYHm+3h4wKChIU6dOVVxcnFq2bKnevXvrhhtu8HYMALA8rxfA/v379fbbb+v9999X69at9eSTT2rp0qV68MEHPZo/LCzI4IQAzOTn5yNJCg9vbXKSS5/XC2D79u2KjY1VWFiYJGn48OF64403PC6AoqJyOZ0uIyMCMFFNjUOSVFhYZnKSS4PdbjvvirPXjwF069ZNO3fuVGVlpVwul7Zs2aLo6GhvxwAAy/P6FkD//v311Vdfafjw4fLz81N0dLQSEhK8HQMALM/rBSBJCQkJvOkDgMk4ExgALIoCAACLogAAwKIoAACwKAoAACyKAgAAi/L4Y6BVVVXasGGDSkpK5HL9eCbuxIkTDQkGADCWxwUwffp0ffvtt+rSpYtsNpuRmQAAXuBxAeTk5Ojdd9+Vr68p544BABqZx8cALr/8ciNzAAC8zOPV+S5dumjChAm66aab1KJFC/d0jgEAQPPkcQFUVFSoY8eOOnLkiJF5AABe4nEBLFiwwMgcAAAvq7cApk6dqpdfflnx8fHnfD4rK6vRQ8F4GRn/q/Xr1yo+fqiGDbvH7DgATFBvAUyaNEmSNGfOHMPDwHvWr18rScrKyqQAAIuq91NAPXr0kCRlZmYqJiamzr9//etfhgdE48vI+N86j1evXmlSEgBmsrnOPq33HObOnauCggLt3r1bvXr1ck+vra1VXl6e3nvvPcNDno17Al+8++8f+7Np//M/b5iQBGcsWrRAhw791+wYTcKpUyclSQEBLep5pTVERHTWk0/OvOD5f+mewPXuAhoxYoQOHjyonJwc3Xbbbe7pPj4+uv766y84FIAfHT/+g05WVcrfh7Ps7Tq9guesrjI5ifmqHS4dP/6DYcuvtwCio6MVHR2tvn37cjIYYJCQkFAFVP2giT3bmh0FTciyvcfVIiTUsOV7/DHQY8eOaf78+aqsrJTL5ZLT6VR+fr4++OADw8LBGHFxd7kPAktSfPxQ88IAMI3Hl4KYPXu2fvOb36i8vFzx8fEKCgrSrbfeamQ2GGTkyNF1HvMpIMCaPC4Am82mhIQExcTEKDIyUi+99JJ27NhhZDYYKC7uLkms/QNW5vEuoFatWkmSfv3rX+vgwYPq1auX7HbuJ9NcjRw5+mdbAgCsxeMC6NmzpxITEzV16lQ99NBDys3N5dLQANCMebwKn5SUpPvuu08RERFKSkqS0+nU4sWLjcwGADBQg44BdOjQQV9//bWuvPJK3XPPPXI6nUZmAwAYqEFXA01PT1dQ0I9nlNlsNn300UeGBAMAGMvjAsjOzta2bdvUpk0bI/MAALzE411AnTp1UnBwsJFZAABe5PEWwPjx4zVu3Dj16dOnzqd/pkyZYkgwAICxPC6AtLQ0BQUFqayszMg8AAAv8bgAqqqqtGLFCiOzAAC8yONjABEREdq/f7+RWQAAXtSgq4GOGDFCHTp0kL+/v3s69wQGgObJ4wJ4/PHHjcwBAPAyj3cBxcTEKCAgQLt27XJfBTQmJuaCBt2yZYuGDx+uuLg4Pfvssxe0DADAxfG4ADIzM/XYY4+ppKREFRUVeuKJJ7RyZcNvJp6Xl6e5c+dqyZIlWrt2rb766itt3bq1wcsBAFwcj3cB/eMf/1BGRobat28vSZo0aZIeeOAB3XNPw24mkp2drdtvv919e8nU1FQFBAQ0aBkAgIvncQE4nU73m78kXXbZZRd0P4DDhw/Lz89PDz/8sI4dO6aBAwcqMTGxwctprnbs+FDbt5u/xVNSUizp9L1ozdS//wD163ezqRkAq/K4AEJDQ7Vp0yYNGTJEkrRp0yaFhIQ0eECHw6FPPvlEy5cvV2BgoCZPnqzVq1dr+PDhHs0fFhZU/4uasODglvLz8zE7hkpLSyRJ7dqFmZojOLilwsNbm5qhKfDz89FJs0OgSfLz8zHsb8TjAnjmmWc0efJkJScny2azydfXV3/9618bPGC7du0UGxurtm3bSpKGDBmivXv3elwARUXlcjpdDR63qYiO7q3o6N5mx1BKSrIk6fHHk0xOIhUWcnZ5TY3D7AhoompqHBf1N2K328674uxxAeTm5mrDhg3Kzc2Vw+FQZGTkBd0R7JZbbtHTTz+t0tJStWrVStu2bdPgwYMbvBwAwMXx+B08NTVVQ4YMUefOnS9qwOuuu04PPvigxo4dq5qaGvXr109/+MMfLmqZAICG87gAunTpor/97W+68cYbFRgY6J5+7bXXNnjQESNGaMSIEQ2eDwDQeDwugD179mjPnj3KyMhwT7PZbNq8ebMhwQAAxvK4ALZs2WJkDgCAl3lcAD/88IMyMjJUVFRUZ/rs2bMbPRQAwHgeF8C0adMUHBysbt26yWazGZkJAOAFDdoCWL58uZFZAABe5PG1HMLDw1VcXGxgFACAN9W7BXDmcs0+Pj4aOXKk+vbtKz8/P/fzHAMAgOap3gIIDQ2VJPXq1Uu9evUyOg8AwEvqLYApU6ZIkmpqatxr/oWFhQoPDzc2GQDAUPUeAygrK9O4ceP03nvvuafNmjVLEyZMUHl5uaHhAADGqbcAXnzxRXXp0kW/+93v3NNeeeUVRUREaNGiRYaGAwAYp95dQJ9++qnWrFkjH58fr2Hv7++vWbNmaejQoUZmAwAYqN4tAD8/vzpv/mf4+/tzK0cAaMbqLYCAgAAdP378Z9OLiorkcjXfG7MAgNXVWwCjRo3SY489pry8PPe0I0eOKDExkev4A0AzVu8xgGHDhqmgoEB33323WrZsKafTqdraWk2aNEnjx4/3RkYAgAE8uhbQww8/rHvvvVfffPON7Ha7OnfuLH9/f/fzu3btUkxMjGEhAQCNz+NrAbVs2VLXXnutrrnmmjpv/pK0YMGCRg8GADCWxwXwSzgYDADNT6MUAPcHAIDmp1EKAADQ/FAAAGBRHAMAAItqlALo06dPYywGAOBF9Z4HMHPmzF98fsGCBfW+BgDQ9NS7BRAVFaWoqCiVlZUpJydHXbt2Vffu3ZWbmyuHw+GNjAAAA9S7BXD//fdLkrKzs5Wenq6WLVtKku655x5NmDDB2HQAAMN4fAygqKiozhnANptNJ06cMCQUAMB4Hl0LSJJiY2P14IMP6s4775TL5dKaNWs0aNAgI7MBAAzkcQHMmTNH6enpys7OliTFxcVp9OjRhgUDABjL4wLw9fXVmDFjNHToUPfn/ktLSxUaGmpUNgCAgTwugBUrVmjBggWqqamRdPrkL5vNpn379hkWDgBgHI8LYOnSpVqxYoWuvfZaI/MAALzE408BtWvXjjd/ALiEeFwA/fv31xtvvKGCggIVFxe7/wEAmiePdwGlpaWpurpaf/rTn9zTLvYYQEpKik6cOKGFCxde8DIAABfG4wLYu3dvow780UcfafXq1Ro4cGCjLhcA4BmPC6C6ulpbt25VRUWFJMnhcOjIkSOaNm1agwctLi5WamqqHn74Ye3fv7/B8wOXou/Ka7Vs73GzY5iuvNopSQry53Yl35XXqpOBy/e4AKZNm6a8vDwVFhaqe/fu2rNnj2JiYi5o0GeeeUbTpk3TsWPHGjxvWFjQBY2Juvz8fCRJ4eGtTU4CSeraNcr9M7G6wm++kSRdcVWkyUnM11pSZGSkYX+nHhfAvn37tHHjRs2bN08TJ06U0+nUvHnzGjxgRkaGrrjiCsXGxmrVqlUNnr+oqFxOJzeguVg1Naev5FpYWGZyEkjS0KGcVX9GSkqyJOnxx5NMTtJ0XMzfqd1uO++Ks8cF0L59e/n6+qpTp046cOCA4uLiVFbW8FDvvvuuCgsLdffdd6ukpESVlZV6/vnnlZTEDxsAvMnjAggMDFRWVpa6deumlStXKjIyUpWVlQ0ecNmyZe6vV61apV27dvHmDwAm8Pgoy5w5c7Rv3z7169dPdrtd48aNc98rAADQ/Hi8BRAREaHp06dLkl566aVGGXz48OEaPnx4oywLANAw9RZAfHz8Lz6flZXVaGEAAN5TbwHMmTOnzuP8/Hy1aNFC5eXl6tSpk1G5AAAGq7cAfH19NXPmTLVv314JCQlKTk5Wx44ddfToUSUnJ3sjIwDAAPUWQEpKihITE3XixAk98sgjSktLU2xsrA4dOqTExETddttt3sgJAGhk9RZAZWWl4uLiJEl///vfFRsbK+n0QWGbzWZsOgCAYer9GKiPz4+npwcHB9d5jgIAgOar3gI4+02eN3wAuHTUuwsoJydHN9xwgyTp5MmT7q9dLpeqq6uNTQcAMEy9BZCdne2NHAAAL6u3ADp06OCNHAAAL+OOCwBgURQAAFgUBQAAFkUBAIBFUQAAYFEe3w/gUvDGG68rL++w2TGahCNHTv8/nLn/qtVddVVHjR07wewYgFdZqgDy8g4r5+DX8mkRanYU0zkdpy/x8XXeDyYnMZ/jZLHZEQBTWKoAJMmnRagCOw42OwaakMrDm82OAJiCYwAAYFEUAABYFAUAABZFAQCARVEAAGBRFAAAWBQFAAAWRQEAgEVRAABgURQAAFgUBQAAFkUBAIBFUQAAYFEUAABYFAUAABZFAQCARVEAAGBRptwR7JVXXtH69eslSQMGDND06dPNiAEAlub1LYCdO3dq+/btWr16tTIzM/Xll18qOzvb2zEAwPK8vgUQHh6uGTNmyN/fX5LUuXNnHT161Ctjl5QUy3GymHvAog7HyWKVlFju9tiA9wsgKirK/XVubq7Wr1+vFStWeDx/WFjQBY/t48MhD5ybj49d4eGtzY4BSX5+PpLEz8MLTFvtOXjwoB566CFNnz5dnTp18ni+oqJyOZ2uCxozKChYPi2qFdhx8AXNj0tT5eHNCgoKVmFhmdlRIKmmxiFJ/Dwaid1uO++KsymrxLt379Z9992nJ554QsOGDTMjAgBYnte3AI4dO6Y//vGPSk1NVWxsrLeHBwD8P68XwNKlS3Xq1CktXLjQPW306NEaM2aMt6MAgKV5vQBmz56t2bNne3tYAMBP8LEYALAoCgAALIoCAACLogAAwKIoAACwKAoAACyKAgAAi6IAAMCiKAAAsCgKAAAsigIAAIuiAADAoigAALAoCgAALIoCAACLMu2ewGZxnCxW5eHNpo3vrD0pV+1J08Zvamy+LWT3bWFqBsfJYkntTM3QVOzY8aG2b99qaoYjRw5LklJSkk3NIUn9+w9Qv343mx3DMJYqgKuu6mh2BJWUFKukxGF2jCYjJCRIISGhJqdo1yR+N3BaSEiI2REsw+ZyuVxmh2iIoqJyOZ3NKjIAmMZutyksLOjcz3k5CwCgiaAAAMCiKAAAsCgKAAAsigIAAIuiAADAoigAALCoZncimN1uMzsCADQbv/Se2exOBAMANA52AQGARVEAAGBRFAAAWBQFAAAWRQEAgEVRAABgURQAAFgUBQAAFkUBAIBFUQDN2Mcff6zx48df9HJmzZqlzz//vBESAZ4ZNGiQ8vPztXnzZr388steGTMvL09JSUleGau5aHbXAkLje+6558yOAIsaPHiwBg8e7JWxjh49qry8PK+M1VxQAM3ciRMn9MADD+j7779Xz549NXfuXP373//Wn//8Z9XW1urKK69UcnKy2rRpo0GDBumuu+7S9u3bVVVVpZSUFPXo0UPjx4/XlClT1KdPHy1evFjvvfee2rRpo/DwcA0aNEgxMTGaMmWKoqKitG/fPoWFhenll19WaGio2d8+TPTxxx/r1VdflZ+fn/Lz8zVo0CAFBgZq06ZNkqS0tDRt2LBBa9asUVVVlfz8/LR48WJFRka6l7Fq1Srt2rVLCxcu1Mcff6xnn31WPj4+uv766/Xf//5Xy5cv1/jx4xUdHa3du3fr+PHjmj17tgYMGKADBw4oOTlZlZWVOn78uBISEjRmzBj95S9/UUFBgQ4fPqxvv/1WI0eO1OTJk/Xss88qPz9f8+fP19y5c836b2tS2AXUzOXn52vOnDlau3atKioqlJaWpsWLF2vp0qXKzMxU//79tWjRIvfrQ0ND9dZbb2n06NF67bXX6ixry5Yt2r17t9atW6e0tDR99dVX7uf279+viRMnat26dQoODlZWVpbXvkc0XXv27NH8+fP19ttvKz09XW3bttWqVavUtWtXvfPOO9q0aZOWL1+udevWaeDAgUpPTz/ncmpqajR9+nS9+OKLyszMlK+v78+ef/PNNzVz5kz3LqOMjAw98sgjevvtt/X666/rhRdecL8+JydHS5cuVUZGhtLS0lRaWqrZs2erR48evPmfhS2AZu7GG29Up06dJEnx8fGaMWOGbDabJkyYIElyOp0KCQlxv/6mm26SJEVFRWnjxo11lrVz507FxcXJ399f/v7+GjJkiPu5sLAwde/e3T1vSUmJkd8WmokuXbroiiuukCS1adNGsbGxkqRf/epXKi0t1eLFi/XOO+8oNzdX27Zt0zXXXHPO5Rw4cEBhYWHq1q2bJGnEiBF1dk2e/XtbXFwsSZoxY4a2bdum1157TQcOHFBlZaX79X369JG/v7/CwsIUGhqqsrKyRv/eLwUUQDN39prSmSt733DDDXr11VclSadOnVJFRYX7NQEBAZIkm+3n1wi32+1yOp3nHOfMfGfm5SrikCQ/P786j318fNxfHzt2TKNGjdK4ceN08803q127dtq3b985l+Pj43Pe3z3p3L+3iYmJCg4O1i233KLbb79d69at+9nrz8zD7+u5sQuomdu9e7eOHj0qp9OpzMxM3XvvvfrPf/6jQ4cOSZKWLFlSZ9P4l/Tt21cbN25UdXW1ysvL9cEHH5yzKABPfP755+rYsaPuu+8+RUdHa9OmTXI4HOd8bWRkpEpLS5WTkyNJHu1i3LFjhx577DENGTJEH374oSSdd/nS6ZKpra29gO/k0sUWQDN39dVXKykpSYWFhfrtb3+ryZMnq3v37kpMTJTT6dRll12mF1980aNlDRw4UJ999pmGDRumkJAQtW/fvs6aFNAQ/fv31/79+3X77bfL5XKpd+/eOnjw4Dlf6+/vrxdeeEFPP/207Ha7IiIi1KJFi19c/qOPPqqxY8cqICBA3bp1U4cOHZSfn3/e13fu3FllZWV66qmnPP6buNRxRzC4ffbZZ8rNzdWwYcNUU1OjUaNG6fnnn3fvlwWM4nQ6tWjRIk2ZMkWBgYFatmyZCgoKNGPGDLOjXdLYAoBbRESEXnnlFS1btkwul0tDhw7lzR9eYbfbFRoaqhEjRsjPz08dOnTg/BQvYAsAACyKg8AAYFEUAABYFAUAABbFQWDgJxwOh15//XVlZWXJ4XCopqZGt9xyi6ZOnapnnnlGUVFReuCBB8yOCVw0CgD4iXnz5qmkpET//Oc/1bp1a1VWVurJJ5/UrFmz6pzpCjR3FABwlvz8fGVlZWn79u0KCgqSJAUGBmr+/Pn69NNP9f7777tf+9Zbb+nNN99UTU2NSkpKNGnSJI0dO1aFhYV6+umndeLECUnSgAEDlJiYeN7pgFk4BgCc5csvv9TVV1/tfvM/Izw8XLfddpv7cUVFhftKk5mZmUpNTXWfXbpy5UpdeeWVWr16tdLT03X48GGVlZWddzpgFrYAgLP80gXxztaqVSu9+uqr2rp1q3Jzc7V//3731ShvuukmJSQk6NixY+rbt6+eeOIJtW7d+rzTAbOwBQCcpWfPnvrmm29UXl5eZ3pBQYESEhJ08uRJSdJ3332noUOH6ttvv1WvXr3q7Mrp2bOnNm/erFGjRrlvSPLFF1+cdzpgFrYAgLNcdtllio+PV1JSkp5//nkFBQWpvLxc8+bNU2hoqOz20+tMX3zxhdq2batHHnlEktyX33Y4HEpNTZXL5dJTTz2lwYMHKycnRwcPHtSGDRvOOb1Hjx6mfb+wNi4FAfxEbW2tlixZoo0bN8rHx0fV1dUaMmSIHn30UffHQMeOHatp06bp0KFDstlsiomJUXZ2ttLT09W6dWvNmDFDBQUF8vf3V9euXTV//nyVlJScc7q/v7/Z3zIsigIAAIviGAAAWBQFAAAWRQEAgEVRAABgURQAAFgUBQAAFkUBAIBFUQAAYFH/B1eGqYKmVZXXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df,x='Class',y='Bland_Chromatin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8f08c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Bland_Chromatin', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(x=\"Bland_Chromatin\", data=df , hue='Class' ,palette='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c097c868",
   "metadata": {},
   "source": [
    "# Normal Nucleoli\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "edf06b51",
   "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>Normal_Nucleoli</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>402</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Normal_Nucleoli   1   2   3   4   5   6   7   8   9   10\n",
       "Class                                                   \n",
       "benign           402  30  12   1   2   4   2   4   1   0\n",
       "malignant         41   6  32  17  17  18  14  20  15  61"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generally very small in benign cell\n",
    "no_nu = pd.crosstab(df['Class'],df['Normal_Nucleoli'])\n",
    "no_nu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "688a9883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='Normal_Nucleoli'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEJCAYAAACdePCvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcV0lEQVR4nO3dfVQVdeLH8c/lAq6GCiJmmg9ZmrY+tG1FPhRmbpYulCyb6WaPis9lW6apq5Jmkno8lnqU9HCS1VJTSK1cU8tEStMetFLztysKYsgqoCQq9+H3R8e7UhoXY+6I3/frHM9hhntnPrcu9zPfmTszDq/X6xUAwDhBdgcAANiDAgAAQ1EAAGAoCgAADEUBAIChKAAAMBQFAACGCrY7QGUVFv4oj4dTFwDAH0FBDkVEXHXB31W7AvB4vBQAAFQBdgEBgKEoAAAwFAUAAIaytABKSkr05z//Wbm5uZKkrKwsxcbG6t5779WsWbOsXDUAoAKWFcDXX3+tvn37Kjs7W5J0+vRpjR07VvPmzdP777+vb775Rps3b7Zq9QCAClhWAMuXL9fEiRPVoEEDSdKuXbvUrFkzNWnSRMHBwYqNjdW6deusWj0AoAKWfQ305ZdfLjd99OhRRUVF+aYbNGig/Px8q1YPVCtLly5WTs5Bu2OouLhIxcXFdse4bNStW1d164bbmqFJk2bq1+9RS5YdsPMAPB6PHA6Hb9rr9Zab9ldkZFhVxgIuCz/8kKvs/9urhmH2nppTdtYjz1mPrRkuJ2XFZ3S69L+2rf+HEpdCQpyKiqptyfID9m5r2LChCgoKfNMFBQW+3UOVcexYCSeC4YpTVuZWw7BgPdG+nt1RcBlJ3XVcZWVuFRScvORlBAU5LrrhHLCvgXbo0EEHDhzQwYMH5Xa7tXbtWt11112BWj0A4GcCNgKoUaOGpk2bphEjRujMmTOKiYnRfffdF6jVAwB+xvIC2LRpk+/njh07avXq1VavEgDgB84EBgBDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADEUBAIChKAAAMBQFYKiiokJNm/aSiouL7I4CwCYUgKHWrEnX/v37tHr1KrujALAJBWCgoqJCZWZultfrVWbmJ4wCAENRAAZasybdd19lj8fDKAAwFAVgoE8/3Sq32yVJcrtd+vTTrTYnAmAHCsBAHTt2ltP5091Anc5gdezY2eZEAOxAARgoNra3goIckqSgoCDFxcXbnAiAHSgAA4WHR6hLlxg5HA516XKX6tYNtzsSABsE2x0A9oiN7a3Dh3PZ+gcMRgEYKjw8QmPGTLA7BgAbsQsIAAxFAQCAoSgAADAUBQAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaypQDeffdd9erVS7169VJycrIdEYy3bVuWnnyynz7//DO7owCwScALoLS0VC+//LLS0tL07rvvaseOHcrKygp0DOMtXDhfkpSSMs/mJADsEvACcLvd8ng8Ki0tlcvlksvlUo0aNQIdw2jbtmWVuycwowDATAEvgLCwMD3zzDO6//77FRMTo8aNG+uWW24JdAyjndv6P4dRAGCmgN8QZu/evVq5cqU++ugj1a5dW88//7wWLVqkAQMG+PX8yMgwixNe+c5t/Z8/HRVV26Y0kKSQEKdO2x0Cl6WQEKdlf58BL4DMzEx17NhRkZGRkqT4+HgtXbrU7wI4dqxEHo/XyohXPKczuFwJOJ3BKig4aWMilJW57Y6Ay1RZmfs3/X0GBTkuuuEc8F1ArVu3VlZWlk6dOiWv16tNmzapXbt2gY5htAEDBpebTkwcalMSAHYKeAF06dJFvXr1Unx8vOLi4uRyuZSYmBjoGEaLju4kp/OnwZ/TGazbbrvD5kQA7GDLTeETExP50LfZgAGDtWDBHLb+AYPZUgCwX3R0J0VHd7I7BgAbcSkIADAUBQAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEqPBGsb9++euutt/SHP/xBDofDN9/r9crhcOiLL76wNCAAwBoVFsDs2bMlSWvXrrU8DAAgcCosgK+++upXf9+4ceOqygIACKAKCyAtLe2iv3M4HLr33nurNBAAIDAqXQAul0ter1chISGWhYL1Dh3KVnLyZI0ZM0FNmjSzOw4AG/j9LaBjx45p4MCBuvnmm9W+fXs9+uijys/PtzIbLJSSMlelpaVasGCO3VEA2MTvAnjppZfUoUMHZWVlKSsrS7feeqsmTZpkYTRY5dChbOXlHZYk5eUdVk7OQZsTAbCD3wWQnZ2t4cOHq06dOoqIiNDTTz+tQ4cOWZkNFklJmVtumlEAYCa/C8DlcunMmTO+6dLS0nLnBaD6OLf1f7FpAGbw+45gPXv21OOPP674+Hg5HA6tXLlSPXr0sDIbLNKoUeNyH/qNGvFVXsBEfhfAsGHD1LBhQ23ZskUej0fx8fFKSEiwMhsskpg4TJMmjfVNDxo03MY0AOxSqXsCd+7cWfXr11fnzp119OhRdgFVU02bNveNAho1aszXQAFD+X0M4OOPP9bDDz+spKQkHT9+XL169dKGDRuszAYLJSYOU82aNdn6BwzmdwHMnTtXy5cvV506ddSgQQMtXbpUr732mpXZYKGmTZtr7txFbP0DBvO7ANxutxo0aOCbbtOmDbuAAKAa87sAatasqby8PN+H/o4dO1SjRg3LggEArOX3QeDnnntOTz75pAoKCtSnTx9lZ2fr9ddftzIbAMBCfhfALbfcouXLl+vLL7+Ux+NRhw4dVK9ePSuzAQAsVGEBrF+//oLzd+zYIUlcDhoAqinuBwAAhqpUAeTk5KhJkyYqKSnRoUOHdNNNN1kaDgBgHb+/BfTPf/5TQ4cOlSQVFhZqxIgRWrFihWXBAADW8rsA3n77bb311luSpCZNmigjI0OLFy+2LBgAwFqVOhEsLCzMN127dm1OBAOAaszvAmjRooVmzJihnJwc5eTkaPbs2WrevLmF0QAAVvK7AJKSkpSdna0HH3xQCQkJys7O5paQAFCN+X0iWP369TVnTtXcOnDTpk2aM2eOSktL1blzZ40fP75Klgv/bduWpQUL5mjIkKd122132B0HgA38LoApU6ZccH5lP7xzcnI0ceJErVixQpGRkXrssce0efNmxcTEVGo5+G0WLpwvSUpJmUcBAIbyexdQeHi4799VV12l7du3X9IKP/zwQ/Xs2VMNGzZUSEiIZs2apQ4dOlzSsnBptm3LktvtkiS53S59/vlnNicCYAe/RwDDh5e/ccjAgQM1ZMiQSq/w4MGDCgkJ0eDBg3XkyBF17dpVI0eOrPRycOnObf2fwyjAfsXFRSoscSl113G7o+Ay8kOJSxHFRZYtv1K3hDxfWFiYjh49Wunnud1u7dixQ2lpaapVq5aGDBmi9PR0xcfH+/X8yMiwih+EX3Vu6//86aio2jalgSQ5nX4PxmEYpzPIsr/PSzoG4PV69e2336pFixaVXmH9+vXVsWNH35VEu3fvrl27dvldAMeOlcjj8VZ6vfgfpzO4XAk4ncEqKDhpYyKEhdVRcNhRPdGeK+zif1J3Hdfvwur8pr/PoCDHRTecL+kYQEREhOLi4jR9+vRKh7n77ruVmZmpEydOyO12a8uWLfr9739f6eXg0g0YMLjcdGLiUJuSALDTJR8DuFQdOnTQgAED1K9fP5WVlalz5876y1/+UiXLhn+ioztp4cL5crtdcjqD2f8PGKrCAujfv/9FL/ngcDj05ptvVnqlCQkJSkhIqPTzUHUGDBisBQvmsPUPGKzCAnjkkUd+Me/AgQOaN28eX9+sxqKjOyk6upPdMQDYqMIC6NGjR7npFStWaNGiRRoyZIgGDx58kWcBAC53fh8DOHnypMaPH6/vvvtOCxcuZOsfAKo5v74FtGPHDsXFxSk0NFTp6el8+APAFaDCEcBrr72m1NRUjRw5Ug888IBcLpeKiop8vw8PD7cwHgDAKhUWwLx58yRJr7zyiqZNmyav938nYTkcDu3Zs8e6dAAAy1RYAHv37q1wIdu3b9ftt99eJYEAAIFRJRcgeeWVV6piMQCAAKqSAjh/txAAoHqokgLg5vAAUP1wDVoAMBQFAACG4hgAABiqSgogOjq6KhaDADp0KFvDhj2lnJyDdkcBYJMKzwOo6IJv8+fP14svvlhlgRAYKSlzVVpaqgUL5mjKlMrf2AdA9Vfpq4Gi+jt0KFt5eYclSXl5h5WTc1BNmjSzORWAQKuwAHr37n3B+V6vVwcPsvugOkpJmVtumlEAYCa/Lwf99ttv69VXX1VpaalvXr169bR161ZLgsE657b+LzYNwAx+HwROSUlRamqqYmJilJ6erqefflrdu3e3Mhss0qhR41+dBmAGvwsgPDxcHTp0UJs2bXTs2DENGTJEn3/+uZXZYJHExGHlpgcNGm5TEgB28rsAgoODVVxcrGbNmmnXrl2SJLfbbVkwWKdp0+a+rf5GjRpzABgwlN8F8NBDD2nQoEHq2rWrli1bpvj4eLVo0cLKbLBQYuIw1axZk61/wGB+HwROSEhQz549VatWLS1btky7d+/WnXfeaWU2WKhp0+aaO3eR3TEA2MjvAjh9+rQ++uijcreDfOedd/S3v/3NilwAAIv5XQCDBw/WiRMndO211/rmORwOCgAAqim/CyA/P1/vv/8+1/4HgCuE3weBW7Vqpf/+979WZgEABJDfI4D77rtP999/v1q1aqXg4P89bfHixZYEAwBYy+8CmDt3rgYNGqSmTZtamQcAECB+F0DNmjU1cOBAK7MAAALI72MAnTp10pIlS3T06FEVFRX5/gEAqie/RwCpqak6e/asJk+e7JvncDi0Z88eS4IBAKzldwEsXbpUbdu2tTILACCA/N4FNGrUKCtzAAACzO8CuPHGG7VmzRrl5eVxDOAKUFRUqGnTXlJxcZHdUQDYxO9dQBs3btS6devKzfutxwCSk5NVWFioadOmXfIycGnWrEnX/v37tHr1KvXv/6TdcQDYwO8C2L17d5Wu+NNPP1V6erq6du1apctFxYqKCpWZuVler1eZmZ8oLi5edeuG2x0LQID5vQvI4/HojTfeUP/+/dW3b1/NmTNHLpfrklZaVFSkWbNmafDgwZf0fPw2a9aky+PxSvrp/+vq1atsTgTADn6PAGbOnKm9e/fqsccek8fj0bJly5ScnKxx48ZVeqUTJkzQs88+qyNHjlT6uZGRYZV+Dsr77LOtcrt/Km+326XPPtuqv//9GZtTmS0kxKnTdofAZSkkxKmoqNqWLNvvAtiyZYtWrlypkJAQSVLXrl0VFxdX6RWuWLFC11xzjTp27KhVqyq/5XnsWIlv6xWX5o47OuuTTz6W2+2S0xmsO+7orIKCk3bHMlpZGbdXxYWVlbl/099nUJDjohvOfheA1+v1ffhLUmhoaLlpf73//vsqKCjQAw88oOLiYp06dUpTp07V2LFjK70sXJrY2N7KzNwst1sKCgpSXFy83ZEA2MDvAmjdurWmTp2qRx55RA6HQ2lpaWrVqlWlV5iamur7edWqVdq+fTsf/gEWHh6hLl1i9PHHG9Wly10cAAYM5fdB4IkTJ+rEiRPq27evHnroIRUWFmrChAlWZoOFYmN7q2XLG9n6Bwzm8Hq9v7pD/cUXX7z4kx0OTZ06tcpD/RqOAeBKlJw8Wad/+D890b6e3VFwGUnddVy/a3iDRo/+xyUv4zcdA2jZsuUv5hUWFurNN99U48aNLzkUAMBeFRbAk0+WP0s0KytLo0ePVmxsrMaPH29ZMACAtfw+COxyuTRz5kylp6crKSlJPXr0sDIXAMBifhVAdna2/v73v+uqq65SRkaGGjZsaHUuAIDFKvwW0MqVK/XQQw/pT3/6k9LS0vjwB4ArRIUjgHHjxikoKEgpKSl64403fPO9Xq8cDoe++OILSwMCAKxRYQFs3LgxEDkAAAFWYQHwVU8AuDL5fSYwAODKQgEAgKEoAAAwFAUAAIby+0xgXFmmT39Fe/bsVrt2HfTss6PtjgPABowADLVnz25J0u7dX9ucBIBdKAADTZ/+SrnpWbOSbUoCwE7sAjLQua3/cxgFXB5+KHEpdddxu2PYruSsR5IUFsr26Q8lLjW3cPkUAHAZaNKkmd0RLhtHDx2UJNVvyH+T5rL2vUEBAJeBfv0etTvCZSM5ebIk/aa7YME/jLEM1KZNu3LT7dp1sCkJADtRAAYaNar8fZ75GihgJgrAUOdGAWz9A+biGIChfj4KAGAeRgAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADEUBAIChKAAAMBQXgzPUgAH95fG45XQG6403FtsdB4ANbCmAOXPm6IMPPpAkxcTE6IUXXrAjhtE8Hrckye122ZwEgF0CvgsoKytLmZmZSk9PV0ZGhr799lt9+OGHgY5htAED+pebHjiQ2xECJgr4CCAqKkpjxoxRaGioJOn6669XXl5eoGMY7dzW/zmMAgAzBbwAWrZs6fs5OztbH3zwgd566y2/nx8ZGWZFLONFRdW2OwIgSQoJcUriPRkIth0E3r9/vwYNGqQXXnhBzZs39/t5x46VyOPxWhfMUAUFJ+2OAEiSysp+GqHynqwaQUGOi2442/I10J07d+rxxx/Xc889p969e9sRwWhBQc5y004nXwYDTBTwAjhy5IiGDRumGTNmqFevXoFePSQtXJhWbpqvgQJmCvim36JFi3TmzBlNmzbNN+/hhx9W3759Ax3FaEFBTt95AADM5PB6vdVqhzrHAIArW3LyZEnS6NH/sDnJleGyOwYAALAfBQAAhqIAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADGXU/QC2bv1ES5fae/ers2fPyO1225rhcuJ0OhUaWsPuGOrX71F17nyX3TFst3XrJ8rM3GxrhkOHDkqSmjZtZmsOSerSJabavy9+7X4A3A4KwGWlbt26dkcwhlEjAAAwDXcEAwD8AgUAAIaiAADAUBQAABiKAgAAQ1EAAGAoCgAADFXtTgQLCnLYHQEAqo1f+8ysdieCAQCqBruAAMBQFAAAGIoCAABDUQAAYCgKAAAMRQEAgKEoAAAwFAUAAIaiAADAUBRANbZt2zb179//Ny9n3Lhx2r17dxUkAvzTrVs35ebmauPGjZo9e3ZA1pmTk6OxY8cGZF3VRbW7FhCq3ssvv2x3BBjqnnvu0T333BOQdeXl5SknJycg66ouKIBqrrCwUE899ZSOHj2q9u3ba+LEifrss8/02muvyeVy6dprr9XkyZMVERGhbt26KS4uTpmZmSotLVVycrLatm2r/v37a/jw4YqOjtbMmTP1r3/9SxEREYqKilK3bt10++23a/jw4WrZsqX27NmjyMhIzZ49W+Hh4Xa/fNho27Ztmj9/vkJCQpSbm6tu3bqpVq1a2rBhgyQpJSVF69at07vvvqvS0lKFhIRo5syZatGihW8Zq1at0vbt2zVt2jRt27ZNU6ZMkdPp1M0336x///vfSktLU//+/dWuXTvt3LlTx48f1/jx4xUTE6Pvv/9ekydP1qlTp3T8+HElJiaqb9++ev3115Wfn6+DBw/q8OHD+utf/6ohQ4ZoypQpys3NVVJSkiZOnGjXf7bLCruAqrnc3Fz94x//0OrVq/Xjjz8qJSVFM2fO1KJFi5SRkaEuXbpoxowZvseHh4frnXfe0cMPP6wFCxaUW9amTZu0c+dOrV27VikpKfruu+98v9u7d6+eeOIJrV27VnXq1NGaNWsC9hpx+fr666+VlJSklStXasmSJapXr55WrVqlG2+8Ue+99542bNigtLQ0rV27Vl27dtWSJUsuuJyysjK98MILmj59ujIyMhQcHPyL3y9btkwvvviib5fRihUrNHToUK1cuVKLFy/Wq6++6nv8vn37tGjRIq1YsUIpKSk6ceKExo8fr7Zt2/Lhfx5GANXcrbfequbNm0uSYmNjNWbMGDkcDj366KOSJI/Ho7p16/oef+edd0qSWrZsqfXr15dbVlZWlu6//36FhoYqNDRU3bt39/0uMjJSN910k++5xcXFVr4sVBOtWrXSNddcI0mKiIhQx44dJUmNGjXSiRMnNHPmTL333nvKzs7Wli1b1KZNmwsu5/vvv1dkZKRat24tSUpISCi3a/L8921RUZEkacyYMdqyZYsWLFig77//XqdOnfI9Pjo6WqGhoYqMjFR4eLhOnjxZ5a/9SkABVHPnbymdu7L3Lbfcovnz50uSzpw5ox9//NH3mBo1akiSHI5fXiM8KChIHo/ngus597xzz+Uq4pCkkJCQctNOp9P385EjR9SnTx898sgjuuuuu1S/fn3t2bPngstxOp0Xfe9JF37fjhw5UnXq1NHdd9+tnj17au3atb94/Lnn8H69MHYBVXM7d+5UXl6ePB6PMjIy9Nhjj+mrr77SgQMHJEnz5s0rNzT+NZ06ddL69et19uxZlZSU6OOPP75gUQD+2L17t5o1a6bHH39c7dq104YNG+R2uy/42BYtWujEiRPat2+fJPm1i3Hr1q16+umn1b17d33yySeSdNHlSz+VjMvluoRXcuViBFDN3XDDDRo7dqwKCgp0xx13aMiQIbrppps0cuRIeTweXX311Zo+fbpfy+ratau+/PJL9e7dW3Xr1lWDBg3KbUkBldGlSxft3btXPXv2lNfr1W233ab9+/df8LGhoaF69dVXNXr0aAUFBem6667T7373u19d/ogRI9SvXz/VqFFDrVu3VuPGjZWbm3vRx19//fU6efKkRo0a5fffxJWOO4LB58svv1R2drZ69+6tsrIy9enTR1OnTvXtlwWs4vF4NGPGDA0fPly1atVSamqq8vPzNWbMGLujXdEYAcDnuuuu05w5c5Samiqv16sHH3yQD38ERFBQkMLDw5WQkKCQkBA1btyY81MCgBEAABiKg8AAYCgKAAAMRQEAgKE4CAz8jNvt1uLFi7VmzRq53W6VlZXp7rvv1jPPPKMJEyaoZcuWeuqpp+yOCfxmFADwM5MmTVJxcbHefPNN1a5dW6dOndLzzz+vcePGlTvTFajuKADgPLm5uVqzZo0yMzMVFhYmSapVq5aSkpL0xRdf6KOPPvI99p133tGyZctUVlam4uJiDRw4UP369VNBQYFGjx6twsJCSVJMTIxGjhx50fmAXTgGAJzn22+/1Q033OD78D8nKipKPXr08E3/+OOPvitNZmRkaNasWb6zS5cvX65rr71W6enpWrJkiQ4ePKiTJ09edD5gF0YAwHl+7YJ457vqqqs0f/58bd68WdnZ2dq7d6/vapR33nmnEhMTdeTIEXXq1EnPPfecateufdH5gF0YAQDnad++vf7zn/+opKSk3Pz8/HwlJibq9OnTkqQffvhBDz74oA4fPqw//vGP5XbltG/fXhs3blSfPn18NyT55ptvLjofsAsjAOA8V199tWJjYzV27FhNnTpVYWFhKikp0aRJkxQeHq6goJ+2mb755hvVq1dPQ4cOlSTf5bfdbrdmzZolr9erUaNG6Z577tG+ffu0f/9+rVu37oLz27Zta9vrhdm4FATwMy6XS/PmzdP69evldDp19uxZde/eXSNGjPB9DbRfv3569tlndeDAATkcDt1+++368MMPtWTJEtWuXVtjxoxRfn6+QkNDdeONNyopKUnFxcUXnB8aGmr3S4ahKAAAMBTHAADAUBQAABiKAgAAQ1EAAGAoCgAADEUBAIChKAAAMBQFAACG+n8Ycb6BlEx/dwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df,x='Class',y='Normal_Nucleoli')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2b40f540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Normal_Nucleoli', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(x=\"Normal_Nucleoli\", data=df , hue='Class' ,palette='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17460d5c",
   "metadata": {},
   "source": [
    "# Mitoses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "758ab29b",
   "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>Mitoses</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>benign</th>\n",
       "      <td>445</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignant</th>\n",
       "      <td>134</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mitoses     1   2   3   4   5   6   7   8   10\n",
       "Class                                         \n",
       "benign     445   8   2   0   1   0   1   1   0\n",
       "malignant  134  27  31  12   5   3   8   7  14"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the process in cell division by which the nucleus divides\n",
    "mit =pd.crosstab(df['Class'],df['Mitoses'])\n",
    "mit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3372dd19",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='Mitoses'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df,x=\"Class\",y=\"Mitoses\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c22aa6cd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Mitoses', ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "sns.countplot(x=\"Mitoses\", data=df , hue='Class' ,palette='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d63670",
   "metadata": {},
   "source": [
    "# Converting Categorical Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "345849a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Clump_Thickness        699 non-null    int64  \n",
      " 1   Cell_Size_Uniformity   699 non-null    int64  \n",
      " 2   Cell_Shape_Uniformity  699 non-null    int64  \n",
      " 3   Marginal_Adhesion      699 non-null    int64  \n",
      " 4   Single_Epi_Cell_Size   699 non-null    int64  \n",
      " 5   Bare_Nuclei            699 non-null    float64\n",
      " 6   Bland_Chromatin        699 non-null    int64  \n",
      " 7   Normal_Nucleoli        699 non-null    int64  \n",
      " 8   Mitoses                699 non-null    int64  \n",
      " 9   Class                  699 non-null    object \n",
      "dtypes: float64(1), int64(8), object(1)\n",
      "memory usage: 54.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "31b96a35",
   "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>malignant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     malignant\n",
       "0            0\n",
       "1            0\n",
       "2            0\n",
       "3            0\n",
       "4            0\n",
       "..         ...\n",
       "694          0\n",
       "695          0\n",
       "696          1\n",
       "697          1\n",
       "698          1\n",
       "\n",
       "[699 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_class =pd.get_dummies(df['Class'],drop_first=True)\n",
    "_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c2773e54",
   "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>Clump_Thickness</th>\n",
       "      <th>Cell_Size_Uniformity</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epi_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "      <th>malignant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>malignant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>malignant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>malignant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clump_Thickness  Cell_Size_Uniformity  Cell_Shape_Uniformity  \\\n",
       "694                3                     1                      1   \n",
       "695                2                     1                      1   \n",
       "696                5                    10                     10   \n",
       "697                4                     8                      6   \n",
       "698                4                     8                      8   \n",
       "\n",
       "     Marginal_Adhesion  Single_Epi_Cell_Size  Bare_Nuclei  Bland_Chromatin  \\\n",
       "694                  1                     3          2.0                1   \n",
       "695                  1                     2          1.0                1   \n",
       "696                  3                     7          3.0                8   \n",
       "697                  4                     3          4.0               10   \n",
       "698                  5                     4          5.0               10   \n",
       "\n",
       "     Normal_Nucleoli  Mitoses      Class  malignant  \n",
       "694                1        1     benign          0  \n",
       "695                1        1     benign          0  \n",
       "696               10        2  malignant          1  \n",
       "697                6        1  malignant          1  \n",
       "698                4        1  malignant          1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.concat([df,_class],axis=1)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ccddea",
   "metadata": {},
   "source": [
    "# Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "774a35cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Clump_Thickness', 'Cell_Size_Uniformity', 'Cell_Shape_Uniformity',\n",
       "       'Marginal_Adhesion', 'Single_Epi_Cell_Size', 'Bare_Nuclei',\n",
       "       'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class', 'malignant'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6258f8f7",
   "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>Clump_Thickness</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epi_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clump_Thickness  Cell_Shape_Uniformity  Cell_Shape_Uniformity  \\\n",
       "0                  5                      1                      1   \n",
       "1                  5                      4                      4   \n",
       "2                  3                      1                      1   \n",
       "3                  6                      8                      8   \n",
       "4                  4                      1                      1   \n",
       "..               ...                    ...                    ...   \n",
       "694                3                      1                      1   \n",
       "695                2                      1                      1   \n",
       "696                5                     10                     10   \n",
       "697                4                      6                      6   \n",
       "698                4                      8                      8   \n",
       "\n",
       "     Marginal_Adhesion  Single_Epi_Cell_Size  Bare_Nuclei  Bland_Chromatin  \\\n",
       "0                    1                     2          1.0                3   \n",
       "1                    5                     7         10.0                3   \n",
       "2                    1                     2          2.0                3   \n",
       "3                    1                     3          4.0                3   \n",
       "4                    3                     2          1.0                3   \n",
       "..                 ...                   ...          ...              ...   \n",
       "694                  1                     3          2.0                1   \n",
       "695                  1                     2          1.0                1   \n",
       "696                  3                     7          3.0                8   \n",
       "697                  4                     3          4.0               10   \n",
       "698                  5                     4          5.0               10   \n",
       "\n",
       "     Normal_Nucleoli  Mitoses  \n",
       "0                  1        1  \n",
       "1                  2        1  \n",
       "2                  1        1  \n",
       "3                  7        1  \n",
       "4                  1        1  \n",
       "..               ...      ...  \n",
       "694                1        1  \n",
       "695                1        1  \n",
       "696               10        2  \n",
       "697                6        1  \n",
       "698                4        1  \n",
       "\n",
       "[699 rows x 9 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df[['Clump_Thickness', 'Cell_Shape_Uniformity', 'Cell_Shape_Uniformity','Marginal_Adhesion'\n",
    "        ,'Single_Epi_Cell_Size','Bare_Nuclei', 'Bland_Chromatin','Normal_Nucleoli','Mitoses']]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "343d1505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "694    0\n",
       "695    0\n",
       "696    1\n",
       "697    1\n",
       "698    1\n",
       "Name: malignant, Length: 699, dtype: uint8"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['malignant']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "92bc1c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.3 , random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "390719ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "435    1\n",
       "541    0\n",
       "381    1\n",
       "611    1\n",
       "647    0\n",
       "Name: malignant, dtype: uint8"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cf3c3f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "691    1\n",
       "517    0\n",
       "258    0\n",
       "437    0\n",
       "213    1\n",
       "      ..\n",
       "690    0\n",
       "654    0\n",
       "400    1\n",
       "533    0\n",
       "496    0\n",
       "Name: malignant, Length: 210, dtype: uint8"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8c4fb87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "691    1\n",
       "517    0\n",
       "258    0\n",
       "437    0\n",
       "213    1\n",
       "      ..\n",
       "690    0\n",
       "654    0\n",
       "400    1\n",
       "533    0\n",
       "496    0\n",
       "Name: malignant, Length: 210, dtype: Int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0c44082a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "691    1\n",
       "517    0\n",
       "258    0\n",
       "437    0\n",
       "213    1\n",
       "Name: malignant, dtype: uint8"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "42f003e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(489, 9)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "60fa659b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 9)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2dbe3cb0",
   "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>Clump_Thickness</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epi_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clump_Thickness  Cell_Shape_Uniformity  Cell_Shape_Uniformity  \\\n",
       "435               10                     10                     10   \n",
       "541                3                      1                      1   \n",
       "381               10                      3                      3   \n",
       "611                5                     10                     10   \n",
       "647                1                      1                      1   \n",
       "\n",
       "     Marginal_Adhesion  Single_Epi_Cell_Size  Bare_Nuclei  Bland_Chromatin  \\\n",
       "435                  1                     3         10.0                5   \n",
       "541                  1                     2          1.0                1   \n",
       "381                  6                     4         10.0                7   \n",
       "611                 10                     5          2.0                8   \n",
       "647                  3                     2          1.0                1   \n",
       "\n",
       "     Normal_Nucleoli  Mitoses  \n",
       "435                1        1  \n",
       "541                1        1  \n",
       "381                8        4  \n",
       "611                5        1  \n",
       "647                1        1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c5998eca",
   "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>Clump_Thickness</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epi_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Clump_Thickness  Cell_Shape_Uniformity  Cell_Shape_Uniformity  \\\n",
       "691                5                     10                     10   \n",
       "517                1                      1                      1   \n",
       "258                3                      1                      1   \n",
       "437                4                      1                      1   \n",
       "213               10                     10                     10   \n",
       "\n",
       "     Marginal_Adhesion  Single_Epi_Cell_Size  Bare_Nuclei  Bland_Chromatin  \\\n",
       "691                  5                     4          5.0                4   \n",
       "517                  1                     1          1.0                2   \n",
       "258                  1                     2          1.0                3   \n",
       "437                  1                     2          1.0                1   \n",
       "213                 10                     7         10.0                7   \n",
       "\n",
       "     Normal_Nucleoli  Mitoses  \n",
       "691                4        1  \n",
       "517                1        1  \n",
       "258                1        1  \n",
       "437                1        1  \n",
       "213               10        4  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c859afc1",
   "metadata": {},
   "source": [
    "# Logestic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5d422630",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg =LogisticRegression(max_iter=100, C=5, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "af4cabf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=5, random_state=101)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fbdaadc3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.38500120e-02, 9.76149988e-01],\n",
       "       [9.98660631e-01, 1.33936929e-03],\n",
       "       [9.94351203e-01, 5.64879705e-03],\n",
       "       [9.95164686e-01, 4.83531387e-03],\n",
       "       [4.92153054e-06, 9.99995078e-01],\n",
       "       [9.01439307e-01, 9.85606933e-02],\n",
       "       [2.36756000e-02, 9.76324400e-01],\n",
       "       [9.84663413e-01, 1.53365866e-02],\n",
       "       [1.02372141e-02, 9.89762786e-01],\n",
       "       [9.96680532e-01, 3.31946806e-03],\n",
       "       [3.80460466e-04, 9.99619540e-01],\n",
       "       [6.03063783e-01, 3.96936217e-01],\n",
       "       [5.38769881e-04, 9.99461230e-01],\n",
       "       [2.77810976e-02, 9.72218902e-01],\n",
       "       [9.94035118e-01, 5.96488207e-03],\n",
       "       [9.97159527e-01, 2.84047327e-03],\n",
       "       [9.28484890e-01, 7.15151097e-02],\n",
       "       [9.46458521e-01, 5.35414792e-02],\n",
       "       [1.72514465e-04, 9.99827486e-01],\n",
       "       [1.08124189e-04, 9.99891876e-01],\n",
       "       [1.03070268e-06, 9.99998969e-01],\n",
       "       [4.88685592e-03, 9.95113144e-01],\n",
       "       [9.88412573e-01, 1.15874273e-02],\n",
       "       [9.96680532e-01, 3.31946806e-03],\n",
       "       [9.99021876e-01, 9.78123658e-04],\n",
       "       [8.53810598e-02, 9.14618940e-01],\n",
       "       [1.19699377e-03, 9.98803006e-01],\n",
       "       [9.87646151e-01, 1.23538487e-02],\n",
       "       [9.95869951e-01, 4.13004854e-03],\n",
       "       [9.96680532e-01, 3.31946806e-03],\n",
       "       [9.84352481e-01, 1.56475194e-02],\n",
       "       [5.20539723e-03, 9.94794603e-01],\n",
       "       [8.50581249e-04, 9.99149419e-01],\n",
       "       [9.19446444e-01, 8.05535561e-02],\n",
       "       [5.39683892e-04, 9.99460316e-01],\n",
       "       [9.83743485e-01, 1.62565154e-02],\n",
       "       [5.67738547e-03, 9.94322615e-01],\n",
       "       [1.13671712e-02, 9.88632829e-01],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [9.67644898e-01, 3.23551022e-02],\n",
       "       [9.08438563e-01, 9.15614367e-02],\n",
       "       [9.97865113e-01, 2.13488724e-03],\n",
       "       [2.68834620e-04, 9.99731165e-01],\n",
       "       [9.84208646e-01, 1.57913538e-02],\n",
       "       [6.74883403e-02, 9.32511660e-01],\n",
       "       [9.95993385e-01, 4.00661461e-03],\n",
       "       [1.63639588e-05, 9.99983636e-01],\n",
       "       [1.35026149e-06, 9.99998650e-01],\n",
       "       [9.95164686e-01, 4.83531387e-03],\n",
       "       [9.24071825e-01, 7.59281747e-02],\n",
       "       [9.97408243e-01, 2.59175722e-03],\n",
       "       [9.99021876e-01, 9.78123658e-04],\n",
       "       [1.67749848e-03, 9.98322502e-01],\n",
       "       [2.87331986e-03, 9.97126680e-01],\n",
       "       [9.98109591e-01, 1.89040861e-03],\n",
       "       [1.00590700e-03, 9.98994093e-01],\n",
       "       [9.99021876e-01, 9.78123658e-04],\n",
       "       [5.73209819e-03, 9.94267902e-01],\n",
       "       [1.87804186e-05, 9.99981220e-01],\n",
       "       [9.88412573e-01, 1.15874273e-02],\n",
       "       [9.96677940e-01, 3.32206019e-03],\n",
       "       [9.90387304e-01, 9.61269647e-03],\n",
       "       [9.93185171e-01, 6.81482946e-03],\n",
       "       [7.53057298e-01, 2.46942702e-01],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [1.05299401e-01, 8.94700599e-01],\n",
       "       [9.97647149e-01, 2.35285104e-03],\n",
       "       [4.92874850e-04, 9.99507125e-01],\n",
       "       [9.98660631e-01, 1.33936929e-03],\n",
       "       [9.96680532e-01, 3.31946806e-03],\n",
       "       [2.23505184e-03, 9.97764948e-01],\n",
       "       [9.90681147e-01, 9.31885250e-03],\n",
       "       [9.97915043e-01, 2.08495675e-03],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [1.88505661e-02, 9.81149434e-01],\n",
       "       [7.28584023e-03, 9.92714160e-01],\n",
       "       [2.00660553e-07, 9.99999799e-01],\n",
       "       [6.08787068e-03, 9.93912129e-01],\n",
       "       [9.95993385e-01, 4.00661461e-03],\n",
       "       [8.16709242e-01, 1.83290758e-01],\n",
       "       [3.92472289e-02, 9.60752771e-01],\n",
       "       [9.91742033e-01, 8.25796698e-03],\n",
       "       [8.19642318e-02, 9.18035768e-01],\n",
       "       [9.86037893e-01, 1.39621071e-02],\n",
       "       [9.92025112e-01, 7.97488847e-03],\n",
       "       [9.98619254e-01, 1.38074587e-03],\n",
       "       [9.66964055e-01, 3.30359454e-02],\n",
       "       [1.39641335e-02, 9.86035867e-01],\n",
       "       [9.89750141e-01, 1.02498587e-02],\n",
       "       [9.94351203e-01, 5.64879705e-03],\n",
       "       [2.57049740e-02, 9.74295026e-01],\n",
       "       [9.95993385e-01, 4.00661461e-03],\n",
       "       [9.95993385e-01, 4.00661461e-03],\n",
       "       [9.72536654e-01, 2.74633457e-02],\n",
       "       [9.95980126e-01, 4.01987439e-03],\n",
       "       [9.91109576e-01, 8.89042447e-03],\n",
       "       [9.79573512e-01, 2.04264881e-02],\n",
       "       [9.91780434e-01, 8.21956624e-03],\n",
       "       [9.91562235e-01, 8.43776491e-03],\n",
       "       [4.84283030e-07, 9.99999516e-01],\n",
       "       [9.97801183e-01, 2.19881652e-03],\n",
       "       [9.98332761e-01, 1.66723853e-03],\n",
       "       [9.98332761e-01, 1.66723853e-03],\n",
       "       [9.56878359e-01, 4.31216408e-02],\n",
       "       [9.95454958e-01, 4.54504206e-03],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [9.97643252e-01, 2.35674765e-03],\n",
       "       [1.90813386e-01, 8.09186614e-01],\n",
       "       [9.42531605e-01, 5.74683954e-02],\n",
       "       [9.98619254e-01, 1.38074587e-03],\n",
       "       [9.93433672e-01, 6.56632786e-03],\n",
       "       [9.62288859e-01, 3.77111413e-02],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [2.91802380e-01, 7.08197620e-01],\n",
       "       [9.97717590e-01, 2.28241012e-03],\n",
       "       [4.30784950e-04, 9.99569215e-01],\n",
       "       [2.40535398e-03, 9.97594646e-01],\n",
       "       [1.31711416e-04, 9.99868289e-01],\n",
       "       [9.99021876e-01, 9.78123658e-04],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [9.84220814e-01, 1.57791862e-02],\n",
       "       [9.95164686e-01, 4.83531387e-03],\n",
       "       [9.98660631e-01, 1.33936929e-03],\n",
       "       [9.98109591e-01, 1.89040861e-03],\n",
       "       [9.76378920e-01, 2.36210801e-02],\n",
       "       [9.64802869e-01, 3.51971312e-02],\n",
       "       [2.39021398e-02, 9.76097860e-01],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [9.90650472e-01, 9.34952795e-03],\n",
       "       [9.82114969e-01, 1.78850309e-02],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [3.63398339e-07, 9.99999637e-01],\n",
       "       [9.99021876e-01, 9.78123658e-04],\n",
       "       [1.68388642e-02, 9.83161136e-01],\n",
       "       [3.07292948e-01, 6.92707052e-01],\n",
       "       [9.95164686e-01, 4.83531387e-03],\n",
       "       [3.02667747e-05, 9.99969733e-01],\n",
       "       [9.98619254e-01, 1.38074587e-03],\n",
       "       [1.84539212e-03, 9.98154608e-01],\n",
       "       [1.14199230e-02, 9.88580077e-01],\n",
       "       [9.60370042e-01, 3.96299579e-02],\n",
       "       [9.78518996e-01, 2.14810039e-02],\n",
       "       [1.51405679e-04, 9.99848594e-01],\n",
       "       [9.97647149e-01, 2.35285104e-03],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [9.37075473e-03, 9.90629245e-01],\n",
       "       [5.27808357e-02, 9.47219164e-01],\n",
       "       [9.74036660e-01, 2.59633398e-02],\n",
       "       [9.85510737e-01, 1.44892631e-02],\n",
       "       [8.90834435e-03, 9.91091656e-01],\n",
       "       [3.59902659e-05, 9.99964010e-01],\n",
       "       [9.97647149e-01, 2.35285104e-03],\n",
       "       [9.99021876e-01, 9.78123658e-04],\n",
       "       [2.27583209e-01, 7.72416791e-01],\n",
       "       [6.12025217e-01, 3.87974783e-01],\n",
       "       [1.78724227e-01, 8.21275773e-01],\n",
       "       [6.17098012e-01, 3.82901988e-01],\n",
       "       [2.03841236e-04, 9.99796159e-01],\n",
       "       [2.03684547e-01, 7.96315453e-01],\n",
       "       [9.94719872e-01, 5.28012827e-03],\n",
       "       [9.98619254e-01, 1.38074587e-03],\n",
       "       [8.41705199e-05, 9.99915829e-01],\n",
       "       [4.19919946e-02, 9.58008005e-01],\n",
       "       [9.98332761e-01, 1.66723853e-03],\n",
       "       [9.67944319e-01, 3.20556808e-02],\n",
       "       [4.63622667e-04, 9.99536377e-01],\n",
       "       [1.56403165e-04, 9.99843597e-01],\n",
       "       [5.42080637e-01, 4.57919363e-01],\n",
       "       [9.71781307e-01, 2.82186931e-02],\n",
       "       [1.27607582e-04, 9.99872392e-01],\n",
       "       [9.95454958e-01, 4.54504206e-03],\n",
       "       [9.72318002e-01, 2.76819975e-02],\n",
       "       [9.93185171e-01, 6.81482946e-03],\n",
       "       [8.19116821e-01, 1.80883179e-01],\n",
       "       [9.97159527e-01, 2.84047327e-03],\n",
       "       [9.94351203e-01, 5.64879705e-03],\n",
       "       [9.94351203e-01, 5.64879705e-03],\n",
       "       [2.23159028e-04, 9.99776841e-01],\n",
       "       [6.26332615e-01, 3.73667385e-01],\n",
       "       [9.71239418e-03, 9.90287606e-01],\n",
       "       [9.99171519e-01, 8.28480748e-04],\n",
       "       [9.91780434e-01, 8.21956624e-03],\n",
       "       [2.80738673e-06, 9.99997193e-01],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [3.93907644e-01, 6.06092356e-01],\n",
       "       [9.94351203e-01, 5.64879705e-03],\n",
       "       [8.84008039e-01, 1.15991961e-01],\n",
       "       [9.97159527e-01, 2.84047327e-03],\n",
       "       [9.91780434e-01, 8.21956624e-03],\n",
       "       [6.92365358e-03, 9.93076346e-01],\n",
       "       [1.46127480e-03, 9.98538725e-01],\n",
       "       [8.92333511e-01, 1.07666489e-01],\n",
       "       [6.95832234e-01, 3.04167766e-01],\n",
       "       [9.91780434e-01, 8.21956624e-03],\n",
       "       [4.35672766e-02, 9.56432723e-01],\n",
       "       [9.93173935e-01, 6.82606511e-03],\n",
       "       [9.81287142e-01, 1.87128581e-02],\n",
       "       [1.70983378e-04, 9.99829017e-01],\n",
       "       [9.95993385e-01, 4.00661461e-03],\n",
       "       [9.89182366e-01, 1.08176342e-02],\n",
       "       [9.97150115e-01, 2.84988478e-03],\n",
       "       [2.33013095e-03, 9.97669869e-01],\n",
       "       [9.53973518e-01, 4.60264819e-02],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [9.98051225e-01, 1.94877501e-03],\n",
       "       [9.98163344e-01, 1.83665645e-03],\n",
       "       [9.94351203e-01, 5.64879705e-03],\n",
       "       [8.32109834e-05, 9.99916789e-01],\n",
       "       [9.95993385e-01, 4.00661461e-03],\n",
       "       [9.99051199e-01, 9.48800925e-04]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c548016a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_logreg = logreg.predict(x_test)\n",
    "pred_logreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ad6574d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "benign=135\n",
      "malignant=75\n"
     ]
    }
   ],
   "source": [
    "benign = 0\n",
    "malignant = 0\n",
    "for i in pred_logreg:\n",
    "    if i ==0:\n",
    "        benign+=1\n",
    "    else :\n",
    "        malignant+=1\n",
    "print('benign={}\\nmalignant={}'.format(benign,malignant))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3d66bac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[127,   2],\n",
       "       [  8,  73]], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation\n",
    "confusion_matrix(y_test,pred_logreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "eb89d5fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9523809523809523"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_test,pred_logreg)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b3251067",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bare_Nuclei']=df['Bare_Nuclei'].astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "edfa541e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.949009324009324"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score = f1_score(y_test, pred_logreg, average='macro')\n",
    "f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "36ee7dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9523809523809523"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec_score=precision_score(y_test, pred_logreg, average='micro')\n",
    "prec_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e10d4d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9844961240310077"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(y_test, pred_logreg).ravel()\n",
    "specificity = tn / (tn+fp)\n",
    "specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0e04eaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9012345679012346"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensitivity = tp/(tp+fn)\n",
    "sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "53af9c8a",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Max_Iter</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>300</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>300</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Max_Iter  C\n",
       "1       200  1\n",
       "2       200  2\n",
       "3       200  3\n",
       "4       200  4\n",
       "5       300  1\n",
       "6       300  2\n",
       "7       300  3\n",
       "8       300  4\n",
       "9       400  1\n",
       "10      400  2\n",
       "11      400  3\n",
       "12      400  4\n",
       "13      500  1\n",
       "14      500  2\n",
       "15      500  3\n",
       "16      500  4"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# k-fold cross validation\n",
    "max_iter=[200,300,400,500]\n",
    "c=[1,2,3,4]\n",
    "grid=itertools.product(max_iter,c)\n",
    "grid=pd.DataFrame(grid,index=range(1,17),columns=[['Max_Iter','C']])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "964ec296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159,\n",
       " 0.963181148748159]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc=[]\n",
    "for i in range (0,16):\n",
    "    lor=LogisticRegression(max_iter=grid.iloc[i,0],C=grid.iloc[i,1],random_state=101)\n",
    "    scores=cross_val_score(lor, X=x_train,y=y_train.values.ravel(), cv=5, scoring='accuracy')\n",
    "    sc.append(np.mean(scores))\n",
    "sc    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "63a5e7cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1abc3e3d0d0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1,17),sc,marker='o',markerfacecolor='red',color='black',\n",
    "        markeredgecolor='black',markeredgewidth=1,markersize=13,linestyle='--')\n",
    "plt.axvline(np.argmax(sc)+1,linestyle='--',color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f847dc2",
   "metadata": {},
   "source": [
    "دلیل این اتفاق ممکن است به این خاطر باشد که تمامی فیچرها عددی بین 1 تا 10 دارند و بیشترین فراوانی این داده ها برای مقادیر 1 و 2 هستند به عبارتی چوله به راست هستند به نظر بهترین راه برای جلوگیری از این اتفاق،نرمال کردن داده ها با استفاده از تبدیل باکس کاکس باشد"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4a84f317",
   "metadata": {},
   "outputs": [],
   "source": [
    "logr=LogisticRegression(max_iter=grid.iloc[2,0],C=grid.iloc[1,1],random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "33cefa74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=2, max_iter=200, random_state=101)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9944cdbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_logr=logr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e33a52dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[127,   2],\n",
       "       [  8,  73]], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_m=confusion_matrix(y_test,pred_logr)\n",
    "c_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d0138c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "TN=c_m[0,0]\n",
    "TP=c_m[1,1]\n",
    "FN=c_m[1,0]\n",
    "FP=c_m[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e89a3a23",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Sensitivity  Specificity\n",
       "Logistic Regression  0.952381   0.973333     0.901235     0.984496"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluated_model=pd.DataFrame({'Accuracy':(TN+TP)/(TP+TN+FN+FP),\n",
    "                             'Precision':TP/(TP+FP),\n",
    "                            'Sensitivity':TP/(TP+FN),\n",
    "                            'Specificity':TN/(TN+FP)},\n",
    "                            index=[\"Logistic Regression\"])\n",
    "evaluated_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74eac079",
   "metadata": {},
   "source": [
    "# Decision Tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dc34dd94",
   "metadata": {},
   "outputs": [],
   "source": [
    "Class_tree=DecisionTreeClassifier(max_depth=5,min_samples_leaf=5,ccp_alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e2016f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_tree=Class_tree.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f3010c9f",
   "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>max_depth</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>ccp_alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max_depth  min_samples_leaf  ccp_alpha\n",
       "1           4                 5     0.0001\n",
       "2           4                 5     0.0010\n",
       "3           4                 5     0.0100\n",
       "4           4                10     0.0001\n",
       "5           4                10     0.0010\n",
       "6           4                10     0.0100\n",
       "7           4                15     0.0001\n",
       "8           4                15     0.0010\n",
       "9           4                15     0.0100\n",
       "10          6                 5     0.0001\n",
       "11          6                 5     0.0010\n",
       "12          6                 5     0.0100\n",
       "13          6                10     0.0001\n",
       "14          6                10     0.0010\n",
       "15          6                10     0.0100\n",
       "16          6                15     0.0001\n",
       "17          6                15     0.0010\n",
       "18          6                15     0.0100\n",
       "19          8                 5     0.0001\n",
       "20          8                 5     0.0010\n",
       "21          8                 5     0.0100\n",
       "22          8                10     0.0001\n",
       "23          8                10     0.0010\n",
       "24          8                10     0.0100\n",
       "25          8                15     0.0001\n",
       "26          8                15     0.0010\n",
       "27          8                15     0.0100"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_depth=[4,6,8]\n",
    "min_samples_leaf=[5,10,15]\n",
    "ccp_alpha=[0.0001,0.001,0.01]\n",
    "grid=list(itertools.product(max_depth,min_samples_leaf,ccp_alpha))\n",
    "grid=pd.DataFrame(grid,index=range(1,28),columns=['max_depth','min_samples_leaf','ccp_alpha'])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5315cc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9488945578231294,\n",
       " 0.9488945578231294,\n",
       " 0.9345663265306122,\n",
       " 0.95093537414966,\n",
       " 0.95093537414966,\n",
       " 0.9447704081632654,\n",
       " 0.9181122448979592,\n",
       " 0.9181122448979592,\n",
       " 0.9181122448979592,\n",
       " 0.940688775510204,\n",
       " 0.940688775510204,\n",
       " 0.9345663265306122,\n",
       " 0.95093537414966,\n",
       " 0.95093537414966,\n",
       " 0.9447704081632654,\n",
       " 0.9181122448979592,\n",
       " 0.9181122448979592,\n",
       " 0.9181122448979592,\n",
       " 0.940688775510204,\n",
       " 0.940688775510204,\n",
       " 0.9345663265306122,\n",
       " 0.95093537414966,\n",
       " 0.95093537414966,\n",
       " 0.9447704081632654,\n",
       " 0.9181122448979592,\n",
       " 0.9181122448979592,\n",
       " 0.9181122448979592]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sco=[]\n",
    "for i in range(1,28):\n",
    "    reg_tree=DecisionTreeClassifier(max_depth=grid.loc[i,'max_depth'],\n",
    "                               min_samples_leaf=grid.loc[i,'min_samples_leaf'],\n",
    "                               ccp_alpha=grid.loc[i,'ccp_alpha'],random_state=101)\n",
    "    Scores=cross_val_score(reg_tree,X=x_train,y=y_train,\n",
    "                           cv=10,scoring='accuracy')\n",
    "    sco.append(np.mean(Scores))\n",
    "sco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "da72bc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(range(1,28),sco,marker='o',markersize=10,\n",
    "         markerfacecolor='red',color='black',\n",
    "        markeredgecolor='blue',markeredgewidth=2)\n",
    "plt.axvline(np.argmax(sco)+1,linestyle='--',color='black')\n",
    "plt.xlabel('The index of grid',fontsize=14,color='blue')\n",
    "plt.ylabel('accuracy',fontsize=14,color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fe4cfc",
   "metadata": {},
   "source": [
    "# Applying the Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d4d75a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0001, max_depth=4, min_samples_leaf=10,\n",
       "                       random_state=101)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_tree=DecisionTreeClassifier(max_depth=grid.loc[4,'max_depth'],\n",
    "                               min_samples_leaf=grid.loc[4,'min_samples_leaf'],\n",
    "                               ccp_alpha=grid.loc[4,'ccp_alpha'],random_state=101)\n",
    "class_tree.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d6583e2c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_tree=class_tree.predict(x_test)\n",
    "pred_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3d251206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[123,   6],\n",
       "       [  9,  72]], dtype=int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_m=confusion_matrix(y_test,pred_tree)\n",
    "c_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "863a2766",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.953488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Sensitivity  Specificity\n",
       "Logistic Regression  0.952381   0.973333     0.901235     0.984496\n",
       "Decision Tree        0.928571   0.923077     0.888889     0.953488"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TN=c_m[0,0]\n",
    "TP=c_m[1,1]\n",
    "FN=c_m[1,0]\n",
    "FP=c_m[0,1]\n",
    "evaluated_model=evaluated_model.append(pd.DataFrame({'Accuracy':(TN+TP)/(TP+TN+FN+FP),\n",
    "                            'Precision':TP/(TP+FP),\n",
    "                           'Sensitivity':TP/(TP+FN),\n",
    "                           'Specificity':TN/(TN+FP)},\n",
    "                           index=[\"Decision Tree\"]))\n",
    "evaluated_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ad5335",
   "metadata": {},
   "source": [
    "# Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9fe9deaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "bagging_class=RandomForestClassifier(n_estimators=300,max_features=9,random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb87d25",
   "metadata": {},
   "source": [
    "# K-Fold for Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "537e37cb",
   "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>n_estimators</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_estimators  min_samples_leaf\n",
       "1            50                 3\n",
       "2            50                 5\n",
       "3            50                 7\n",
       "4           100                 3\n",
       "5           100                 5\n",
       "6           100                 7\n",
       "7           150                 3\n",
       "8           150                 5\n",
       "9           150                 7"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_estimators=[50,100,150]\n",
    "min_samples_leaf=[3,5,7]\n",
    "grid=list(itertools.product(n_estimators,min_samples_leaf))\n",
    "grid=pd.DataFrame(grid,index=range(1,10),columns=['n_estimators','min_samples_leaf'])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5a420adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.963223227435304,\n",
       " 0.9611824111087733,\n",
       " 0.9591205554386703,\n",
       " 0.963223227435304,\n",
       " 0.9652640437618345,\n",
       " 0.9611613717652009,\n",
       " 0.963223227435304,\n",
       " 0.963223227435304,\n",
       " 0.9591205554386703]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scor=[]\n",
    "for i in range(1,10):\n",
    "    rfr=RandomForestClassifier(n_estimators=grid.loc[i,'n_estimators'],\n",
    "                               min_samples_leaf=grid.loc[i,'min_samples_leaf']\n",
    "                            ,random_state=101\n",
    "                             )\n",
    "    Scores=cross_val_score(rfr,X=x_train,y=y_train,\n",
    "                           cv=5,scoring='accuracy')\n",
    "    scor.append(np.mean(Scores));\n",
    "scor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "99aa6d93",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1abc3f15190>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(range(1,10),scor,marker='o',markersize=10,\n",
    "         markerfacecolor='red',color='black',\n",
    "        markeredgecolor='blue',markeredgewidth=2)\n",
    "plt.axvline(np.argmax(scor)+1,linestyle='--',color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b1c1f7",
   "metadata": {},
   "source": [
    "# The Best Model for Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c448515d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(min_samples_leaf=5, random_state=101)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag=RandomForestClassifier(n_estimators=grid.loc[5,'n_estimators'],\n",
    "                               min_samples_leaf=grid.loc[5,'min_samples_leaf']\n",
    "                           ,random_state=101)\n",
    "bag.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896c0e1b",
   "metadata": {},
   "source": [
    "# Prediction on test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "07085914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_bag=bag.predict(x_test)\n",
    "pred_bag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "61a464f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[127,   2],\n",
       "       [  3,  78]], dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_M=confusion_matrix(y_test,pred_bag)\n",
    "C_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c9f98ea9",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Sensitivity  Specificity\n",
       "Logistic Regression  0.952381   0.973333     0.901235     0.984496\n",
       "Decision Tree        0.928571   0.923077     0.888889     0.953488\n",
       "Bagging              0.976190   0.975000     0.962963     0.984496"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TN=C_M[0,0]\n",
    "TP=C_M[1,1]\n",
    "FN=C_M[1,0]\n",
    "FP=C_M[0,1]\n",
    "evaluated_model=evaluated_model.append(pd.DataFrame({'Accuracy':(TN+TP)/(TP+TN+FN+FP),\n",
    "                            'Precision':TP/(TP+FP),\n",
    "                           'Sensitivity':TP/(TP+FN),\n",
    "                           'Specificity':TN/(TN+FP)},\n",
    "                           index=[\"Bagging\"]))\n",
    "evaluated_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f7c352",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5d722e13",
   "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>n_estimators</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>max_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_estimators  min_samples_leaf  max_features\n",
       "1             50                 3             2\n",
       "2             50                 3             3\n",
       "3             50                 3             4\n",
       "4             50                 5             2\n",
       "5             50                 5             3\n",
       "6             50                 5             4\n",
       "7             50                 7             2\n",
       "8             50                 7             3\n",
       "9             50                 7             4\n",
       "10           100                 3             2\n",
       "11           100                 3             3\n",
       "12           100                 3             4\n",
       "13           100                 5             2\n",
       "14           100                 5             3\n",
       "15           100                 5             4\n",
       "16           100                 7             2\n",
       "17           100                 7             3\n",
       "18           100                 7             4\n",
       "19           150                 3             2\n",
       "20           150                 3             3\n",
       "21           150                 3             4\n",
       "22           150                 5             2\n",
       "23           150                 5             3\n",
       "24           150                 5             4\n",
       "25           150                 7             2\n",
       "26           150                 7             3\n",
       "27           150                 7             4"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC=RandomForestClassifier(n_estimators=100,max_depth=4,max_features=5,min_samples_leaf=1,random_state=101)\n",
    "n_estimators=[50,100,150]\n",
    "min_samples_leaf=[3,5,7]\n",
    "max_features=[2,3,4]\n",
    "grid=list(itertools.product(n_estimators,min_samples_leaf,max_features))\n",
    "grid=pd.DataFrame(grid,index=range(1,28),columns=['n_estimators','min_samples_leaf','max_features'])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f5eaf6da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9611824111087733,\n",
       " 0.963223227435304,\n",
       " 0.963223227435304,\n",
       " 0.963223227435304,\n",
       " 0.9611824111087733,\n",
       " 0.9570797391121397,\n",
       " 0.9632021880917314,\n",
       " 0.9591205554386703,\n",
       " 0.9529981064590786,\n",
       " 0.963223227435304,\n",
       " 0.963223227435304,\n",
       " 0.9591415947822428,\n",
       " 0.9652640437618345,\n",
       " 0.9652640437618345,\n",
       " 0.9570797391121397,\n",
       " 0.9652640437618345,\n",
       " 0.9611613717652009,\n",
       " 0.9570797391121397,\n",
       " 0.963223227435304,\n",
       " 0.963223227435304,\n",
       " 0.963223227435304,\n",
       " 0.9652640437618345,\n",
       " 0.963223227435304,\n",
       " 0.9591205554386703,\n",
       " 0.9652640437618345,\n",
       " 0.9591205554386703,\n",
       " 0.9591205554386703]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC=[]\n",
    "for i in range(1,28):\n",
    "    RFC=RandomForestClassifier(n_estimators=grid.loc[i,'n_estimators'],\n",
    "                               min_samples_leaf=grid.loc[i,'min_samples_leaf'],\n",
    "                               max_features=grid.loc[i,'max_features'],random_state=101\n",
    "                             )\n",
    "    Scores=cross_val_score(RFC,X=x_train,y=y_train.values.ravel(),\n",
    "                           cv=5,scoring='accuracy')\n",
    "    SC.append(np.mean(Scores));\n",
    "SC    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b44ce2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1abc40d32e0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(range(1,28),SC,marker='o',markersize=10,\n",
    "         markerfacecolor='green',linestyle='--',color='black',\n",
    "        markeredgecolor='black')\n",
    "plt.axvline(np.argmax(SC)+1,color='black',linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f5f89f",
   "metadata": {},
   "source": [
    "# The Best Model for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7540e1c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=2, min_samples_leaf=5, random_state=101)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC=RandomForestClassifier(n_estimators=grid.loc[13,'n_estimators'],\n",
    "                               min_samples_leaf=grid.loc[13,'min_samples_leaf'],\n",
    "                               max_features=grid.loc[13,'max_features'],random_state=101)\n",
    "RFC.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0d0bc9",
   "metadata": {},
   "source": [
    "### The Importance of Each Feature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0e71f236",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clump_Thickness</th>\n",
       "      <td>0.073254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <td>0.217785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell_Shape_Uniformity</th>\n",
       "      <td>0.228624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <td>0.057778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_Epi_Cell_Size</th>\n",
       "      <td>0.074018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <td>0.112212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <td>0.145684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <td>0.079342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>0.011302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       importance\n",
       "Clump_Thickness          0.073254\n",
       "Cell_Shape_Uniformity    0.217785\n",
       "Cell_Shape_Uniformity    0.228624\n",
       "Marginal_Adhesion        0.057778\n",
       "Single_Epi_Cell_Size     0.074018\n",
       "Bare_Nuclei              0.112212\n",
       "Bland_Chromatin          0.145684\n",
       "Normal_Nucleoli          0.079342\n",
       "Mitoses                  0.011302"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.feature_importances_\n",
    "importance=pd.DataFrame(RFC.feature_importances_,index=[x_train.columns],columns=[\"importance\"])\n",
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0dcd2d86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "importance.sort_values(by='importance',ascending=True).plot(kind='barh',color='purple',legend=None)\n",
    "plt.xlabel('Importance',color='blue',fontsize=12)\n",
    "plt.ylabel('Features',color='blue',fontsize=12)\n",
    "plt.title('The Importance of variable',color='blue',fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8abb0d6",
   "metadata": {},
   "source": [
    "## Prediction on test data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "26ed85a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_rfc=RFC.predict(x_test)\n",
    "pred_rfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a083d10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[127,   2],\n",
       "       [  3,  78]], dtype=int64)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_M=confusion_matrix(y_test,pred_rfc)\n",
    "C_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "67d6b944",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Sensitivity  Specificity\n",
       "Logistic Regression  0.952381   0.973333     0.901235     0.984496\n",
       "Decision Tree        0.928571   0.923077     0.888889     0.953488\n",
       "Bagging              0.976190   0.975000     0.962963     0.984496\n",
       "Random Forest        0.976190   0.975000     0.962963     0.984496"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TN=C_M[0,0]\n",
    "TP=C_M[1,1]\n",
    "FN=C_M[1,0]\n",
    "FP=C_M[0,1]\n",
    "evaluated_model=evaluated_model.append(pd.DataFrame({'Accuracy':(TN+TP)/(TP+TN+FN+FP),\n",
    "                            'Precision':TP/(TP+FP),\n",
    "                           'Sensitivity':TP/(TP+FN),\n",
    "                           'Specificity':TN/(TN+FP)},\n",
    "                           index=[\"Random Forest\"]))\n",
    "evaluated_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d873bb5a",
   "metadata": {},
   "source": [
    "# Linear Discriminant analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8121fbe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.21488353e-03, 9.97785116e-01],\n",
       "       [9.99999751e-01, 2.48912284e-07],\n",
       "       [9.99996181e-01, 3.81927481e-06],\n",
       "       [9.99997441e-01, 2.55931216e-06],\n",
       "       [4.38462600e-11, 1.00000000e+00],\n",
       "       [9.99594597e-01, 4.05402746e-04],\n",
       "       [8.61215357e-05, 9.99913878e-01],\n",
       "       [9.99987249e-01, 1.27509689e-05],\n",
       "       [1.99438027e-05, 9.99980056e-01],\n",
       "       [9.99998465e-01, 1.53530500e-06],\n",
       "       [5.81054222e-06, 9.99994189e-01],\n",
       "       [9.77640015e-01, 2.23599845e-02],\n",
       "       [6.70331238e-07, 9.99999330e-01],\n",
       "       [9.60704544e-04, 9.99039295e-01],\n",
       "       [9.99989456e-01, 1.05443446e-05],\n",
       "       [9.99998971e-01, 1.02881359e-06],\n",
       "       [9.99366850e-01, 6.33149592e-04],\n",
       "       [9.99840529e-01, 1.59471065e-04],\n",
       "       [8.05193994e-08, 9.99999919e-01],\n",
       "       [3.39915074e-09, 9.99999997e-01],\n",
       "       [3.58340024e-11, 1.00000000e+00],\n",
       "       [6.58652890e-05, 9.99934135e-01],\n",
       "       [9.99986846e-01, 1.31540126e-05],\n",
       "       [9.99998465e-01, 1.53530500e-06],\n",
       "       [9.99999834e-01, 1.66250361e-07],\n",
       "       [1.79872669e-01, 8.20127331e-01],\n",
       "       [1.95080830e-07, 9.99999805e-01],\n",
       "       [9.99992326e-01, 7.67435572e-06],\n",
       "       [9.99997449e-01, 2.55092499e-06],\n",
       "       [9.99998465e-01, 1.53530500e-06],\n",
       "       [9.98557924e-01, 1.44207577e-03],\n",
       "       [1.32687487e-04, 9.99867313e-01],\n",
       "       [5.03515111e-08, 9.99999950e-01],\n",
       "       [9.99257557e-01, 7.42442805e-04],\n",
       "       [3.52721366e-07, 9.99999647e-01],\n",
       "       [9.99933902e-01, 6.60980389e-05],\n",
       "       [1.51568757e-05, 9.99984843e-01],\n",
       "       [9.23495801e-05, 9.99907650e-01],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [9.99963938e-01, 3.60624922e-05],\n",
       "       [9.98836540e-01, 1.16345957e-03],\n",
       "       [9.99998685e-01, 1.31529861e-06],\n",
       "       [3.83338564e-08, 9.99999962e-01],\n",
       "       [9.99982640e-01, 1.73598249e-05],\n",
       "       [2.24553682e-04, 9.99775446e-01],\n",
       "       [9.99998018e-01, 1.98225258e-06],\n",
       "       [4.87579910e-09, 9.99999995e-01],\n",
       "       [7.05041359e-10, 9.99999999e-01],\n",
       "       [9.99997441e-01, 2.55931216e-06],\n",
       "       [9.98967300e-01, 1.03270006e-03],\n",
       "       [9.99999358e-01, 6.41537758e-07],\n",
       "       [9.99999834e-01, 1.66250361e-07],\n",
       "       [2.00578376e-05, 9.99979942e-01],\n",
       "       [3.00386066e-05, 9.99969961e-01],\n",
       "       [9.99999520e-01, 4.79588704e-07],\n",
       "       [4.95979481e-06, 9.99995040e-01],\n",
       "       [9.99999834e-01, 1.66250361e-07],\n",
       "       [3.95101418e-05, 9.99960490e-01],\n",
       "       [1.43549089e-07, 9.99999856e-01],\n",
       "       [9.99986846e-01, 1.31540126e-05],\n",
       "       [9.99998549e-01, 1.45120742e-06],\n",
       "       [9.99989812e-01, 1.01881427e-05],\n",
       "       [9.99995069e-01, 4.93111289e-06],\n",
       "       [9.89274603e-01, 1.07253969e-02],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [1.14881676e-02, 9.88511832e-01],\n",
       "       [9.99999203e-01, 7.96842101e-07],\n",
       "       [1.02144355e-07, 9.99999898e-01],\n",
       "       [9.99999751e-01, 2.48912284e-07],\n",
       "       [9.99998465e-01, 1.53530500e-06],\n",
       "       [1.24828497e-05, 9.99987517e-01],\n",
       "       [9.99993021e-01, 6.97850854e-06],\n",
       "       [9.99998778e-01, 1.22234582e-06],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [1.00562931e-03, 9.98994371e-01],\n",
       "       [2.42387953e-04, 9.99757612e-01],\n",
       "       [4.27791136e-12, 1.00000000e+00],\n",
       "       [1.17345746e-04, 9.99882654e-01],\n",
       "       [9.99998018e-01, 1.98225258e-06],\n",
       "       [9.84184286e-01, 1.58157140e-02],\n",
       "       [5.41612607e-05, 9.99945839e-01],\n",
       "       [9.99988675e-01, 1.13252538e-05],\n",
       "       [4.49948957e-04, 9.99550051e-01],\n",
       "       [9.99983017e-01, 1.69832620e-05],\n",
       "       [9.99995053e-01, 4.94732582e-06],\n",
       "       [9.99999680e-01, 3.20320877e-07],\n",
       "       [9.99912717e-01, 8.72831016e-05],\n",
       "       [3.12348250e-05, 9.99968765e-01],\n",
       "       [9.99994056e-01, 5.94399120e-06],\n",
       "       [9.99996181e-01, 3.81927481e-06],\n",
       "       [2.23287322e-04, 9.99776713e-01],\n",
       "       [9.99998018e-01, 1.98225258e-06],\n",
       "       [9.99998018e-01, 1.98225258e-06],\n",
       "       [9.99936956e-01, 6.30444026e-05],\n",
       "       [9.99997721e-01, 2.27938744e-06],\n",
       "       [9.99973175e-01, 2.68247928e-05],\n",
       "       [9.99910422e-01, 8.95779528e-05],\n",
       "       [9.99993633e-01, 6.36661871e-06],\n",
       "       [9.99795063e-01, 2.04936607e-04],\n",
       "       [1.28954625e-11, 1.00000000e+00],\n",
       "       [9.99998875e-01, 1.12524178e-06],\n",
       "       [9.99999586e-01, 4.13570662e-07],\n",
       "       [9.99999586e-01, 4.13570662e-07],\n",
       "       [9.99791167e-01, 2.08833150e-04],\n",
       "       [9.99997827e-01, 2.17276595e-06],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [9.99999146e-01, 8.54479979e-07],\n",
       "       [1.80354728e-02, 9.81964527e-01],\n",
       "       [9.89177811e-01, 1.08221894e-02],\n",
       "       [9.99999680e-01, 3.20320877e-07],\n",
       "       [9.99991039e-01, 8.96098724e-06],\n",
       "       [9.99820610e-01, 1.79390417e-04],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [6.63869836e-01, 3.36130164e-01],\n",
       "       [9.99999381e-01, 6.19203502e-07],\n",
       "       [2.55831485e-06, 9.99997442e-01],\n",
       "       [7.97374135e-06, 9.99992026e-01],\n",
       "       [2.69893673e-06, 9.99997301e-01],\n",
       "       [9.99999834e-01, 1.66250361e-07],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [9.99981634e-01, 1.83658112e-05],\n",
       "       [9.99997441e-01, 2.55931216e-06],\n",
       "       [9.99999751e-01, 2.48912284e-07],\n",
       "       [9.99999520e-01, 4.79588704e-07],\n",
       "       [9.99954697e-01, 4.53028672e-05],\n",
       "       [9.99951865e-01, 4.81352526e-05],\n",
       "       [3.14686006e-04, 9.99685314e-01],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [9.99991975e-01, 8.02456408e-06],\n",
       "       [9.99980972e-01, 1.90282415e-05],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [3.00515168e-12, 1.00000000e+00],\n",
       "       [9.99999834e-01, 1.66250361e-07],\n",
       "       [1.18260888e-04, 9.99881739e-01],\n",
       "       [2.51327715e-01, 7.48672285e-01],\n",
       "       [9.99997441e-01, 2.55931216e-06],\n",
       "       [1.06427980e-07, 9.99999894e-01],\n",
       "       [9.99999680e-01, 3.20320877e-07],\n",
       "       [6.64107246e-07, 9.99999336e-01],\n",
       "       [1.13040474e-03, 9.98869595e-01],\n",
       "       [9.99887310e-01, 1.12689553e-04],\n",
       "       [9.99956142e-01, 4.38576217e-05],\n",
       "       [5.58072886e-07, 9.99999442e-01],\n",
       "       [9.99999203e-01, 7.96842101e-07],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [1.56914523e-05, 9.99984309e-01],\n",
       "       [5.64230997e-03, 9.94357690e-01],\n",
       "       [9.99963526e-01, 3.64736651e-05],\n",
       "       [9.99997904e-01, 2.09626610e-06],\n",
       "       [2.24599311e-04, 9.99775401e-01],\n",
       "       [2.91292301e-09, 9.99999997e-01],\n",
       "       [9.99999203e-01, 7.96842101e-07],\n",
       "       [9.99999834e-01, 1.66250361e-07],\n",
       "       [3.18932361e-02, 9.68106764e-01],\n",
       "       [1.45972388e-01, 8.54027612e-01],\n",
       "       [1.60778245e-02, 9.83922175e-01],\n",
       "       [3.09044283e-01, 6.90955717e-01],\n",
       "       [1.97174456e-07, 9.99999803e-01],\n",
       "       [2.85014362e-02, 9.71498564e-01],\n",
       "       [9.99994607e-01, 5.39329212e-06],\n",
       "       [9.99999680e-01, 3.20320877e-07],\n",
       "       [1.67212800e-07, 9.99999833e-01],\n",
       "       [2.27807033e-03, 9.97721930e-01],\n",
       "       [9.99999586e-01, 4.13570662e-07],\n",
       "       [9.99843946e-01, 1.56054373e-04],\n",
       "       [1.54543510e-05, 9.99984546e-01],\n",
       "       [3.31380877e-06, 9.99996686e-01],\n",
       "       [2.99667559e-01, 7.00332441e-01],\n",
       "       [9.99879059e-01, 1.20940697e-04],\n",
       "       [6.06237838e-09, 9.99999994e-01],\n",
       "       [9.99997827e-01, 2.17276595e-06],\n",
       "       [9.99964538e-01, 3.54622256e-05],\n",
       "       [9.99995069e-01, 4.93111289e-06],\n",
       "       [9.89961885e-01, 1.00381151e-02],\n",
       "       [9.99998971e-01, 1.02881359e-06],\n",
       "       [9.99996181e-01, 3.81927481e-06],\n",
       "       [9.99996181e-01, 3.81927481e-06],\n",
       "       [2.06006447e-05, 9.99979399e-01],\n",
       "       [5.39827021e-01, 4.60172979e-01],\n",
       "       [2.64338356e-03, 9.97356616e-01],\n",
       "       [9.99999742e-01, 2.57614943e-07],\n",
       "       [9.99993633e-01, 6.36661871e-06],\n",
       "       [5.40010259e-11, 1.00000000e+00],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [2.55383769e-01, 7.44616231e-01],\n",
       "       [9.99996181e-01, 3.81927481e-06],\n",
       "       [8.99664966e-01, 1.00335034e-01],\n",
       "       [9.99998971e-01, 1.02881359e-06],\n",
       "       [9.99993633e-01, 6.36661871e-06],\n",
       "       [1.97777647e-04, 9.99802222e-01],\n",
       "       [1.22440351e-05, 9.99987756e-01],\n",
       "       [9.99140744e-01, 8.59256192e-04],\n",
       "       [1.83801754e-01, 8.16198246e-01],\n",
       "       [9.99993633e-01, 6.36661871e-06],\n",
       "       [6.41088331e-03, 9.93589117e-01],\n",
       "       [9.99994712e-01, 5.28779287e-06],\n",
       "       [9.99984110e-01, 1.58901153e-05],\n",
       "       [4.60442230e-08, 9.99999954e-01],\n",
       "       [9.99998018e-01, 1.98225258e-06],\n",
       "       [9.99982678e-01, 1.73220990e-05],\n",
       "       [9.99998817e-01, 1.18303042e-06],\n",
       "       [5.47405230e-06, 9.99994526e-01],\n",
       "       [9.99831842e-01, 1.68158087e-04],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [9.99999383e-01, 6.17174296e-07],\n",
       "       [9.99999667e-01, 3.32965810e-07],\n",
       "       [9.99996181e-01, 3.81927481e-06],\n",
       "       [1.18390010e-06, 9.99998816e-01],\n",
       "       [9.99998018e-01, 1.98225258e-06],\n",
       "       [9.99999871e-01, 1.29188444e-07]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda=LinearDiscriminantAnalysis()\n",
    "lda.fit(x_train,y_train)\n",
    "lda.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ed810066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_lda=lda.predict(x_test)\n",
    "pred_lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d0a84ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[127,   2],\n",
       "       [  5,  76]], dtype=int64)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_M=confusion_matrix(y_test,pred_lda)\n",
    "C_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1f9e893b",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDA</th>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Sensitivity  Specificity\n",
       "Logistic Regression  0.952381   0.973333     0.901235     0.984496\n",
       "Decision Tree        0.928571   0.923077     0.888889     0.953488\n",
       "Bagging              0.976190   0.975000     0.962963     0.984496\n",
       "Random Forest        0.976190   0.975000     0.962963     0.984496\n",
       "LDA                  0.966667   0.974359     0.938272     0.984496"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TN=C_M[0,0]\n",
    "TP=C_M[1,1]\n",
    "FN=C_M[1,0]\n",
    "FP=C_M[0,1]\n",
    "evaluated_model=evaluated_model.append(pd.DataFrame({'Accuracy':(TN+TP)/(TP+TN+FN+FP),\n",
    "                            'Precision':TP/(TP+FP),\n",
    "                           'Sensitivity':TP/(TP+FN),\n",
    "                           'Specificity':TN/(TN+FP)},\n",
    "                           index=[\"LDA\"]))\n",
    "evaluated_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f931dfa",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "510c4640",
   "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>C</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C  degree\n",
       "1  1       2\n",
       "2  1       3\n",
       "3  1       4\n",
       "4  2       2\n",
       "5  2       3\n",
       "6  2       4\n",
       "7  3       2\n",
       "8  3       3\n",
       "9  3       4"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc=SVC(C=1.0,\n",
    "        kernel='poly', \n",
    "        degree=3,\n",
    "random_state=101)\n",
    "C=[1,2,3]\n",
    "degree=[2,3,4]  \n",
    "grid=itertools.product(C,degree)\n",
    "grid=pd.DataFrame(grid,index=range(1,10),columns=['C','degree'])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b6a39177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9632227891156461,\n",
       " 0.9632227891156461,\n",
       " 0.9632227891156461,\n",
       " 0.969345238095238,\n",
       " 0.969345238095238,\n",
       " 0.969345238095238,\n",
       " 0.9673044217687075,\n",
       " 0.9673044217687075,\n",
       " 0.9673044217687075]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scsvc=[]\n",
    "for i in range(0,9):\n",
    "    svc=SVC(C=grid.iloc[i,0],\n",
    "           degree=grid.iloc[i,1],random_state=101)\n",
    "    scores=cross_val_score(estimator=svc,X=x_train,\n",
    "                          y=y_train,\n",
    "                          cv=10,scoring='accuracy')\n",
    "    scsvc.append(np.mean(scores))\n",
    "scsvc    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "58a38306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1abc6bdd340>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(range(1,10),scsvc,\n",
    "        marker='o',markersize=10,markerfacecolor='red',markeredgecolor='black',\n",
    "        markeredgewidth=2)\n",
    "plt.axvline(np.argmax(scsvc)+1,color='black',linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d4b9d371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc=SVC(C=grid.iloc[4,0],degree=grid.iloc[4,1],random_state=101)\n",
    "svc.fit(x_train,y_train)\n",
    "pred_svc=svc.predict(x_test)\n",
    "pred_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "15acb00a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[125,   4],\n",
       "       [  3,  78]], dtype=int64)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_M=confusion_matrix(y_test,pred_svc)\n",
    "C_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "0be48278",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDA</th>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.968992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Sensitivity  Specificity\n",
       "Logistic Regression  0.952381   0.973333     0.901235     0.984496\n",
       "Decision Tree        0.928571   0.923077     0.888889     0.953488\n",
       "Bagging              0.976190   0.975000     0.962963     0.984496\n",
       "Random Forest        0.976190   0.975000     0.962963     0.984496\n",
       "LDA                  0.966667   0.974359     0.938272     0.984496\n",
       "SVM                  0.966667   0.951220     0.962963     0.968992"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TN=C_M[0,0]\n",
    "TP=C_M[1,1]\n",
    "FN=C_M[1,0]\n",
    "FP=C_M[0,1]\n",
    "evaluated_model=evaluated_model.append(pd.DataFrame({'Accuracy':(TN+TP)/(TP+TN+FN+FP),\n",
    "                            'Precision':TP/(TP+FP),\n",
    "                           'Sensitivity':TP/(TP+FN),\n",
    "                           'Specificity':TN/(TN+FP)},\n",
    "                           index=[\"SVM\"]))\n",
    "evaluated_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02402af2",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "af50ecb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(x_train,y_train)\n",
    "pred_knn=knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3020b46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[127,   2],\n",
       "       [  4,  77]], dtype=int64)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_M=confusion_matrix(y_test,pred_knn)\n",
    "C_M\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "0f01c4da",
   "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDA</th>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.968992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.974684</td>\n",
       "      <td>0.950617</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Sensitivity  Specificity\n",
       "Logistic Regression  0.952381   0.973333     0.901235     0.984496\n",
       "Decision Tree        0.928571   0.923077     0.888889     0.953488\n",
       "Bagging              0.976190   0.975000     0.962963     0.984496\n",
       "Random Forest        0.976190   0.975000     0.962963     0.984496\n",
       "LDA                  0.966667   0.974359     0.938272     0.984496\n",
       "SVM                  0.966667   0.951220     0.962963     0.968992\n",
       "KNN                  0.971429   0.974684     0.950617     0.984496"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TN=C_M[0,0]\n",
    "TP=C_M[1,1]\n",
    "FN=C_M[1,0]\n",
    "FP=C_M[0,1]\n",
    "evaluated_model=evaluated_model.append(pd.DataFrame({'Accuracy':(TN+TP)/(TP+TN+FN+FP),\n",
    "                            'Precision':TP/(TP+FP),\n",
    "                           'Sensitivity':TP/(TP+FN),\n",
    "                           'Specificity':TN/(TN+FP)},\n",
    "                           index=[\"KNN\"]))\n",
    "evaluated_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d85dbc",
   "metadata": {},
   "source": [
    "# The Best Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2344c416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag.fit(x_train,y_train)\n",
    "pred_bag=bag.predict(x_test)\n",
    "pred_bag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "883bd8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[125,   4],\n",
       "       [  3,  78]], dtype=int64)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_M=confusion_matrix(y_test,pred_svc)\n",
    "C_M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "ac2f5c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TN=C_M[0,0]\n",
    "TP=C_M[1,1]\n",
    "FN=C_M[1,0]\n",
    "FP=C_M[0,1]\n",
    "Accuracy=(TN+TP)/(TP+TN+FN+FP)\n",
    "Accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "3a36114d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9512195121951219"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Precision=TP/(TP+FP)\n",
    "Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "a7564524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9629629629629629"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sensitivity=TP/(TP+FN)\n",
    "Sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3e589afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9689922480620154"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Specificity=TN/(TN+FP)\n",
    "Specificity"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}