Ensemble_BreastCancerPred_LMS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2183229b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import svm \n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d997478",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r'C:\\Users\\Bushra Ansari\\Desktop\\LMS_DATASET\\SVM\\Breast Cancer Prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48dd03f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cancer.csv', 'Problem Statement_EnsembleTechniques.pdf']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b57aa0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>842302</td>\n",
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       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer = pd.read_csv('cancer.csv')\n",
    "cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6603617d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "cancer.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b527a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    31\n",
       "int64       1\n",
       "object      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e75a98b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0206dfee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           0\n",
       "diagnosis                    0\n",
       "radius_mean                  0\n",
       "texture_mean                 0\n",
       "perimeter_mean               0\n",
       "area_mean                    0\n",
       "smoothness_mean              0\n",
       "compactness_mean             0\n",
       "concavity_mean               0\n",
       "concave points_mean          0\n",
       "symmetry_mean                0\n",
       "fractal_dimension_mean       0\n",
       "radius_se                    0\n",
       "texture_se                   0\n",
       "perimeter_se                 0\n",
       "area_se                      0\n",
       "smoothness_se                0\n",
       "compactness_se               0\n",
       "concavity_se                 0\n",
       "concave points_se            0\n",
       "symmetry_se                  0\n",
       "fractal_dimension_se         0\n",
       "radius_worst                 0\n",
       "texture_worst                0\n",
       "perimeter_worst              0\n",
       "area_worst                   0\n",
       "smoothness_worst             0\n",
       "compactness_worst            0\n",
       "concavity_worst              0\n",
       "concave points_worst         0\n",
       "symmetry_worst               0\n",
       "fractal_dimension_worst      0\n",
       "Unnamed: 32                569\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42e3c292",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer.drop(['Unnamed: 32', 'id'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "604c9cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target Variable\n",
    "cancer['diagnosis'].unique()\n",
    "# B=patient has no cancer, M=patient has cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c52ba6cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace B with 0 and M with 1\n",
    "cancer['diagnosis'].replace({'B' : '0', 'M' : '1'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "204d563e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cancer['diagnosis'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', figsize = [5,5], explode = [0,0.05],\n",
    "                                       colors = ['#CAFF70', '#AB82FF'], shadow = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99b08e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "665cef1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking correlation\n",
    "corr = cancer.corr()\n",
    "kot = corr[corr>=.7]\n",
    "plt.figure(figsize = [15,10])\n",
    "sns.heatmap(kot, annot = True, cmap = 'YlOrRd', fmt = '0.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c3eb455",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>2%</th>\n",
       "      <th>3%</th>\n",
       "      <th>4%</th>\n",
       "      <th>5%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "      <th>96%</th>\n",
       "      <th>97%</th>\n",
       "      <th>98%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>radius_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>8.458360</td>\n",
       "      <td>8.728880</td>\n",
       "      <td>9.029520</td>\n",
       "      <td>9.379080</td>\n",
       "      <td>9.529200</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>20.576000</td>\n",
       "      <td>20.925600</td>\n",
       "      <td>21.552400</td>\n",
       "      <td>22.794800</td>\n",
       "      <td>24.371600</td>\n",
       "      <td>28.11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>10.930400</td>\n",
       "      <td>12.188000</td>\n",
       "      <td>12.711200</td>\n",
       "      <td>12.901600</td>\n",
       "      <td>13.088000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>27.922000</td>\n",
       "      <td>28.209600</td>\n",
       "      <td>29.315600</td>\n",
       "      <td>30.652000</td>\n",
       "      <td>39.28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>53.827600</td>\n",
       "      <td>55.475200</td>\n",
       "      <td>58.798800</td>\n",
       "      <td>59.800400</td>\n",
       "      <td>60.496000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>135.820000</td>\n",
       "      <td>139.236000</td>\n",
       "      <td>142.988000</td>\n",
       "      <td>152.100000</td>\n",
       "      <td>165.724000</td>\n",
       "      <td>188.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>215.664000</td>\n",
       "      <td>232.124000</td>\n",
       "      <td>246.396000</td>\n",
       "      <td>267.456000</td>\n",
       "      <td>275.780000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>1309.800000</td>\n",
       "      <td>1351.760000</td>\n",
       "      <td>1418.520000</td>\n",
       "      <td>1625.360000</td>\n",
       "      <td>1786.600000</td>\n",
       "      <td>2501.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.068654</td>\n",
       "      <td>0.070126</td>\n",
       "      <td>0.073512</td>\n",
       "      <td>0.074346</td>\n",
       "      <td>0.075042</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.118780</td>\n",
       "      <td>0.121656</td>\n",
       "      <td>0.123696</td>\n",
       "      <td>0.126724</td>\n",
       "      <td>0.132888</td>\n",
       "      <td>0.16340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.033351</td>\n",
       "      <td>0.035884</td>\n",
       "      <td>0.037663</td>\n",
       "      <td>0.038614</td>\n",
       "      <td>0.040660</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.208700</td>\n",
       "      <td>0.219392</td>\n",
       "      <td>0.227452</td>\n",
       "      <td>0.240688</td>\n",
       "      <td>0.277192</td>\n",
       "      <td>0.34540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>0.002677</td>\n",
       "      <td>0.004983</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.243020</td>\n",
       "      <td>0.254640</td>\n",
       "      <td>0.286856</td>\n",
       "      <td>0.317528</td>\n",
       "      <td>0.351688</td>\n",
       "      <td>0.42680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003130</td>\n",
       "      <td>0.004874</td>\n",
       "      <td>0.005621</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.125740</td>\n",
       "      <td>0.131336</td>\n",
       "      <td>0.146664</td>\n",
       "      <td>0.150292</td>\n",
       "      <td>0.164208</td>\n",
       "      <td>0.20120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.129508</td>\n",
       "      <td>0.134936</td>\n",
       "      <td>0.137308</td>\n",
       "      <td>0.138772</td>\n",
       "      <td>0.141500</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.230720</td>\n",
       "      <td>0.237584</td>\n",
       "      <td>0.239796</td>\n",
       "      <td>0.253188</td>\n",
       "      <td>0.259564</td>\n",
       "      <td>0.30400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.051504</td>\n",
       "      <td>0.052473</td>\n",
       "      <td>0.053072</td>\n",
       "      <td>0.053394</td>\n",
       "      <td>0.053926</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>0.076090</td>\n",
       "      <td>0.077114</td>\n",
       "      <td>0.078173</td>\n",
       "      <td>0.080831</td>\n",
       "      <td>0.085438</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>0.277313</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.119740</td>\n",
       "      <td>0.136488</td>\n",
       "      <td>0.148556</td>\n",
       "      <td>0.154856</td>\n",
       "      <td>0.160100</td>\n",
       "      <td>0.232400</td>\n",
       "      <td>0.324200</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>0.959520</td>\n",
       "      <td>0.996256</td>\n",
       "      <td>1.057520</td>\n",
       "      <td>1.170200</td>\n",
       "      <td>1.291320</td>\n",
       "      <td>2.87300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>1.216853</td>\n",
       "      <td>0.551648</td>\n",
       "      <td>0.360200</td>\n",
       "      <td>0.410548</td>\n",
       "      <td>0.469576</td>\n",
       "      <td>0.489264</td>\n",
       "      <td>0.502348</td>\n",
       "      <td>0.540140</td>\n",
       "      <td>0.833900</td>\n",
       "      <td>1.108000</td>\n",
       "      <td>1.474000</td>\n",
       "      <td>2.212000</td>\n",
       "      <td>2.301680</td>\n",
       "      <td>2.506200</td>\n",
       "      <td>2.656440</td>\n",
       "      <td>2.915440</td>\n",
       "      <td>4.88500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>2.866059</td>\n",
       "      <td>2.021855</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>0.953248</td>\n",
       "      <td>1.019640</td>\n",
       "      <td>1.069840</td>\n",
       "      <td>1.102720</td>\n",
       "      <td>1.132800</td>\n",
       "      <td>1.606000</td>\n",
       "      <td>2.287000</td>\n",
       "      <td>3.357000</td>\n",
       "      <td>7.041600</td>\n",
       "      <td>7.255120</td>\n",
       "      <td>7.730600</td>\n",
       "      <td>8.627400</td>\n",
       "      <td>9.690040</td>\n",
       "      <td>21.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>40.337079</td>\n",
       "      <td>45.491006</td>\n",
       "      <td>6.802000</td>\n",
       "      <td>8.514440</td>\n",
       "      <td>9.370160</td>\n",
       "      <td>9.842880</td>\n",
       "      <td>10.791600</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>24.530000</td>\n",
       "      <td>45.190000</td>\n",
       "      <td>115.800000</td>\n",
       "      <td>122.888000</td>\n",
       "      <td>134.728000</td>\n",
       "      <td>154.936000</td>\n",
       "      <td>177.684000</td>\n",
       "      <td>542.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.003284</td>\n",
       "      <td>0.003444</td>\n",
       "      <td>0.003603</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.005169</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.012644</td>\n",
       "      <td>0.012955</td>\n",
       "      <td>0.013848</td>\n",
       "      <td>0.015280</td>\n",
       "      <td>0.017258</td>\n",
       "      <td>0.03113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.025478</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.004705</td>\n",
       "      <td>0.005628</td>\n",
       "      <td>0.006099</td>\n",
       "      <td>0.006866</td>\n",
       "      <td>0.007892</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.032450</td>\n",
       "      <td>0.060578</td>\n",
       "      <td>0.066088</td>\n",
       "      <td>0.070548</td>\n",
       "      <td>0.080392</td>\n",
       "      <td>0.089872</td>\n",
       "      <td>0.13540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>0.001982</td>\n",
       "      <td>0.003253</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.078936</td>\n",
       "      <td>0.081787</td>\n",
       "      <td>0.092626</td>\n",
       "      <td>0.101584</td>\n",
       "      <td>0.122292</td>\n",
       "      <td>0.39600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.011796</td>\n",
       "      <td>0.006170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002948</td>\n",
       "      <td>0.003457</td>\n",
       "      <td>0.003831</td>\n",
       "      <td>0.007638</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.022884</td>\n",
       "      <td>0.024557</td>\n",
       "      <td>0.025907</td>\n",
       "      <td>0.027688</td>\n",
       "      <td>0.031194</td>\n",
       "      <td>0.05279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.010547</td>\n",
       "      <td>0.010690</td>\n",
       "      <td>0.011034</td>\n",
       "      <td>0.011447</td>\n",
       "      <td>0.011758</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.018730</td>\n",
       "      <td>0.023480</td>\n",
       "      <td>0.034988</td>\n",
       "      <td>0.037568</td>\n",
       "      <td>0.040748</td>\n",
       "      <td>0.044936</td>\n",
       "      <td>0.052208</td>\n",
       "      <td>0.07895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>0.001522</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>0.007960</td>\n",
       "      <td>0.008745</td>\n",
       "      <td>0.009624</td>\n",
       "      <td>0.011415</td>\n",
       "      <td>0.012650</td>\n",
       "      <td>0.02984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>9.207600</td>\n",
       "      <td>9.587680</td>\n",
       "      <td>9.968520</td>\n",
       "      <td>10.266000</td>\n",
       "      <td>10.534000</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>25.640000</td>\n",
       "      <td>26.053600</td>\n",
       "      <td>27.296400</td>\n",
       "      <td>28.324400</td>\n",
       "      <td>30.762800</td>\n",
       "      <td>36.04000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>15.200800</td>\n",
       "      <td>15.663600</td>\n",
       "      <td>15.922000</td>\n",
       "      <td>16.336000</td>\n",
       "      <td>16.574000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>37.138400</td>\n",
       "      <td>38.043200</td>\n",
       "      <td>39.275200</td>\n",
       "      <td>41.802400</td>\n",
       "      <td>49.54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>58.270400</td>\n",
       "      <td>62.361600</td>\n",
       "      <td>64.506000</td>\n",
       "      <td>66.287200</td>\n",
       "      <td>67.856000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>171.640000</td>\n",
       "      <td>177.736000</td>\n",
       "      <td>184.584000</td>\n",
       "      <td>195.576000</td>\n",
       "      <td>208.304000</td>\n",
       "      <td>251.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>256.192000</td>\n",
       "      <td>278.768000</td>\n",
       "      <td>300.232000</td>\n",
       "      <td>316.412000</td>\n",
       "      <td>331.060000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>2009.600000</td>\n",
       "      <td>2075.240000</td>\n",
       "      <td>2231.800000</td>\n",
       "      <td>2491.080000</td>\n",
       "      <td>2918.160000</td>\n",
       "      <td>4254.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.087910</td>\n",
       "      <td>0.090878</td>\n",
       "      <td>0.093686</td>\n",
       "      <td>0.094342</td>\n",
       "      <td>0.095734</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.171840</td>\n",
       "      <td>0.177052</td>\n",
       "      <td>0.178888</td>\n",
       "      <td>0.185228</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.22260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.050094</td>\n",
       "      <td>0.054626</td>\n",
       "      <td>0.064453</td>\n",
       "      <td>0.067778</td>\n",
       "      <td>0.071196</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.564120</td>\n",
       "      <td>0.592260</td>\n",
       "      <td>0.616184</td>\n",
       "      <td>0.687424</td>\n",
       "      <td>0.778644</td>\n",
       "      <td>1.05800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005633</td>\n",
       "      <td>0.012028</td>\n",
       "      <td>0.018360</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.682380</td>\n",
       "      <td>0.696580</td>\n",
       "      <td>0.723708</td>\n",
       "      <td>0.783260</td>\n",
       "      <td>0.902380</td>\n",
       "      <td>1.25200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011221</td>\n",
       "      <td>0.020659</td>\n",
       "      <td>0.024286</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.236920</td>\n",
       "      <td>0.244040</td>\n",
       "      <td>0.250796</td>\n",
       "      <td>0.258652</td>\n",
       "      <td>0.269216</td>\n",
       "      <td>0.29100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.176028</td>\n",
       "      <td>0.190136</td>\n",
       "      <td>0.198704</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.212700</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.406160</td>\n",
       "      <td>0.417192</td>\n",
       "      <td>0.436216</td>\n",
       "      <td>0.467448</td>\n",
       "      <td>0.486908</td>\n",
       "      <td>0.66380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.058580</td>\n",
       "      <td>0.059727</td>\n",
       "      <td>0.060852</td>\n",
       "      <td>0.061647</td>\n",
       "      <td>0.062558</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>0.119520</td>\n",
       "      <td>0.123496</td>\n",
       "      <td>0.125200</td>\n",
       "      <td>0.132388</td>\n",
       "      <td>0.140628</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count        mean         std         min  \\\n",
       "radius_mean              569.0   14.127292    3.524049    6.981000   \n",
       "texture_mean             569.0   19.289649    4.301036    9.710000   \n",
       "perimeter_mean           569.0   91.969033   24.298981   43.790000   \n",
       "area_mean                569.0  654.889104  351.914129  143.500000   \n",
       "smoothness_mean          569.0    0.096360    0.014064    0.052630   \n",
       "compactness_mean         569.0    0.104341    0.052813    0.019380   \n",
       "concavity_mean           569.0    0.088799    0.079720    0.000000   \n",
       "concave points_mean      569.0    0.048919    0.038803    0.000000   \n",
       "symmetry_mean            569.0    0.181162    0.027414    0.106000   \n",
       "fractal_dimension_mean   569.0    0.062798    0.007060    0.049960   \n",
       "radius_se                569.0    0.405172    0.277313    0.111500   \n",
       "texture_se               569.0    1.216853    0.551648    0.360200   \n",
       "perimeter_se             569.0    2.866059    2.021855    0.757000   \n",
       "area_se                  569.0   40.337079   45.491006    6.802000   \n",
       "smoothness_se            569.0    0.007041    0.003003    0.001713   \n",
       "compactness_se           569.0    0.025478    0.017908    0.002252   \n",
       "concavity_se             569.0    0.031894    0.030186    0.000000   \n",
       "concave points_se        569.0    0.011796    0.006170    0.000000   \n",
       "symmetry_se              569.0    0.020542    0.008266    0.007882   \n",
       "fractal_dimension_se     569.0    0.003795    0.002646    0.000895   \n",
       "radius_worst             569.0   16.269190    4.833242    7.930000   \n",
       "texture_worst            569.0   25.677223    6.146258   12.020000   \n",
       "perimeter_worst          569.0  107.261213   33.602542   50.410000   \n",
       "area_worst               569.0  880.583128  569.356993  185.200000   \n",
       "smoothness_worst         569.0    0.132369    0.022832    0.071170   \n",
       "compactness_worst        569.0    0.254265    0.157336    0.027290   \n",
       "concavity_worst          569.0    0.272188    0.208624    0.000000   \n",
       "concave points_worst     569.0    0.114606    0.065732    0.000000   \n",
       "symmetry_worst           569.0    0.290076    0.061867    0.156500   \n",
       "fractal_dimension_worst  569.0    0.083946    0.018061    0.055040   \n",
       "\n",
       "                                 1%          2%          3%          4%  \\\n",
       "radius_mean                8.458360    8.728880    9.029520    9.379080   \n",
       "texture_mean              10.930400   12.188000   12.711200   12.901600   \n",
       "perimeter_mean            53.827600   55.475200   58.798800   59.800400   \n",
       "area_mean                215.664000  232.124000  246.396000  267.456000   \n",
       "smoothness_mean            0.068654    0.070126    0.073512    0.074346   \n",
       "compactness_mean           0.033351    0.035884    0.037663    0.038614   \n",
       "concavity_mean             0.000000    0.000000    0.001489    0.002677   \n",
       "concave points_mean        0.000000    0.000000    0.003130    0.004874   \n",
       "symmetry_mean              0.129508    0.134936    0.137308    0.138772   \n",
       "fractal_dimension_mean     0.051504    0.052473    0.053072    0.053394   \n",
       "radius_se                  0.119740    0.136488    0.148556    0.154856   \n",
       "texture_se                 0.410548    0.469576    0.489264    0.502348   \n",
       "perimeter_se               0.953248    1.019640    1.069840    1.102720   \n",
       "area_se                    8.514440    9.370160    9.842880   10.791600   \n",
       "smoothness_se              0.003058    0.003284    0.003444    0.003603   \n",
       "compactness_se             0.004705    0.005628    0.006099    0.006866   \n",
       "concavity_se               0.000000    0.000000    0.001196    0.001982   \n",
       "concave points_se          0.000000    0.000000    0.002948    0.003457   \n",
       "symmetry_se                0.010547    0.010690    0.011034    0.011447   \n",
       "fractal_dimension_se       0.001114    0.001321    0.001367    0.001456   \n",
       "radius_worst               9.207600    9.587680    9.968520   10.266000   \n",
       "texture_worst             15.200800   15.663600   15.922000   16.336000   \n",
       "perimeter_worst           58.270400   62.361600   64.506000   66.287200   \n",
       "area_worst               256.192000  278.768000  300.232000  316.412000   \n",
       "smoothness_worst           0.087910    0.090878    0.093686    0.094342   \n",
       "compactness_worst          0.050094    0.054626    0.064453    0.067778   \n",
       "concavity_worst            0.000000    0.000000    0.005633    0.012028   \n",
       "concave points_worst       0.000000    0.000000    0.011221    0.020659   \n",
       "symmetry_worst             0.176028    0.190136    0.198704    0.208160   \n",
       "fractal_dimension_worst    0.058580    0.059727    0.060852    0.061647   \n",
       "\n",
       "                                 5%         25%         50%          75%  \\\n",
       "radius_mean                9.529200   11.700000   13.370000    15.780000   \n",
       "texture_mean              13.088000   16.170000   18.840000    21.800000   \n",
       "perimeter_mean            60.496000   75.170000   86.240000   104.100000   \n",
       "area_mean                275.780000  420.300000  551.100000   782.700000   \n",
       "smoothness_mean            0.075042    0.086370    0.095870     0.105300   \n",
       "compactness_mean           0.040660    0.064920    0.092630     0.130400   \n",
       "concavity_mean             0.004983    0.029560    0.061540     0.130700   \n",
       "concave points_mean        0.005621    0.020310    0.033500     0.074000   \n",
       "symmetry_mean              0.141500    0.161900    0.179200     0.195700   \n",
       "fractal_dimension_mean     0.053926    0.057700    0.061540     0.066120   \n",
       "radius_se                  0.160100    0.232400    0.324200     0.478900   \n",
       "texture_se                 0.540140    0.833900    1.108000     1.474000   \n",
       "perimeter_se               1.132800    1.606000    2.287000     3.357000   \n",
       "area_se                   11.360000   17.850000   24.530000    45.190000   \n",
       "smoothness_se              0.003690    0.005169    0.006380     0.008146   \n",
       "compactness_se             0.007892    0.013080    0.020450     0.032450   \n",
       "concavity_se               0.003253    0.015090    0.025890     0.042050   \n",
       "concave points_se          0.003831    0.007638    0.010930     0.014710   \n",
       "symmetry_se                0.011758    0.015160    0.018730     0.023480   \n",
       "fractal_dimension_se       0.001522    0.002248    0.003187     0.004558   \n",
       "radius_worst              10.534000   13.010000   14.970000    18.790000   \n",
       "texture_worst             16.574000   21.080000   25.410000    29.720000   \n",
       "perimeter_worst           67.856000   84.110000   97.660000   125.400000   \n",
       "area_worst               331.060000  515.300000  686.500000  1084.000000   \n",
       "smoothness_worst           0.095734    0.116600    0.131300     0.146000   \n",
       "compactness_worst          0.071196    0.147200    0.211900     0.339100   \n",
       "concavity_worst            0.018360    0.114500    0.226700     0.382900   \n",
       "concave points_worst       0.024286    0.064930    0.099930     0.161400   \n",
       "symmetry_worst             0.212700    0.250400    0.282200     0.317900   \n",
       "fractal_dimension_worst    0.062558    0.071460    0.080040     0.092080   \n",
       "\n",
       "                                 95%          96%          97%          98%  \\\n",
       "radius_mean                20.576000    20.925600    21.552400    22.794800   \n",
       "texture_mean               27.150000    27.922000    28.209600    29.315600   \n",
       "perimeter_mean            135.820000   139.236000   142.988000   152.100000   \n",
       "area_mean                1309.800000  1351.760000  1418.520000  1625.360000   \n",
       "smoothness_mean             0.118780     0.121656     0.123696     0.126724   \n",
       "compactness_mean            0.208700     0.219392     0.227452     0.240688   \n",
       "concavity_mean              0.243020     0.254640     0.286856     0.317528   \n",
       "concave points_mean         0.125740     0.131336     0.146664     0.150292   \n",
       "symmetry_mean               0.230720     0.237584     0.239796     0.253188   \n",
       "fractal_dimension_mean      0.076090     0.077114     0.078173     0.080831   \n",
       "radius_se                   0.959520     0.996256     1.057520     1.170200   \n",
       "texture_se                  2.212000     2.301680     2.506200     2.656440   \n",
       "perimeter_se                7.041600     7.255120     7.730600     8.627400   \n",
       "area_se                   115.800000   122.888000   134.728000   154.936000   \n",
       "smoothness_se               0.012644     0.012955     0.013848     0.015280   \n",
       "compactness_se              0.060578     0.066088     0.070548     0.080392   \n",
       "concavity_se                0.078936     0.081787     0.092626     0.101584   \n",
       "concave points_se           0.022884     0.024557     0.025907     0.027688   \n",
       "symmetry_se                 0.034988     0.037568     0.040748     0.044936   \n",
       "fractal_dimension_se        0.007960     0.008745     0.009624     0.011415   \n",
       "radius_worst               25.640000    26.053600    27.296400    28.324400   \n",
       "texture_worst              36.300000    37.138400    38.043200    39.275200   \n",
       "perimeter_worst           171.640000   177.736000   184.584000   195.576000   \n",
       "area_worst               2009.600000  2075.240000  2231.800000  2491.080000   \n",
       "smoothness_worst            0.171840     0.177052     0.178888     0.185228   \n",
       "compactness_worst           0.564120     0.592260     0.616184     0.687424   \n",
       "concavity_worst             0.682380     0.696580     0.723708     0.783260   \n",
       "concave points_worst        0.236920     0.244040     0.250796     0.258652   \n",
       "symmetry_worst              0.406160     0.417192     0.436216     0.467448   \n",
       "fractal_dimension_worst     0.119520     0.123496     0.125200     0.132388   \n",
       "\n",
       "                                 99%         max  \n",
       "radius_mean                24.371600    28.11000  \n",
       "texture_mean               30.652000    39.28000  \n",
       "perimeter_mean            165.724000   188.50000  \n",
       "area_mean                1786.600000  2501.00000  \n",
       "smoothness_mean             0.132888     0.16340  \n",
       "compactness_mean            0.277192     0.34540  \n",
       "concavity_mean              0.351688     0.42680  \n",
       "concave points_mean         0.164208     0.20120  \n",
       "symmetry_mean               0.259564     0.30400  \n",
       "fractal_dimension_mean      0.085438     0.09744  \n",
       "radius_se                   1.291320     2.87300  \n",
       "texture_se                  2.915440     4.88500  \n",
       "perimeter_se                9.690040    21.98000  \n",
       "area_se                   177.684000   542.20000  \n",
       "smoothness_se               0.017258     0.03113  \n",
       "compactness_se              0.089872     0.13540  \n",
       "concavity_se                0.122292     0.39600  \n",
       "concave points_se           0.031194     0.05279  \n",
       "symmetry_se                 0.052208     0.07895  \n",
       "fractal_dimension_se        0.012650     0.02984  \n",
       "radius_worst               30.762800    36.04000  \n",
       "texture_worst              41.802400    49.54000  \n",
       "perimeter_worst           208.304000   251.20000  \n",
       "area_worst               2918.160000  4254.00000  \n",
       "smoothness_worst            0.188908     0.22260  \n",
       "compactness_worst           0.778644     1.05800  \n",
       "concavity_worst             0.902380     1.25200  \n",
       "concave points_worst        0.269216     0.29100  \n",
       "symmetry_worst              0.486908     0.66380  \n",
       "fractal_dimension_worst     0.140628     0.20750  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Outliers\n",
    "cancer.describe(percentiles = [0.01, 0.02, 0.03, 0.04, 0.05, 0.25, 0.5, 0.75, 0.95, 0.96, 0.97, 0.98, 0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d6207a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outliers(x):\n",
    "    x=x.clip(upper=x.quantile(.99))\n",
    "    x=x.clip(lower=x.quantile(.01))\n",
    "    return x "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "57a07445",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer1 = cancer.copy()\n",
    "cancer1.drop(['diagnosis'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5764dbfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer1 = cancer1.apply(lambda x : outliers(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dacc723d",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = cancer['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04a379f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_cleaned = pd.concat([target, cancer1], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cae5ca09",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_dummy = pd.get_dummies(cancer_cleaned, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9955964a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = cancer_dummy['diagnosis_1']\n",
    "x = cancer_dummy.drop(columns=['diagnosis_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "152cc1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d695c03b",
   "metadata": {},
   "source": [
    "#### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3a05e91c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "sc_fit = sc.fit(x_train)\n",
    "\n",
    "x_train_sc = sc_fit.transform(x_train)\n",
    "x_test_sc = sc_fit.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3c1a1ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_sc_df = pd.DataFrame(x_train_sc, columns=x_train.columns)\n",
    "x_test_sc_df = pd.DataFrame(x_test_sc, columns=x_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d9d6d3a",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0e7a043c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 216 candidates, totalling 2160 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
       "                         'degree': [1, 2, 3, 4], 'gamma': [0.1, 1, 0.001],\n",
       "                         'kernel': ['linear', 'poly', 'rbf']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using GridSearchCV for hyperparameter tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {\"kernel\":['linear', 'poly', 'rbf'],\n",
    "       \"degree\":[1,2,3,4],\n",
    "       \"gamma\":[0.1, 1,.001],\n",
    "       \"C\":[0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "\n",
    "sv = svm.SVC()\n",
    "svm_rs = GridSearchCV(sv, params, cv=10, n_jobs=-1, verbose=True)\n",
    "svm_rs.fit(x_train_sc_df, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "710f85c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'degree': 1, 'gamma': 0.001, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_rs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "614b7d13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score : 0.9882629107981221\n",
      "Test score : 0.965034965034965\n",
      "Time Taken 0.024712324142456055 sec\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "st = time.time()\n",
    "\n",
    "sv1 = svm.SVC(kernel=\"rbf\", degree=1,gamma=0.001, C=100, probability=True)\n",
    "\n",
    "sv1.fit(x_train_sc_df, y_train)\n",
    "\n",
    "print(\"Train score :\", sv1.score(x_train_sc_df, y_train))\n",
    "print(\"Test score :\", sv1.score(x_test_sc_df, y_test))\n",
    "\n",
    "et = time.time()\n",
    "print(\"Time Taken\", et-st, \"sec\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea57ddb7",
   "metadata": {},
   "source": [
    "#### Model Evalutaion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2ea2b467",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = sv1.predict(x_train_sc_df)\n",
    "pred_test = sv1.predict(x_test_sc_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6cec2218",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       269\n",
      "           1       0.99      0.97      0.98       157\n",
      "\n",
      "    accuracy                           0.99       426\n",
      "   macro avg       0.99      0.99      0.99       426\n",
      "weighted avg       0.99      0.99      0.99       426\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_train, pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9e4f4cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97        88\n",
      "           1       0.98      0.93      0.95        55\n",
      "\n",
      "    accuracy                           0.97       143\n",
      "   macro avg       0.97      0.96      0.96       143\n",
      "weighted avg       0.97      0.97      0.96       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6eab9f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = sv1.predict_proba(x_test_sc_df)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1ac7660a",
   "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>Model</th>\n",
       "      <th>ROC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.957955</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.927273</td>\n",
       "      <td>0.953271</td>\n",
       "      <td>0.965035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  ROC Score  Precision Score  Recall Score  \\\n",
       "0   Support Vector Machine    0.957955         0.980769      0.927273   \n",
       "\n",
       "   F1 Score  Accuracy Score  \n",
       "0  0.953271        0.965035  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_report = pd.DataFrame()\n",
    "\n",
    "tmp = pd.Series({'Model': \" Support Vector Machine \",\n",
    "                 'ROC Score' : metrics.roc_auc_score(y_test, pred_test),\n",
    "                 'Precision Score': metrics.precision_score(y_test, pred_test),\n",
    "                 'Recall Score': metrics.recall_score(y_test, pred_test),\n",
    "                 'F1 Score' : metrics.f1_score(y_test, pred_test),\n",
    "                 'Accuracy Score': metrics.accuracy_score(y_test, pred_test)})\n",
    "\n",
    "model_svm_report = model_report.append(tmp, ignore_index = True)\n",
    "model_svm_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f029e3ff",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "91868fe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 768 candidates, totalling 3840 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [True, False],\n",
       "                         'criterion': ['entropy', 'gini'],\n",
       "                         'max_depth': [3, 5, 7, 9],\n",
       "                         'max_features': ['auto', 'log2', None],\n",
       "                         'min_samples_split': [10, 15, 20, 25],\n",
       "                         'n_estimators': [25, 50, 100, 150]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist = {'max_depth':[3,5,7,9],\n",
    "              'min_samples_split':[10,15,20,25],\n",
    "              'n_estimators':[25,50,100,150],\n",
    "              'bootstrap': [True, False],\n",
    "              'max_features': ['auto', 'log2', None],\n",
    "              'criterion': ['entropy', 'gini']}\n",
    "\n",
    "fit_rf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "cv_rf = GridSearchCV(fit_rf, cv = 5, param_grid= param_dist, n_jobs = -1, verbose=1)\n",
    "\n",
    "cv_rf.fit(x_train_sc_df, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "554b1c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'criterion': 'entropy',\n",
       " 'max_depth': 7,\n",
       " 'max_features': 'log2',\n",
       " 'min_samples_split': 20,\n",
       " 'n_estimators': 50}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "74ba94c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9976525821596244\n",
      "Test Accuracy : 0.958041958041958\n"
     ]
    }
   ],
   "source": [
    "rf =RandomForestClassifier(n_estimators = 50,\n",
    "                           max_depth = 7, \n",
    "                           criterion = 'entropy',\n",
    "                           min_samples_split = 20,\n",
    "                           max_features = 'log2',\n",
    "                           bootstrap = False)\n",
    "\n",
    "rf.fit(x_train_sc_df, y_train)\n",
    "\n",
    "print(\"Train Accuracy :\", rf.score(x_train_sc_df,y_train))\n",
    "print(\"Test Accuracy :\", rf.score(x_test_sc_df,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda78a12",
   "metadata": {},
   "source": [
    "#### Model evalutaion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b825c61e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train_rf = rf.predict(x_train_sc_df)\n",
    "pred_test_rf = rf.predict(x_test_sc_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3954f2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = rf.predict_proba(x_test_sc_df)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cb3cbab5",
   "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>Model</th>\n",
       "      <th>ROC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.952273</td>\n",
       "      <td>0.962264</td>\n",
       "      <td>0.927273</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.958042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  ROC Score  Precision Score  Recall Score  \\\n",
       "0   Random Forest Classifier    0.952273         0.962264      0.927273   \n",
       "\n",
       "   F1 Score  Accuracy Score  \n",
       "0  0.944444        0.958042  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp1 = pd.Series({'Model': \" Random Forest Classifier \",\n",
    "                 'ROC Score' : metrics.roc_auc_score(y_test, pred_test_rf),\n",
    "                 'Precision Score': metrics.precision_score(y_test, pred_test_rf),\n",
    "                 'Recall Score': metrics.recall_score(y_test, pred_test_rf),\n",
    "                 'F1 Score' : metrics.f1_score(y_test, pred_test_rf),\n",
    "                 'Accuracy Score': metrics.accuracy_score(y_test, pred_test_rf)})\n",
    "\n",
    "model_rf_report = model_report.append(tmp1, ignore_index = True)\n",
    "model_rf_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c8de5c",
   "metadata": {},
   "source": [
    "### Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "88536ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 160 candidates, totalling 800 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=BaggingClassifier(random_state=1), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [True, False],\n",
       "                         'max_features': [1, 2, 3, 5, 10],\n",
       "                         'max_samples': [15, 20, 25, 30],\n",
       "                         'n_estimators': [150, 200, 250, 300]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hyperparameter Tuning Using GridSearchCV\n",
    "param_dist1 = {'n_estimators':[150,200,250,300],\n",
    "              'bootstrap': [True, False],\n",
    "              'max_features': [1,2,3,5,10],\n",
    "              'max_samples' : [15,20,25,30]}\n",
    "\n",
    "fit_bagging = BaggingClassifier(random_state = 1)\n",
    "\n",
    "cv_bagging = GridSearchCV(fit_bagging, cv = 5, param_grid= param_dist1, n_jobs = -1, verbose = 1)\n",
    "\n",
    "cv_bagging.fit(x_train_sc_df, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e4227d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True, 'max_features': 5, 'max_samples': 25, 'n_estimators': 200}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_bagging.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8bf60386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_accuracy 0.9460093896713615\n",
      "Test_accuracy 0.9300699300699301\n"
     ]
    }
   ],
   "source": [
    "bagging = BaggingClassifier(n_estimators=200,max_samples=25, max_features=10, bootstrap=True, random_state=1)\n",
    "bagging.fit(x_train_sc_df, y_train)\n",
    "\n",
    "print('Train_accuracy', bagging.score(x_train_sc_df, y_train))\n",
    "print('Test_accuracy', bagging.score(x_test_sc_df, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dac32e55",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "902fd91a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = bagging.predict_proba(x_test_sc_df)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e83199c2",
   "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>Model</th>\n",
       "      <th>ROC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bagging Classifier</td>\n",
       "      <td>0.922727</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.93007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  ROC Score  Precision Score  Recall Score  F1 Score  \\\n",
       "0  Bagging Classifier    0.922727         0.924528      0.890909  0.907407   \n",
       "\n",
       "   Accuracy Score  \n",
       "0         0.93007  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_bag = bagging.predict(x_test_sc_df)\n",
    "\n",
    "tmp2 = pd.Series({'Model': \"Bagging Classifier \",\n",
    "                 'ROC Score' : metrics.roc_auc_score(y_test, test_pred_bag),\n",
    "                 'Precision Score': metrics.precision_score(y_test, test_pred_bag),\n",
    "                 'Recall Score': metrics.recall_score(y_test, test_pred_bag),\n",
    "                 'F1 Score' : metrics.f1_score(y_test, test_pred_bag),\n",
    "                 'Accuracy Score': metrics.accuracy_score(y_test, test_pred_bag)})\n",
    "\n",
    "model_bag_report = model_report.append(tmp2, ignore_index = True)\n",
    "model_bag_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6bd405c",
   "metadata": {},
   "source": [
    "### Adaboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0974a445",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=AdaBoostClassifier(random_state=1), n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.5, 0.7, 1, 1.5, 2],\n",
       "                         'n_estimators': [50, 100, 150, 200]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hyperparameter Tuning Using GridSearchCV\n",
    "param_dist2 = {'n_estimators':[50,100,150,200],\n",
    "               'learning_rate':[0.5,0.7,1,1.5,2]}\n",
    "\n",
    "fit_adaboost = AdaBoostClassifier(random_state = 1)\n",
    "\n",
    "cv_adaboost = GridSearchCV(fit_adaboost, cv = 5, param_grid= param_dist2, n_jobs = -1, verbose = 1)\n",
    "\n",
    "cv_adaboost.fit(x_train_sc_df, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "237f337e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 1.5, 'n_estimators': 150}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_adaboost.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9c978ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_accuracy 1.0\n",
      "Test_accuracy 0.965034965034965\n"
     ]
    }
   ],
   "source": [
    "ABC = AdaBoostClassifier(n_estimators=150,learning_rate=1.5,random_state=1)\n",
    "ABC.fit(x_train_sc_df, y_train)\n",
    "\n",
    "print('Train_accuracy', ABC.score(x_train_sc_df, y_train))\n",
    "print('Test_accuracy', ABC.score(x_test_sc_df, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa5c3e0",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "087d4895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = ABC.predict_proba(x_test_sc_df)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6db8f58e",
   "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>Model</th>\n",
       "      <th>ROC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adaboost Classifier</td>\n",
       "      <td>0.961364</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.954128</td>\n",
       "      <td>0.965035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model  ROC Score  Precision Score  Recall Score  F1 Score  \\\n",
       "0   Adaboost Classifier    0.961364         0.962963      0.945455  0.954128   \n",
       "\n",
       "   Accuracy Score  \n",
       "0        0.965035  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_ABC = ABC.predict(x_test_sc_df)\n",
    "\n",
    "tmp3 = pd.Series({'Model': \" Adaboost Classifier \",\n",
    "                 'ROC Score' : metrics.roc_auc_score(y_test, test_pred_ABC),\n",
    "                 'Precision Score': metrics.precision_score(y_test, test_pred_ABC),\n",
    "                 'Recall Score': metrics.recall_score(y_test, test_pred_ABC),\n",
    "                 'F1 Score' : metrics.f1_score(y_test, test_pred_ABC),\n",
    "                 'Accuracy Score': metrics.accuracy_score(y_test, test_pred_ABC)})\n",
    "\n",
    "model_ABC_report = model_report.append(tmp3, ignore_index = True)\n",
    "model_ABC_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd8c3fd5",
   "metadata": {},
   "source": [
    "#### Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b9a7b919",
   "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>Model</th>\n",
       "      <th>ROC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.957955</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>0.927273</td>\n",
       "      <td>0.953271</td>\n",
       "      <td>0.965035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adaboost Classifier</td>\n",
       "      <td>0.961364</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.954128</td>\n",
       "      <td>0.965035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.952273</td>\n",
       "      <td>0.962264</td>\n",
       "      <td>0.927273</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.958042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bagging Classifier</td>\n",
       "      <td>0.922727</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>0.890909</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.930070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  ROC Score  Precision Score  Recall Score  \\\n",
       "0     Support Vector Machine    0.957955         0.980769      0.927273   \n",
       "1        Adaboost Classifier    0.961364         0.962963      0.945455   \n",
       "2   Random Forest Classifier    0.952273         0.962264      0.927273   \n",
       "3         Bagging Classifier    0.922727         0.924528      0.890909   \n",
       "\n",
       "   F1 Score  Accuracy Score  \n",
       "0  0.953271        0.965035  \n",
       "1  0.954128        0.965035  \n",
       "2  0.944444        0.958042  \n",
       "3  0.907407        0.930070  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Model', 'ROC Score', 'Precision Score', 'Recall Score','F1 Score', 'Accuracy Score']\n",
    "model = pd.DataFrame(columns = cols)\n",
    "model = model.append([model_svm_report,model_ABC_report,model_rf_report,model_bag_report], ignore_index = True)\n",
    "model"
   ]
  }
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