07_KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15b8c78a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</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>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</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>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</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>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</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>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302        17.99         10.38          122.80     1001.0   \n",
       "1      842517        20.57         17.77          132.90     1326.0   \n",
       "2    84300903        19.69         21.25          130.00     1203.0   \n",
       "3    84348301        11.42         20.38           77.58      386.1   \n",
       "4    84358402        20.29         14.34          135.10     1297.0   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564    926424        21.56         22.39          142.00     1479.0   \n",
       "565    926682        20.13         28.25          131.20     1261.0   \n",
       "566    926954        16.60         28.08          108.30      858.1   \n",
       "567    927241        20.60         29.33          140.10     1265.0   \n",
       "568     92751         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760         0.30010              0.14710   \n",
       "1            0.08474           0.07864         0.08690              0.07017   \n",
       "2            0.10960           0.15990         0.19740              0.12790   \n",
       "3            0.14250           0.28390         0.24140              0.10520   \n",
       "4            0.10030           0.13280         0.19800              0.10430   \n",
       "..               ...               ...             ...                  ...   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     symmetry_mean  fractal_dimension_mean diagnosis  \n",
       "0           0.2419                 0.07871         M  \n",
       "1           0.1812                 0.05667         M  \n",
       "2           0.2069                 0.05999         M  \n",
       "3           0.2597                 0.09744         M  \n",
       "4           0.1809                 0.05883         M  \n",
       "..             ...                     ...       ...  \n",
       "564         0.1726                 0.05623         M  \n",
       "565         0.1752                 0.05533         M  \n",
       "566         0.1590                 0.05648         M  \n",
       "567         0.2397                 0.07016         M  \n",
       "568         0.1587                 0.05884         B  \n",
       "\n",
       "[569 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) Develop a KNN classification model for the Cancer dataset using scikit-learn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from scipy import stats\n",
    "df=pd.read_csv('cancer.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ee2cb6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  fractal_dimension_mean  \n",
       "0         0.2419                 0.07871  \n",
       "1         0.1812                 0.05667  \n",
       "2         0.2069                 0.05999  \n",
       "3         0.2597                 0.09744  \n",
       "4         0.1809                 0.05883  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a) use the columns: 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean'\n",
    "# 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean',\n",
    "# 'symmetry_mean', 'fractal_dimension_mean', as the independent variables\n",
    "x=df.iloc[:,0:11]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "076458cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    M\n",
       "2    M\n",
       "3    M\n",
       "4    M\n",
       "Name: diagnosis, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# b) use the target variable as 'diagnosis' (Malignant - M, Benign - B)\n",
    "y=df.iloc[:,11]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff56be97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# c) Encode the categorical value of the target column to numerical value \n",
    "le=LabelEncoder()\n",
    "y=le.fit_transform(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3371a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# d) Divide the data into training (75%) and testing set (25%)\n",
    "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d46bb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# e) Perform the classification with K = 3\n",
    "knn=KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c457c8f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# f) Analyse the performance of the classifier with various performance measures\n",
    "# such as confusion matrix, accuracy, recall, precision, specificity, f-score, \n",
    "# Receiver operating characteristics curve and Area under curve score.\n",
    "y_pred=knn.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e2cbaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[77, 12],\n",
       "       [33, 21]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "cm=conf_matrix\n",
    "conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e3b8e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6853146853146853\n",
      "specificity: 0.3888888888888889\n",
      "recall: 0.3888888888888889\n",
      "presiction: 0.6363636363636364\n",
      "f1: 0.4827586206896552\n"
     ]
    }
   ],
   "source": [
    "accc=accuracy_score(y_test,y_pred)\n",
    "fsc=f1_score(y_test,y_pred)\n",
    "print(\"accuracy:\",accuracy_score(y_test,y_pred))\n",
    "print(\"specificity:\",recall_score(y_test,y_pred))\n",
    "print(\"recall:\",recall_score(y_test,y_pred))\n",
    "print(\"presiction:\",precision_score(y_test,y_pred))\n",
    "print(\"f1:\",f1_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4250b086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4f9c816940>,\n",
       " <matplotlib.lines.Line2D at 0x7f4f9c816a90>,\n",
       " <matplotlib.lines.Line2D at 0x7f4f9c816be0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dd3hVVdbA4d8ihRJaIKETaugCCaEjUkQBpVkQBEVlPhRF7GMbKdFx7I4IFlQGpRcREVFBdFQ6SQi9hU5oodeQtr8/zmW4hEBuyO1Z7/Pkyb2nrhwuKzv77LO2GGNQSinlvwp5OgCllFKupYleKaX8nCZ6pZTyc5rolVLKz2miV0opPxfo6QByEhYWZqpXr+7pMJRSymfEx8cfNcaE57TOKxN99erViYuL83QYSinlM0Rkz7XWadeNUkr5OU30Sinl5zTRK6WUn9NEr5RSfk4TvVJK+blcE72IVBWR30Vkk4hsFJGncthGRGSMiCSJyDoRibZbN0hEttu+Bjn7B1BKKXV9jgyvzACeM8YkiEgJIF5EFhljNtlt0w2ItH21BD4FWopIGWAkEAMY277zjDEnnPpTKKWUuqZcW/TGmIPGmATb6zPAZqByts16Ad8YywqgtIhUBG4HFhljjtuS+yKgq1N/AqWU8gOrdh3ny7924orS8XnqoxeR6kAUsDLbqsrAPrv3+23LrrU8p2MPEZE4EYlLSUnJS1hKKeXTjpxJ5YmpCUxZuZcL6ZlOP77DiV5EigPfAk8bY047OxBjzHhjTIwxJiY8PMeneJVSyu9kZGbx5NQ1nElN59OB0RQLdn7BAocSvYgEYSX5KcaYOTlskgxUtXtfxbbsWsuVUkoB7y3cxspdx3mzz03Uq1DSJedwZNSNAF8Bm40xH1xjs3nAg7bRN62AU8aYg8AvwG0iEioiocBttmVKKVXgLdp0mM/+2MH9LSO4K7qKy87jyN8IbYEHgPUikmhb9goQAWCM+QxYAHQHkoDzwMO2dcdF5HVgtW2/WGPMceeFr5RSvmnPsXM8OzORmyqXYsSdDVx6rlwTvTFmCSC5bGOAJ66xbgIw4YaiU0opP5SansnQyQkUEuGTAdEUCQpw6fm8skyxUkr5s5Hfb2TTwdNMeCiGqmWKufx8WgJBKaXcaObqfcyI28ewjrXpVK+8W86piV4ppdxk44FTvPb9BtrWLsszXeq47bya6JVSyg1OXUhn6OQEQosF81G/KAIKXffWp1NpH71SSrmYMYbnZ63lwMkLzHi0NWHFC7v1/NqiV0opF/v8z50s2nSYV7rXp1m1ULefXxO9Ukq50Iqdx3jn5y3c0bgiD7et7pEYNNErpZSLHDmdyrCpa6geFsLbdzfGKjTgftpHr5RSLpCRmcWwaWs4dzGDqf/XkuKFPZduNdErpZQLvPvLVlbtOs6/72tKnfIlPBqLdt0opZST/bLxEJ//uZOBrSLoHZXjFBxupYleKaWcaPfRczw/cy1NqpTiNRcXK3OUJnqllHKS1PRMhk5JICBAGDcgmsKBri1W5ijto1dKKScwxvCPuRvYcug0Ex5qTpVQ1xcrc5S26JVSyglmrN7H7Pj9PNmxNh3rlvN0OFfQRK+UUvm0IfkUI+Zt5ObIMJ661X3FyhyVa9eNiEwA7gSOGGMa5bD+BWCA3fHqA+G22aV2A2eATCDDGBPjrMCVUsobnDqfztAp8ZQNCebf9zV1a7EyRznSop8IdL3WSmPMu8aYpsaYpsDLwB/ZpgvsaFuvSV4p5VeysgzPzUrk0KlUxg2Ipqybi5U5KtdEb4z5E3B0ntf+wLR8RaSUUj7isz938OvmI7zavT7REe4vVuYop/XRi0gxrJb/t3aLDbBQROJFZEgu+w8RkTgRiUtJSXFWWEop5RLLdhzlvV+20qNJJQa1qe7pcK7LmTdjewBLs3XbtDPGRAPdgCdEpP21djbGjDfGxBhjYsLDw50YllJKOdfh06kMn7aGGmEhvHXXTR4rVuYoZyb6fmTrtjHGJNu+HwG+A1o48XxKKeV26ZlZDJuawPm0TD4b2IwQDxYrc5RTEr2IlAJuAb63WxYiIiUuvQZuAzY443xKKeUp7/y8hdW7T/Cvu24i0sPFyhzlyPDKaUAHIExE9gMjgSAAY8xnts36AAuNMefsdi0PfGf7kyYQmGqM+dl5oSullHv9vOEgX/y1iwdbV6NXU88XK3NUroneGNPfgW0mYg3DtF+2E2hyo4EppZQ32XX0HC/MWkeTqqV59Y76ng4nT/TJWKWUysWFtEyGTo4nMED4xIuKlTnK++8iKKWUBxljeHXuerYePsPEh1tQuXRRT4eUZ9qiV0qp65i2ah9zEpIZ3imSW+r45tBvTfRKKXUN6/efYtS8jbSvE87wzpGeDueGaaJXSqkcnDyfxtAp8YQV995iZY7SPnqllMomK8vw7My1HD6dyqzH2lAmJNjTIeWLtuiVUiqbT//YwW9bjvDanQ1oWrW0p8PJN030SillZ2nSUd5fuJWeTSrxQKtqng7HKTTRK6WUzaFTVrGymuHF+ZcPFCtzlPbRK6UUl4uVXUjPZMbAaJ8oVuYo//lJlFIqH976aQtxe07wcf8oapfzjWJljtKuG6VUgbdg/UG+WrKLh9pUp0eTSp4Ox+k00SulCrQdKWf5++x1REWU5pXuvlWszFGa6JVSBdb5tAyGTo4nOLAQ4+6PJjjQP1Oi9tErpQokYwyvfreB7UfO8s0jLajkg8XKHOWfv76UUioXU1bu5bs1yTzduQ43R/pmsTJH5ZroRWSCiBwRkRynARSRDiJySkQSbV8j7NZ1FZGtIpIkIi85M3CllLpR6/afJPaHTXSoG86TnWp7OhyXc6RFPxHomss2fxljmtq+YgFEJAAYB3QDGgD9RaRBfoJVSqn8OnEujaGTEwgvUZgP+zalkA8XK3NUroneGPMncPwGjt0CSDLG7DTGpAHTgV43cByllHKKrCzDMzMTSTlzkU8GRBPq48XKHOWsPvrWIrJWRH4SkYa2ZZWBfXbb7Lcty5GIDBGROBGJS0lJcVJYSil12bjfk/jv1hRe69GAJn5QrMxRzkj0CUA1Y0wT4GNg7o0cxBgz3hgTY4yJCQ/37xsjSin3W7L9KB/8uo3eTSsxsGWEp8Nxq3wnemPMaWPMWdvrBUCQiIQByUBVu02r2JYppZRbHTx1geHT1xBZrjhv+lGxMkflO9GLSAWxXTURaWE75jFgNRApIjVEJBjoB8zL7/mUUiov0jKyeGJKAhfTM/l0YDOKBRe8x4dy/YlFZBrQAQgTkf3ASCAIwBjzGXAPMFREMoALQD9jjAEyRGQY8AsQAEwwxmx0yU+hlFLX8K+fNpOw9yTj7o+mVnhxT4fjEbkmemNM/1zWjwXGXmPdAmDBjYWmlFL5M3/dAf6zdDcPt63OHY0rejocj9EnY5VSfinpyFlenL2O6IjSvNzNP4uVOUoTvVLK75y7aBUrKxwUwLgB/luszFEF766EUsqvGWN45bv1JKWcZdIjLalYyn+LlTmqYP+aU0r5nckr9vB94gGe61KHdpFhng7HK2iiV0r5jcR9J4mdv4lO9crxeAf/L1bmKE30Sim/cOJcGk9MSaB8ySJ80LdJgShW5ijto1dK+bysLMPTM6xiZbOHtqZ0sYJRrMxR2qJXSvm8j39L4o9tKYzs2YDGVQpOsTJHaaJXSvm0P7el8O/F27grqjL3tyhYxcocpYleKeWzDpy8wFPT11CnXAn+2afgFStzlCZ6pZRPSsvI4vEpCaRnGj4dGE3R4ABPh+S19GasUsonvblgM4n7TvLJgGhqFtBiZY7SFr1SyufMW3uAict2M7hdDbrfVHCLlTlKE71SyqdsP3yGl75dR0y1UF7qVs/T4fgETfRKKZ9x7mIGQ6ckUCw4gLH3RxMUoCnMEbleJRGZICJHRGTDNdYPEJF1IrJeRJaJSBO7dbttyxNFJM6ZgSulChZjDC/NWc/OlLOM6RdFhVJFPB2Sz3Dk1+FEoOt11u8CbjHG3AS8DozPtr6jMaapMSbmxkJUSin4Zvkeflh7gOduq0ub2lqsLC8cmWHqTxGpfp31y+zersCaBFwppZwmYe8J3vhxE53rlWPoLbU8HY7PcXYH12DgJ7v3BlgoIvEiMuR6O4rIEBGJE5G4lJQUJ4ellPJVx8+lMWxKAhVKFeGDvk21WNkNcNo4ehHpiJXo29ktbmeMSRaRcsAiEdlijPkzp/2NMeOxdfvExMQYZ8WllPJdmVmGp6av4ei5NOYMbUOpYkGeDsknOaVFLyKNgS+BXsaYY5eWG2OSbd+PAN8BLZxxPqVUwTBm8Xb+2n6U0T0b0qhyKU+H47PynehFJAKYAzxgjNlmtzxEREpceg3cBuQ4ckcppbL779YjjPltO3dHV6Ff86qeDsen5dp1IyLTgA5AmIjsB0YCQQDGmM+AEUBZ4BNbQaEM2wib8sB3tmWBwFRjzM8u+BmUUn4m+eQFnp6RSN3yJXijdyMtVpZPjoy66Z/L+r8Bf8th+U6gydV7KKXUtV3MyOTxKQlkZho+HdhMi5U5gRY1U0p5lTfmb2btvpN8NjCaGmEhng7HL+jzw0opr/F9YjKTVuzh/26uQddGWqzMWTTRK6W8wrbDZ3jp2/U0rx7K37tqsTJn0kSvlPK4sxczeGxyPCGFA7VYmQvo1VRKeZQxhhe/Xcfuo+f4uH8U5UtqsTJn00SvlPKoict28+O6g7xwez1a1yrr6XD8kiZ6pZTHxO85wT9/3Myt9cvz2C01PR2O39JEr5TyiGNnLzJsagKVShfl/b5N9KEoF9Jx9Eopt7OKlSVy7FKxsqJarMyVtEWvlHK7j37dxpKko7zeS4uVuYMmeqWUW/2+9Qhjfkvi3mZVuK95hKfDKRA00Sul3Gbf8fM8MyOR+hVL8nrvRp4Op8DQRK+UcouLGZk8MdVWrGxANEWCtFiZu+jNWKWUW8T+sIl1+0/x+QPNqK7FytxKW/RKKZf7bs1+pqzcy6Pta3J7wwqeDqfA0USvlHKprYfO8PKc9bSoUYYXbq/r6XAKJIcSvYhMEJEjIpLjVIBiGSMiSSKyTkSi7dYNEpHttq9BzgpcKeX9zqSmM3RyPMULBzG2fxSBWqzMIxy96hOBrtdZ3w2ItH0NAT4FEJEyWFMPtsSaGHykiITeaLBKKd9xqVjZnuPnGXd/FOW0WJnHOHQz1hjzp4hUv84mvYBvjDEGWCEipUWkItZcs4uMMccBRGQR1i+MafkJ+lpe/PNF0rPSXXFopQBoHNaYAQ0GEFRIn+TMzYSlu1mw/hAvd6tHy5parOy6zhyGxClwfCf0Guv0wztr1E1lYJ/d+/22ZddafhURGYL11wARETf2EMWe03u4mHnxhvZVKjfpWeks2rOIH3f9yOg2o2lQtoGnQ/JacbuP868Fm7mtQXmGtNdiZTnKyoKdv0H8RNj6E2RlQPWbIeMiBBZ26qm8ZnilMWY8MB4gJibG3Mgxpt853akxKZXd4j2LeWPlG9z/4/081PAhHmvyGEUCtUvC3tGzF3liagKVQ4vy7r1arOwqpw/AmimQ8A2c2gvFykKroRA9CMIiXXJKZyX6ZKCq3fsqtmXJWN039sv/66RzKuV2nat1JqZCDB/Ef8BXG75i8d7FjG4zmujy0bnvXABYxcrWcPJ8Ot893kKLlV2SlQlJv1qt920/g8mCmh2gy2iod4fTW/DZOesW+DzgQdvom1bAKWPMQeAX4DYRCbXdhL3Ntkwpn1WqcClGtxnN+C7jSc9KZ9DPg/jnin9yLv2cp0PzuA8XbWNp0jFe792IBpVKejoczzu5D37/F/z7JpjaF/bHQdunYPgaePB7aHSXy5M8ONiiF5FpWC3zMBHZjzWSJgjAGPMZsADoDiQB54GHbeuOi8jrwGrboWIv3ZhVyte1rtSaOT3n8PGaj5myeQp/7P+DEa1H0K5yO0+H5hGLNx9m7O9J3BdTlb4xVXPfwV9lZsD2X6zWe9KvYAzU7gxd34K63SDA/X/liDVQxrvExMSYuLg4T4ehlMMSjyQyctlIdp7aSc9aPXkh5gVKFynt6bDcZt/x89wx5i+qhBZjzuNtCmYdmxO7IWESrJkMZw9BiYoQNRCiHoDQai4/vYjEG2NiclrnNTdjlfJlTcs1ZVaPWYxfN56v1n/FkuQlvNLyFW6rdpvf34xMTc9k6JR4DPDZwGYFK8lnpsPWBVbrfcfvIAKRt1k3ViNvgwDvSLHeEYVSfiA4IJhhUcPoUq0LI5aN4Pk/nqdT1U78o9U/CC8W7unwXGb0D5vYkHyaLx6MIaJsMU+H4x7HdlijZhKnwLkUKFkFOrxkteBLVfF0dFfRRK+Uk9UtU5cp3acwadMkxiWOo9fcXrzQ/AV61+7td637b+P3M23VXh67pRZdGpT3dDiulXERtsy3Wu+7/gQJgDpdodlDVh98Ie/9S0b76JVyoT2n9zBy2UjiD8fTqmIrRrYeSZUS3tfiuxFbDp2m97ilNK1amsmDW/pvHZuj263kvnYanD8GpSMg+kFoOhBKVvR0dP9zvT56TfRKuViWyWL2ttl8EP8BWSaL4VHD6V+vPwFe3ALMzenUdHqNXcq5ixnMH96OciX87KGx9FTYPM9K8HuWQqFAqNvdar3X7AiFvO+XmiZ6pbzAoXOHiF0ey1/Jf9E4vDGxbWKpVbqWp8PKM2MMQycnsGjzYab9Xyta1Cjj6ZCc58hmiP/aar2nnoTQGtBsEDQdAMXLeTq669JRN0p5gQohFRjXeRw/7vqRt1e9zb0/3MuQxkMY3GgwQR4YW32jvlqyi583HuLV7vX9I8mnnYdNc63W+76VUCgI6vewWu/Vb/bK1nteaYteKQ84duEYb696m592/0Sd0DrEtomlYVhDT4eVq9W7j9Nv/Aq61C/PpwOjffvm8qH1Vut93Uy4eArKRlqt9yb9ISTM09HlmXbdKOWlft/7O2+seIOjqUcZ1GAQjzd93GuLpKWcucgdY/6iWHAA855sR8kivvNXyP9cPAsb51it9+R4CCgMDXpZrfdqbaxx8D5Ku26U8lIdIzoSUyGG9+Pe5z8b/8PivYsZ1WYUzSs093RoV8jIzGL4tDWcTk3n60da+F6SP5BoJff1syHtDITXs0oSNL4PivlB91MuNNEr5WElgkswqs0outXoxqhlo3jkl0foW6cvzzR7huLBxT0dHgDvL9rG8p3HeO/eJtSv6CPFylJPw4bZVoI/uBYCi0LDPlbrvWoLn26955UmeqW8RMuKLZnTaw5j14xl8ubJ/yuS1r5Ke4/GtWjTYT797w76t6jKPc28/BkAYyA5AeL/AxvmQPo5KN8Iur8HN90LRQtO/SF72kevlBdal7KOkctGknQyiTtq3sGLzV8ktIj7p1vee+w8d3z8F9XKFmP2Y15crOzCSVg/y2q9H94AQSFWCeBmD0Pl6ALRetc+eqV8TOPwxsy8cyZfrP+CL9Z/wbLkZbzc8mW6Vu/qtpEul4qVCfDpAC8sVmYM7FtlJfeN30HGBajYFO78EBrdA0V8pIvJDTTRK+WlggKCeLzp49xa7VZGLh3J3//8Owt2LeAfLf9B+RDX15UZNW8jGw+c5qtBMVQt40XFys4fh3UzrASfsgWCS0CTftbQyEpRno7OK2nXjVI+IDMrk8mbJzN2zVgCCwXyXMxz3B15t8ta97Pi9vHC7HU83qEWf+9azyXnyBNjYM8yK7lv+h4yL0LlZtaN1YZ3QWHvuGntSfkeRy8iXYGPgADgS2PMW9nWfwh0tL0tBpQzxpS2rcsE1tvW7TXG9MztfJrolcrZvtP7GLV8FKsOraJFhRaMaj2KqiWdO5vTpgOn6fPJUqIjQpk0uIVni5WdOwZrp1oPNh3bDoVLQeO+Vuu9wk2ei8sL5SvRi0gAsA3oAuzHmhawvzFm0zW2fxKIMsY8Ynt/1hiTp1+3muiVujZjDN9u/5b3494nIyuDYVHDGFh/oFOKpJ1OTafnx0u4kJ7J/CdvJryE6+czvUpWFuz+y2q9b5kPmWlQtaXVem/QG4K9qBvJi+T3ZmwLIMkYs9N2sOlALyDHRA/0x5pTVinlAiLCPXXu4ebKN/PGijd4L+49ftn9C6PbjCYyNPKGj2uM4fmZa9l/4gLTh7Ryf5I/e8SayCP+azixC4qUhpjBVuu9XH33xuJnHEn0lYF9du/3Ay1z2lBEqgE1gN/sFhcRkTggA3jLGDP3GvsOAYYAREREOBCWUgVb+ZDyjOk0hp93/8y/Vv6LvvP7MuSmIfztpr/dUJG0L/7aycJNh/nHHfWJqe6mp0WzsmDn71brfesCyMqAam2h4ytQvycEeWc5CF/j7FE3/YDZxphMu2XVjDHJIlIT+E1E1htjdmTf0RgzHhgPVteNk+NSyi+JCN1qdKNVxVa8vfptPln7CQv3LCS2TSw3hTveh71y5zHe/nkr3W+qwOB2NVwYsc3pg5A42ZqO7+ReKFYWWj5mzbUaXsf15y9gHEn0yYD93Z4qtmU56Qc8Yb/AGJNs+75TRP4LRAFXJXql1I0LLRLKWze/Rfca3YldHsvAnwbyQP0HeCLqCYoGFr3uvkfOpDJs2hqqlSnG23c3dt04/axMSFpstd63/QwmE2rcAreOgnp3QqAH7gcUEI4k+tVApIjUwErw/YD7s28kIvWAUGC53bJQ4Lwx5qKIhAFtgXecEbhS6mrtq7Rnbq+5fBj/IV9v+prFexczus1oWlRskeP2GZlZDJu6hjOp6Uwa3IISrihWdmo/rJkMCZPg9H4ICYe2wyHqASjrexOv+KJcE70xJkNEhgG/YA2vnGCM2SgisUCcMWaebdN+wHRz5TCe+sDnIpIFFMLqo7/WTVyllBMUDy7Oa61fo2uNroxaNorBCwdzd+TdPBfzHCWCS1yx7bsLt7Jq13E+6NuEehWc+CRpZgZsX2i13pMWWePga3WCrm9CnW4QGOy8c6lc6QNTSvmxCxkX+DTxU77e9DVhRcJ4rfVrdKjaAYCFGw8xZFI897eM4M0+ThqTfmIPrJlkteDPHITiFSD6Aav1HlrNOedQOdKJR5Qq4DYe3chry15j+4ntdKvejYGRwxkwfiPVy4Yw67HW+atjk5kOW3+yWu87frMKiNXuYg2LjLwdArTSijtooldKkZ6ZzlcbvuLzdZ+TlVEYc6w38x9+koiyITd2wOM7rVEza6bAuSNQsjJEPwhRA6GUl5cz9kNavVIpRVBAEI81eYy1WyP44+w4AsKn8HbiPl5r9RoVQio4dpCMNOtp1fiJsOsPkACo09Vqvde+FZzwdK5yPk30ShUgM1fv46cEw+Md3qVSRAJj1oyh9/e9ebbZs9xT5x4KyTXq2hxNgoSJkDgNzh+FUhHQ8R8QNQBKVnLrz6DyThO9UgXExgOneO37DbStXZbnbqtPQKEG3FL1FkYvH83rK17np10/MarNKKqVtN00TU+FzT9Yrfc9S6BQINTtbrXea3aCQh4sdqbyRPvolSoATl1Ip8fHS0jLyOLH4e0oW/zyw0nGGOYmzeXd1e+SlpXGE7Xv5YETxwlcNwMunIDQGlbfe9MBUML1dfDVjdE+eqUKMGMMz89ay4GTF5jxaOsrkjxYZRT6VO9K25Mp/HPDF3ywdTK/XExjdPWW1G0+FKq319a7j9N/PaX83Od/7mTRpsO80r0+zaplm3f28EZY8AK8X5dyP77Av89m8l6FzhwsWZ5+F7cx9tQG0kyGZwJXTqMteqX82PIdx3jn5y3c0bgiD7etbi1MOwcb5lh978lxEFAYGvSCZoOQam25XYSWqSd5N+5dPl/3Ob/u+ZXRbUfTJLyJJ38UlQ/aR6+UnzpyOpXuY5ZQsmgg84a1o/jxjVZyXzcL0s5AeD2rWmSTflAs57LEf+3/i9gVsRw+d5gB9QfwZNSTFAvSiT+8kT4wpVQBk56ZxYAvVrIj+RALOh2i/LbpcDARAotYc6w2G2TN2uRApcpz6ef4d/y/mb51OpWLV2Zk65G0rtTaDT+FygtN9EoVJMbwn1lzKLJuEvcWXkFg5gUo19Caiq/xvVA0NNdD5CT+cDwjl41kz+k99Kndh+ebP0/JYCcWQlP5ooleqYIg9RSsm8npZV9R8uRm0goVIbjJvVaCr9zModZ7rqfISOWztZ8xceNEyhQpw6utXqVzROf8x67yTRO9Uv7KGNi/2up73zAHMi6wydTgj+LdeeTxFygccmOt99xsOraJkctGsuX4Fm6rdhsvt3yZsKJhLjmXcoyOo1fK31w4AWtnQMLXcGQTBBcno1Ffnk5qypLzVZj/t3YUDnHdTdMGZRsw9Y6pTNwwkU/XfsrKQyt5sfmL3FnzTtfNUKVumEPj6EWkq4hsFZEkEXkph/UPiUiKiCTavv5mt26QiGy3fQ1yZvBKFSjGwJ5lMGcIvF8Pfn4RgopCjzGY57bwUvpgfjxWng/va0qVUNePjAkqFMT/Nf4/ZvecTY2SNXhlySsMXTyUg2cPuvzcKm9y7boRkQBgG9AF2I81tWB/+5miROQhIMYYMyzbvmWAOCAGMEA80MwYc+J659SuG6XsnDsGa6dZrfej26BwSWjc1xoaWbExANNX7eWlOesZ3jmSZ7u4f3LtLJPFtC3T+CjhIwTh6WZPc1/d+65dJE05XX67bloAScaYnbaDTQd6AY5MCXg7sMgYc9y27yKgKzDNkcCVKrCMgd1/WX3vm3+AzDRrOGSvT6Bhbwi+XEN+Q/IpRszbyM2RYTzVOdIj4RaSQgyoP4AOVTswetlo3lz5Jj/v+plRbUZRo1QNj8SkLnMk0VcG9tm93w+0zGG7u0WkPVbr/xljzL5r7Fs5p5OIyBBgCEBERIQDYSnlh86mQOIUa0KP4zugSGmIecRqvZdvcNXmp86nM3RKPGVDgvmoXxQBhTzbP165eGU+7/I583bM453V73DPvHsY2nQoDzV8iMBCekvQU5x15X8AphljLorIo8DXQKe8HMAYMx4YD1bXjZPiUsr7ZWXBrv9arfctCyArHaq1hVtehAY9rX74HHczPDcrkUOnUpnxaGvKhHjHhNsiQq/avWhbuS1vrnyTjxI+YuHuhXPQ+OIAABZeSURBVMS2jaVemXqeDq9AciTRJwNV7d5XsS37H2PMMbu3XwLv2O3bIdu+/81rkEr5pTOHrEm0E76Bk3ugaBlo+ajVeg/PvZ/9sz938OvmI4zq0YDoCNcMo8yPsKJhfNDhAxbtWcQ/V/yTfvP78UijR3i0yaMUDiic+wGU0ziS6FcDkSJSAytx9wPut99ARCoaYy7dau8JbLa9/gV4U0QufQpvA17Od9RK+aqsTGsC7fiJ1oTaJhNqtIfOI6B+Dwh0LAEu23GU937ZSo8mlRjUprpLQ86vLtW60KJCC95d/S5frP+CRXsWEds2lqhyUZ4OrcDINdEbYzJEZBhW0g4AJhhjNopILBBnjJkHDBeRnkAGcBx4yLbvcRF5HeuXBUDspRuzShUop5Kt1vuaSXBqH4SEQ5thVuu9bK08HerQqVSGT1tDjbAQ3rrrJp8Yt16qcCneaPcG3Wt0Z/Ty0Qz6aRD96/XnqeintEiaG+iTsUq5SmYGJC2yWu/bF4LJglqdrORetzsE5r1PPT0zi/7jV7Dp4Gm+f6ItkeVLOD9uFzuffp6PEj5i2pZpVAypyMjWI2lTuY2nw/J5+mSsUu50ci8kTLJa8GcOQPHy0O4ZiHoAyuRvqOHbP20hbs8JPurX1CeTPECxoGK83PJlutboyshlI3n010fpVasXLzR/gVKFS3k6PL+kiV4pZ8hMh20/Q/zXkPSrtSyyC3R/F+rcDgFB+T7FT+sP8uWSXTzYuhq9muY4StmnRJWLYlaPWXy+9nMmbJjAkuQlvNrqVbpU6+Lp0PyOdt0olR/Hd1mjZhKnwNnDUKISRD8AUQOhtPOeB9mZcpaeY5dSq1xxZj7aisKBAU47tjfYcnwLI5aOYPPxzXSp1oVXWr6iRdLySLtulHKmjDTY+qPVet/5O0ghiLzdKgdc+1YIcO5/qwtpmTw+JYGgAOGTAdF+l+QB6pWpx9Q7pvL1xq/5JPETVhxcwd+b/51etXr5xM1mb6cteqUccfEspGyFTXMhcSqcPwqlqkL0g9B0AJRyTVeKMYbnZq3luzXJTHy4BbfUCXfJebzJrlO7GLVsFAlHEmhdsTUj24ykcnHf76pyNW3RK+Woi2eshJ6yxfa1FY5sgVN7rfUSAHW7QbOHoVZHKOTa1vW0VfuYk5DM07dGFogkD1CjVA3+0/U/zNw6kw/jP6TP9314Kvop+tfrr0XSbpC26FXBdOGkVQkyZYuVyC8l9dP7L28TUNh6QjW8HoTXtb5XaQElyrslxPX7T3H3p8toVassEx9qTiEP17HxhINnDzJ6xWiWJi+laXhTRrcZTc3SNT0dllfSGaZUwXX+uF0LfSukbLa+n7GrmR5Y1C6h17uc2EOru7zFfi0nz6dx58dLyMoyzB9+s9fUsfEEYwzzd87n7dVvcz79PEObDOWhRg8RVCj/I5n8iXbdKP937pgtmW++MrGfPXx5m6AQK4HX7Hi5hV6uHpSKgELe0yWQlWV4duZaDp9OZdZjbQp0kgerSFqPWj1oXak1b616izFrxvDL7l+IbRtLg7JXV/RUV9MWvfIdxsC5FLu+c7ukfv7o5e2CS1yZyC+10EtW8aqEfi1jf9vOewu3EdurIQ+2ru7pcLzO4r2LeWPFG5xIPcGghoMY2mQoRQKLeDosj9MWvfItxlgt8SPZWucpW+CCXamkwqWsRF6vu10/en0oWQl8dEje0qSjfLBoGz2bVOKBVtU8HY5X6hzRmZjyMXwQ/wETNkzgt72/MarNKJqVb+bp0LyWtuiV5xgDpw9c3X+esgVST13erkhpKFf/ciK/1FovUcFnE3pODp1K5Y4xfxEaEsz3T7QlpLC2w3Kz/MByRi8fTfLZZO6rex/PNHuGkKCQ3Hf0Q9qiV55lDJzabzdk8VJi3woXT1/erlhZK5E3uudyC71cfavSox8l9JykZ2bxxNQELqRnMmNgtCZ5B7Wu1Jo5Pefw8ZqPmbJ5Cn/s/4MRrUZwc5WbPR2aV9EWvXKerCxrvPmlVvmlYYtHt0Ha2cvbhZTLoQ+9HoQU3EfeY3/YxISlu/i4fxQ9mlTydDg+aW3KWkYuHcmOUzvoUbMHf2/+d0oXKe3psNxGW/TKubIy4cTuq/vPj26D9POXtytR0UroUQOv7HYpVsZjoXujH9cdZMLSXTzUprom+XxoEt6EmT1mMn7deL5a/xVLDyzllZavcFu12wp8GQVt0atry8ywJfRswxaPboeM1Mvblax8df95eB0o6n3T23mbHSln6fnxEupUKMGMIa0JDvT+UUG+YOvxrYxYNoJNxzbRqWonXm31KuWKlfN0WC6V7wemRKQr8BHWDFNfGmPeyrb+WeBvWDNMpQCPGGP22NZlAuttm+41xvTM7Xya6N0sMx2O77yydX5kCxzbDplpl7crFWFL5Je6XepDWB0oUtJzsfuw82kZ9B63lKNn05j/ZDsqlc55EnB1YzKyMpi0aRLjEscRXCiY55s/T5/affy2dZ+vRC8iAcA2oAuwH2tawP7GmE1223QEVhpjzovIUKCDMeY+27qzxpjieQlYE72LZKTB8R1XP/Z/LAmy0i9vV7ra1WPQw+pAYd+c6MIbGWM9FDU3MZlvHmnBzZEFo46NJ+w5vYeRy0YSfzielhVbMrL1SKqWqOrpsJwuv330LYAkY8xO28GmA72A/yV6Y8zvdtuvAAbeeLgq39JTreRt30JP2QLHdliTUQMg1mxH4fWgbtfLN0TDIiG4YA5Pc6cpK/fy3Zpknu1SR5O8i1UrWY0Jt09g9rbZfBD/AXfPu5sno57k/nr3E+ChEhfu5kiirwzss3u/H2h5ne0HAz/ZvS8iInFY3TpvGWPm5rSTiAwBhgBERDhvwga/ln7B6i+/YsjiFqsbxmRZ20ghKFPTSuL1e9q10CMhSLsKPGHd/pPE/rCJDnXDGdaxtqfDKRAKSSH61u1L+yrteX3F67yz+h1+3v0zsW1iqVU6b5Oz+yKnjroRkYFADHCL3eJqxphkEakJ/CYi640xO7Lva4wZD4wHq+vGmXH5vLRztkqL2YYtntgN2C5VoUAoUwvKNYCGd10eg162NgQW9mT0ys6Jc2kMnZxAeInCfNi3aYGsSOlJFUIqMLbTWBbsWsBbq97i3h/uZUjjIQxuNJggJ0z36K0cSfTJgH2HVhXbsiuIyK3Aq8AtxpiLl5YbY5Jt33eKyH+BKOCqRK+w1ULfdnUL/eReLif0ICt5V2oKTfpdvjFaphYEFuziV94uK8vwzMxEUs5cZNZjrQkt4MXKPEVEuKPmHVaRtJVvMS5xHAv3LCS2TSyNwhp5OjyXcORmbCDWzdjOWAl+NXC/MWaj3TZRwGygqzFmu93yUOC8MeaiiIQBy4Fe9jdyc+L3N2NTT2VL6Lakfsquhywg2LoB+r/hiravMjWcMtG0cr8xi7fzwaJtvN67kdax8SK/7/2dN1a8wdHUowxqMIihTYdSNND3ujXzdTPWGJMhIsOAX7CGV04wxmwUkVggzhgzD3gXKA7Msg1dujSMsj7wuYhkAYWw+uivm+T9yoUTVz9UdGQLnDlweZvAIlZ/eURrCH/ockIPre70uUeV5/y1PYUPf91G76aVGNhS70F5k44RHYmpEMP7ce/zn43/YfHexYxqM4rmFZp7OjSn0QemnOH88cstc/thi2cPXd4mqJithW5XwyW8rjWUsYDc+S+oDpy8wJ0fLyGseDBzn2hLsWD9Be6tVh5cyahlo9h/dj996/TlmWbPUDw4T6PDPUZLIDjL2ZSr+89Ttlg10i+5NLlF7c523S51vW5yC+UeaRlWsbKL6Zl8OrCZJnkv17JiS+b0msO4NeOYtHmSVSSt9QjaV2nv6dDyRVv02RkDZ4/kUGlxC5w/dnm7wiWvfEr00uP/par4faVF5bhR8zYycdluxt0fzR2NK3o6HJUH61PWM2LZCJJOJtG9RndebPEiZYp4b50mbdHnxBhr3tDs/ecpWyD15OXtipSyEnm9O6+cJNqHJ7dQ7vHD2gNMXLabh9tW1yTvg24Kv4mZd87ky/VfMn79eJYfWM7LLV+ma/WuPldGwf9b9MbA6eSr+89TtsJFu8ktioZebpWXsyvOVby8JnSVZ0lHztJr7BLqVijBdC1W5vO2n9jOyGUjWX90PR2qdOAfrf5B+ZDyng7rCvkuauZuN5TojbHGm18xymWzNYwx7czl7YqFXZnIL1VdDAnThK6c4txFq1jZsXNp/Di8HRVL+d5QPXW1zKxMJm+ezNg1YwksFMhzMc9xd+TdXtO6LxiJPjMD3qx4udpi8fJXj0EPr1ugJ7dQrmeM4ekZifyw9gCTBrekbW39vPmbfaf3MWr5KFYdWkWLCi0Y1XoUVUt6vkhawUj0ABvmXJ7sQie3UB4wafluXvt+I8/fVodhnSI9HY5yEWMMc7bP4b2498jIymBY1DAG1h/o0SJp10v0/tVx2OguqNZak7zyiMR9J4mdv4lO9crxeActVubPRIS769zN3F5zaVWxFe/FvcfABQPZfmJ77jt7gH8leqU85Pi5NB6fHE/5kkX4oG8TLVZWQJQPKc+YTmN4t/27HDh3gL7z+/JJ4iekZ6bnvrMbaaJXKp8ys6x++aNn0/hkQDSli2mxsoJEROhaoytze83l9uq38+naT+k7vy/rU9bnvrObaKJXKp8+/m07f25LYWTPBjSuUtrT4SgPCS0Syls3v8W4zuM4k3aGgT8N5N3V73Ih44KnQ9NEr1R+/LEthY8Wb+euqMrc30KLlSloX6U9c3vN5Z7Ie/hm0zfc9f1drDq4yqMxaaJX6gYln7zA09PXUKdcCf7Z5yavGU+tPK94cHFea/0aE26fQCEpxOCFgxm1bBSn0057JB5N9ErdgLSMLJ6YkkB6puHTgdEUDdYKpOpqzSs059ue3/Jwo4f5Luk7+sztw+97f899RyfTRK/UDfjnj5tI3HeSd+5pTM1w3yhjqzyjSGARnm32LFO7T6VUkVIM/304L/zxAscuHMt9ZyfRRK9UHs1be4Cvl+9hcLsadL9Ji5UpxzQMa8iMO2YwrOkwFu9dTO/vezN/53zc8dCqQ4leRLqKyFYRSRKRl3JYX1hEZtjWrxSR6nbrXrYt3yoitzsvdKXcb/vhM7z07TpiqoXyUrd6ng5H+ZiggCAebfIos3rMIqJkBC//9TLDfhvGoXOHct85H3JN9CISAIwDugENgP4i0iDbZoOBE8aY2sCHwNu2fRsA/YCGQFfgE9vxlPI55y5mMHRKAsWCAxh7fzRBAfoHsboxtUrX4puu3/Bi8xdZfWg1vb/vzcytM8kyWS45nyP16FsAScaYnQAiMh3oBdjP/doLGGV7PRsYK9YQhF7AdGPMRWCXiCTZjrfcOeFfqcfHS0hNz3TFoZXiTGoGR86kMvlvLalQqoinw1E+LqBQAAMbDKRD1Q6MXj6a11e8zoJdC/ik8ycUCyrm1HM5kugrA/vs3u8HWl5rG9tk4qeAsrblK7LtWzmnk4jIEGAIQETEjY1HrhUeQlqma34jKgVwe8MKtKmlFSmV81QpUYXxXcYzN2kuiSmJTk/y4EUzTBljxgPjwapeeSPH+He/KKfGpJRS7iAi9InsQ5/IPi45viOdjMmAfbHlKrZlOW4jIoFAKeCYg/sqpZRyIUcS/WogUkRqiEgw1s3Vedm2mQcMsr2+B/jNWGOG5gH9bKNyagCRgGefBVZKqQIm164bW5/7MOAXIACYYIzZKCKxQJwxZh7wFTDJdrP1ONYvA2zbzcS6cZsBPGGM0bulSinlRv41w5RSShVQBWeGKaWUUlfRRK+UUn5OE71SSvk5TfRKKeXnvPJmrIikAHtucPcw4KgTw3EWjStvNK680bjyxh/jqmaMCc9phVcm+vwQkbhr3Xn2JI0rbzSuvNG48qagxaVdN0op5ec00SullJ/zx0Q/3tMBXIPGlTcaV95oXHlToOLyuz56pZRSV/LHFr1SSik7muiVUsrP+Uyi99YJyh2I61kR2SQi60RksYhUs1uXKSKJtq/spZ9dHddDIpJid/6/2a0bJCLbbV+Dsu/r4rg+tItpm4ictFvnyus1QUSOiMiGa6wXERlji3udiETbrXPl9cotrgG2eNaLyDIRaWK3brdteaKIOLVKoANxdRCRU3b/XiPs1l33M+DiuF6wi2mD7TNVxrbOlderqoj8bssFG0XkqRy2cd1nzBjj9V9Y5ZF3ADWBYGAt0CDbNo8Dn9le9wNm2F43sG1fGKhhO06AG+PqCBSzvR56KS7b+7MevF4PAWNz2LcMsNP2PdT2OtRdcWXb/kmsstguvV62Y7cHooEN11jfHfgJEKAVsNLV18vBuNpcOh/Q7VJctve7gTAPXa8OwPz8fgacHVe2bXtgzZ3hjutVEYi2vS4BbMvh/6TLPmO+0qL/3wTlxpg04NIE5fZ6AV/bXs8GOotcOUG5MWYXcGmCcrfEZYz53Rhz3vZ2BdYsW67myPW6ltuBRcaY48aYE8AioKuH4uoPTHPSua/LGPMn1lwK19IL+MZYVgClRaQirr1eucZljFlmOy+47/PlyPW6lvx8Np0dlzs/XweNMQm212eAzVw9f7bLPmO+kuhzmqA8+0W6YoJywH6C8tz2dWVc9gZj/ca+pIiIxInIChHp7aSY8hLX3bY/EWeLyKUpH73ietm6uGoAv9ktdtX1csS1Ynfl9cqr7J8vAywUkXgRGeKBeFqLyFoR+UlEGtqWecX1EpFiWMnyW7vFbrleYnUrRwErs61y2WfMayYH93ciMhCIAW6xW1zNGJMsIjWB30RkvTFmh5tC+gGYZoy5KCKPYv011MlN53ZEP2C2uXJGMk9eL68mIh2xEn07u8XtbNerHLBIRLbYWrzukID173VWRLoDc7GmEvUWPYClxhj71r/Lr5eIFMf65fK0Mea0M499Pb7SovfWCcodOraI3Aq8CvQ0xly8tNwYk2z7vhP4L9ZvebfEZYw5ZhfLl0AzR/d1ZVx2+pHtz2oXXi9HXCt2V14vh4hIY6x/w17GmGOXlttdryPAdzivyzJXxpjTxpizttcLgCARCcMLrpfN9T5fLrleIhKEleSnGGPm5LCJ6z5jrrjx4OwvrL88dmL9KX/pBk7DbNs8wZU3Y2faXjfkypuxO3HezVhH4orCuvkUmW15KFDY9joM2I6Tbko5GFdFu9d9gBXm8o2fXbb4Qm2vy7grLtt29bBujIk7rpfdOapz7ZuLd3DljbJVrr5eDsYVgXXfqU225SFACbvXy4CuboyrwqV/P6yEudd27Rz6DLgqLtv6Ulj9+CHuul62n/0b4N/X2cZlnzGnXVxXf2Hdkd6GlTRftS2LxWolAxQBZtk+9KuAmnb7vmrbbyvQzc1x/QocBhJtX/Nsy9sA620f9PXAYDfH9S9go+38vwP17PZ9xHYdk4CH3RmX7f0o4K1s+7n6ek0DDgLpWH2gg4HHgMds6wUYZ4t7PRDjpuuVW1xfAifsPl9xtuU1bddqre3f+VU3xzXM7vO1ArtfRDl9BtwVl22bh7AGaNjv5+rr1Q7rHsA6u3+r7u76jGkJBKWU8nO+0kevlFLqBmmiV0opP6eJXiml/JwmeqWU8nOa6JVSys9poldKKT+niV4ppfzc/wOBXbdarqhfDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(roc_curve(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95240c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6270287141073657"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc1,auc2,thresholds=roc_curve(y_test,y_pred)\n",
    "auccc=auc(auc1,auc2)\n",
    "auc(auc1,auc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df6d7b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=           id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302        17.99         10.38          122.80     1001.0   \n",
       "1      842517        20.57         17.77          132.90     1326.0   \n",
       "2    84300903        19.69         21.25          130.00     1203.0   \n",
       "3    84348301        11.42         20.38           77.58      386.1   \n",
       "4    84358402        20.29         14.34          135.10     1297.0   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564    926424        21.56         22.39          142.00     1479.0   \n",
       "565    926682        20.13         28.25          131.20     1261.0   \n",
       "566    926954        16.60         28.08          108.30      858.1   \n",
       "567    927241        20.60         29.33          140....\n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     symmetry_mean  fractal_dimension_mean  \n",
       "0           0.2419                 0.07871  \n",
       "1           0.1812                 0.05667  \n",
       "2           0.2069                 0.05999  \n",
       "3           0.2597                 0.09744  \n",
       "4           0.1809                 0.05883  \n",
       "..             ...                     ...  \n",
       "564         0.1726                 0.05623  \n",
       "565         0.1752                 0.05533  \n",
       "566         0.1590                 0.05648  \n",
       "567         0.2397                 0.07016  \n",
       "568         0.1587                 0.05884  \n",
       "\n",
       "[569 rows x 11 columns],\n",
       "               with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_f=StandardScaler(x)\n",
    "x_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d56a4abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.36405166e-01,  1.09706398e+00, -2.07333501e+00, ...,\n",
       "         2.53247522e+00,  2.21751501e+00,  2.25574689e+00],\n",
       "       [-2.36403445e-01,  1.82982061e+00, -3.53632408e-01, ...,\n",
       "         5.48144156e-01,  1.39236330e-03, -8.68652457e-01],\n",
       "       [ 4.31741086e-01,  1.57988811e+00,  4.56186952e-01, ...,\n",
       "         2.03723076e+00,  9.39684817e-01, -3.98007910e-01],\n",
       "       ...,\n",
       "       [-2.35727466e-01,  7.02284249e-01,  2.04557380e+00, ...,\n",
       "         1.05777359e-01, -8.09117071e-01, -8.95586935e-01],\n",
       "       [-2.35725168e-01,  1.83834103e+00,  2.33645719e+00, ...,\n",
       "         2.65886573e+00,  2.13719425e+00,  1.04369542e+00],\n",
       "       [-2.42405862e-01, -1.80840125e+00,  1.22179204e+00, ...,\n",
       "        -1.26181958e+00, -8.20069901e-01, -5.61032377e-01]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_p=StandardScaler().fit_transform(x)\n",
    "x_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d24cae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.88150465532582e-17"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "603d2f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.23581339121091452"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(x_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d4908b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([[-0.24307899, -0.50761623, -1.01451947, -0.38550046, -0.40582245,\n",
       "         0.30883801,  0.19631867, -1.11487284, -1.26181958, -0.76895669,\n",
       "        -0.86865246]]), count=array([[ 1,  4,  3,  3,  3,  5,  3, 13, 13,  4,  3]]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(x_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ad1bea3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ftn=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7f00c03d",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>77</td>\n",
       "      <td>21</td>\n",
       "      <td>0.685315</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>0.627029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>81</td>\n",
       "      <td>13</td>\n",
       "      <td>0.657343</td>\n",
       "      <td>0.346667</td>\n",
       "      <td>0.575427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>0.643357</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.560549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>86</td>\n",
       "      <td>9</td>\n",
       "      <td>0.664336</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.566479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>86</td>\n",
       "      <td>11</td>\n",
       "      <td>0.678322</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.584998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2         3         4         5\n",
       "0   3  77  21  0.685315  0.482759  0.627029\n",
       "1   5  81  13  0.657343  0.346667  0.575427\n",
       "2   7  80  12  0.643357  0.320000  0.560549\n",
       "3   9  86   9  0.664336  0.272727  0.566479\n",
       "4  11  86  11  0.678322  0.323529  0.584998"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# h) Change the value of K in KNN with 5,6,7,9,11 and tabulate the various TP, TN,\n",
    "# accuracy, f-score and AUC score obtained. \n",
    "for i in range(3,12,2):\n",
    " knn=KNeighborsClassifier(n_neighbors=i)\n",
    " knn.fit(x_train,y_train)\n",
    " y_pred=knn.predict(x_test)\n",
    " conf_matrix=confusion_matrix(y_test,y_pred)\n",
    " cm=conf_matrix\n",
    " accc=accuracy_score(y_test,y_pred)\n",
    " fsc=f1_score(y_test,y_pred)\n",
    " auc1,auc2,thresholds=roc_curve(y_test,y_pred)\n",
    " auccc=auc(auc1,auc2)\n",
    " ins=[i,cm[0][0],cm[1][1],accc,fsc,auccc]\n",
    " ftn.append(ins)\n",
    "ftdn=pd.DataFrame(ftn)\n",
    "ftdn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c27635c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}