DM_assignment2_knearest_6372106221.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('breast-cancer-wisconsin.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filt = df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
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      "text/plain": [
       "        id  clump_thickness  size_uniformity  shape_uniformity  \\\n",
       "0  1000025                5                1                 1   \n",
       "1  1002945                5                4                 4   \n",
       "2  1015425                3                1                 1   \n",
       "3  1016277                6                8                 8   \n",
       "4  1017023                4                1                 1   \n",
       "\n",
       "   marginal_adhesion  epithelial_size bare_nucleoli  bland_chromatin  \\\n",
       "0                  1                2             1                3   \n",
       "1                  5                7            10                3   \n",
       "2                  1                2             2                3   \n",
       "3                  1                3             4                3   \n",
       "4                  3                2             1                3   \n",
       "\n",
       "   normal_nucleoli  mitoses  class  \n",
       "0                1        1      2  \n",
       "1                2        1      2  \n",
       "2                1        1      2  \n",
       "3                7        1      2  \n",
       "4                1        1      2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>clump_thickness</th>\n",
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       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
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      ],
      "text/plain": [
       "        id  clump_thickness  size_uniformity  shape_uniformity  \\\n",
       "0  1000025                5                1                 1   \n",
       "1  1002945                5                4                 4   \n",
       "2  1015425                3                1                 1   \n",
       "3  1016277                6                8                 8   \n",
       "4  1017023                4                1                 1   \n",
       "\n",
       "   marginal_adhesion  epithelial_size bare_nucleoli  bland_chromatin  \\\n",
       "0                  1                2             1                3   \n",
       "1                  5                7            10                3   \n",
       "2                  1                2             2                3   \n",
       "3                  1                3             4                3   \n",
       "4                  3                2             1                3   \n",
       "\n",
       "   normal_nucleoli  mitoses  class  \n",
       "0                1        1      0  \n",
       "1                2        1      0  \n",
       "2                1        1      0  \n",
       "3                7        1      0  \n",
       "4                1        1      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].replace(2,0,inplace=True)\n",
    "df['class'].replace(4,1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
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       "      <th>count</th>\n",
       "      <td>6.990000e+02</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>699.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.071704e+06</td>\n",
       "      <td>4.417740</td>\n",
       "      <td>3.134478</td>\n",
       "      <td>3.207439</td>\n",
       "      <td>2.806867</td>\n",
       "      <td>3.216023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.437768</td>\n",
       "      <td>2.866953</td>\n",
       "      <td>1.589413</td>\n",
       "      <td>0.344778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.170957e+05</td>\n",
       "      <td>2.815741</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.715078</td>\n",
       "      <td>0.475636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.163400e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.706885e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.171710e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.238298e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.345435e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "                  id  clump_thickness  size_uniformity  shape_uniformity  \\\n",
       "count   6.990000e+02       699.000000       699.000000        699.000000   \n",
       "unique           NaN              NaN              NaN               NaN   \n",
       "top              NaN              NaN              NaN               NaN   \n",
       "freq             NaN              NaN              NaN               NaN   \n",
       "mean    1.071704e+06         4.417740         3.134478          3.207439   \n",
       "std     6.170957e+05         2.815741         3.051459          2.971913   \n",
       "min     6.163400e+04         1.000000         1.000000          1.000000   \n",
       "25%     8.706885e+05         2.000000         1.000000          1.000000   \n",
       "50%     1.171710e+06         4.000000         1.000000          1.000000   \n",
       "75%     1.238298e+06         6.000000         5.000000          5.000000   \n",
       "max     1.345435e+07        10.000000        10.000000         10.000000   \n",
       "\n",
       "        marginal_adhesion  epithelial_size bare_nucleoli  bland_chromatin  \\\n",
       "count          699.000000       699.000000           699       699.000000   \n",
       "unique                NaN              NaN            11              NaN   \n",
       "top                   NaN              NaN             1              NaN   \n",
       "freq                  NaN              NaN           402              NaN   \n",
       "mean             2.806867         3.216023           NaN         3.437768   \n",
       "std              2.855379         2.214300           NaN         2.438364   \n",
       "min              1.000000         1.000000           NaN         1.000000   \n",
       "25%              1.000000         2.000000           NaN         2.000000   \n",
       "50%              1.000000         2.000000           NaN         3.000000   \n",
       "75%              4.000000         4.000000           NaN         5.000000   \n",
       "max             10.000000        10.000000           NaN        10.000000   \n",
       "\n",
       "        normal_nucleoli     mitoses       class  \n",
       "count        699.000000  699.000000  699.000000  \n",
       "unique              NaN         NaN         NaN  \n",
       "top                 NaN         NaN         NaN  \n",
       "freq                NaN         NaN         NaN  \n",
       "mean           2.866953    1.589413    0.344778  \n",
       "std            3.053634    1.715078    0.475636  \n",
       "min            1.000000    1.000000    0.000000  \n",
       "25%            1.000000    1.000000    0.000000  \n",
       "50%            1.000000    1.000000    0.000000  \n",
       "75%            4.000000    1.000000    1.000000  \n",
       "max           10.000000   10.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   645\n",
       "clump_thickness       10\n",
       "size_uniformity       10\n",
       "shape_uniformity      10\n",
       "marginal_adhesion     10\n",
       "epithelial_size       10\n",
       "bare_nucleoli         11\n",
       "bland_chromatin       10\n",
       "normal_nucleoli       10\n",
       "mitoses                9\n",
       "class                  2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     579\n",
       "2      35\n",
       "3      33\n",
       "10     14\n",
       "4      12\n",
       "7       9\n",
       "8       8\n",
       "5       6\n",
       "6       3\n",
       "Name: mitoses, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mitoses'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     402\n",
       "10    132\n",
       "2      30\n",
       "5      30\n",
       "3      28\n",
       "8      21\n",
       "4      19\n",
       "?      16\n",
       "9       9\n",
       "7       8\n",
       "6       4\n",
       "Name: bare_nucleoli, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bare_nucleoli'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    int64\n",
       "clump_thickness       int64\n",
       "size_uniformity       int64\n",
       "shape_uniformity      int64\n",
       "marginal_adhesion     int64\n",
       "epithelial_size       int64\n",
       "bare_nucleoli        object\n",
       "bland_chromatin       int64\n",
       "normal_nucleoli       int64\n",
       "mitoses               int64\n",
       "class                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df.replace('?',np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  clump_thickness  size_uniformity  shape_uniformity  \\\n",
       "0  False            False            False             False   \n",
       "1  False            False            False             False   \n",
       "2  False            False            False             False   \n",
       "3  False            False            False             False   \n",
       "4  False            False            False             False   \n",
       "\n",
       "   marginal_adhesion  epithelial_size  bare_nucleoli  bland_chromatin  \\\n",
       "0              False            False          False            False   \n",
       "1              False            False          False            False   \n",
       "2              False            False          False            False   \n",
       "3              False            False          False            False   \n",
       "4              False            False          False            False   \n",
       "\n",
       "   normal_nucleoli  mitoses  class  \n",
       "0            False    False  False  \n",
       "1            False    False  False  \n",
       "2            False    False  False  \n",
       "3            False    False  False  \n",
       "4            False    False  False  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = df.isnull()\n",
    "missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   1\n",
       "clump_thickness      1\n",
       "size_uniformity      1\n",
       "shape_uniformity     1\n",
       "marginal_adhesion    1\n",
       "epithelial_size      1\n",
       "bare_nucleoli        2\n",
       "bland_chromatin      1\n",
       "normal_nucleoli      1\n",
       "mitoses              1\n",
       "class                1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     418\n",
       "10.0    132\n",
       "5.0      30\n",
       "2.0      30\n",
       "3.0      28\n",
       "8.0      21\n",
       "4.0      19\n",
       "9.0       9\n",
       "7.0       8\n",
       "6.0       4\n",
       "Name: bare_nucleoli, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bare_nucleoli'] = pd.to_numeric(df['bare_nucleoli'], errors='coerce')\n",
    "df['bare_nucleoli'].fillna(1, inplace = True)\n",
    "df['bare_nucleoli'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>size_uniformity</th>\n",
       "      <th>shape_uniformity</th>\n",
       "      <th>marginal_adhesion</th>\n",
       "      <th>epithelial_size</th>\n",
       "      <th>bare_nucleoli</th>\n",
       "      <th>bland_chromatin</th>\n",
       "      <th>normal_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clump_thickness  size_uniformity  shape_uniformity  marginal_adhesion  \\\n",
       "0                5                1                 1                  1   \n",
       "1                5                4                 4                  5   \n",
       "2                3                1                 1                  1   \n",
       "3                6                8                 8                  1   \n",
       "4                4                1                 1                  3   \n",
       "\n",
       "   epithelial_size  bare_nucleoli  bland_chromatin  normal_nucleoli  mitoses  \\\n",
       "0                2            1.0                3                1        1   \n",
       "1                7           10.0                3                2        1   \n",
       "2                2            2.0                3                1        1   \n",
       "3                3            4.0                3                7        1   \n",
       "4                2            1.0                3                1        1   \n",
       "\n",
       "   class  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop(['id'],axis=1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['clump_thickness', 'size_uniformity', 'shape_uniformity',\n",
       "       'marginal_adhesion', 'epithelial_size', 'bare_nucleoli',\n",
       "       'bland_chromatin', 'normal_nucleoli', 'mitoses', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.  1.  1.  1.  2.  1.  3.  1.  1.]\n",
      " [ 5.  4.  4.  5.  7. 10.  3.  2.  1.]\n",
      " [ 3.  1.  1.  1.  2.  2.  3.  1.  1.]\n",
      " [ 6.  8.  8.  1.  3.  4.  3.  7.  1.]\n",
      " [ 4.  1.  1.  3.  2.  1.  3.  1.  1.]]\n",
      "(699, 9)\n"
     ]
    }
   ],
   "source": [
    "X = df1[['clump_thickness', 'size_uniformity', 'shape_uniformity',\n",
    "       'marginal_adhesion', 'epithelial_size', 'bare_nucleoli',\n",
    "       'bland_chromatin', 'normal_nucleoli', 'mitoses']].values\n",
    "print(X[0:5])\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df1['class'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.20693572, -0.69999505, -0.74329904, -0.63324716, -0.54956136,\n",
       "        -0.68697917, -0.17966213, -0.61182504, -0.34391178],\n",
       "       [ 0.20693572,  0.28384518,  0.2668747 ,  0.76862067,  1.71010564,\n",
       "         1.79966407, -0.17966213, -0.28411186, -0.34391178],\n",
       "       [-0.50386559, -0.69999505, -0.74329904, -0.63324716, -0.54956136,\n",
       "        -0.41068547, -0.17966213, -0.61182504, -0.34391178],\n",
       "       [ 0.56233637,  1.59563215,  1.61377302, -0.63324716, -0.09762796,\n",
       "         0.14190191, -0.17966213,  1.35445405, -0.34391178],\n",
       "       [-0.14846494, -0.69999505, -0.74329904,  0.06768675, -0.54956136,\n",
       "        -0.68697917, -0.17966213, -0.61182504, -0.34391178]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(489, 9) (489,)\n",
      "(210, 9) (210,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 42)\n",
    "print(X_train.shape,y_train.shape)\n",
    "print(X_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When k =  1\n"
     ]
    },
    {
     "data": {
      "image/png": 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GxBrIgqCkMemw8cA9JaetTGndcoAzs9xyPCayLiKmlTtA0jDgOuCLEbFR6jZ4VjSZfCkHODPLJRDtVepFldRKFtyuiojrU/LTksal2ts44JmUnnsyed+DM7PcosKlHGVVtZ8Cj0XEf5TsmgucnNZPBm4oSZ8paWCaUH4KcF+5a7gGZ2b5RNXeRT0K+DjwiKSHUtrXgYuAOZJOAZYDHwaIiAWS5gALyXpgz4iItnIXcIAzs/yq8KpWRNxN1/fVAN7dzTkXABdUeg0HODPLrelHE5H0A8rE6Yj4fE1KZGYNLYD29iYPcMD9ZfaZWV8VQLPX4CLiytJtSUMj4uXaF8nMGl2zDJfU42Miko6UtJDsNQokHSLpkpqXzMwaVzWeE+kFlTwH9z1gBrAeICIeBo6pYZnMrKFV9h5qI3REVNSLGhErOr0+UfbZEzMruAaonVWikgC3QtI7gJA0APg8qblqZn1QQDRJL2olTdTTgTPI3tpfBUxN22bWZ6nCpb56rMFFxDrgY71QFjNrFk3SRK2kF3UfSb+V9KykZyTdIGmf3iicmTWoAvWiXg3MAcYBewHXAtfUslBm1sA6HvStZKmzSgKcIuIXEbE1Lb+kIWKzmdVLNSad6Q3l3kUdmVb/KOksYDZZYPsIcGMvlM3MGlWT9KKW62R4gCygdfwmp5XsC+D8WhXKzBqbGqB2Voly76JO7s2CmFmTaJAOhEpU9CaDpIOBA4FBHWkR8fNaFcrMGlljdCBUoscAJ+kcYDpZgLsJOB64G3CAM+urmqQGV0kv6t+SDR+8NiI+BRwCDKxpqcyssbVXuNRZJU3UVyOiXdJWSSPIpvDyg75mfVURBrwscb+kNwCXkvWsvkQPU3WZWbE1fS9qh4j4XFr9saSbgRERMb+2xTKzhtbsAU7SYeX2RcSDtSmSmVl1lKvBfafMvgCOrXJZeGL+EGbsNbXa2VoNPXHp2+pdBMth0/l/rko+Td9EjYh39WZBzKxJBE3zqlYlj4mYmW2vSsMlSbo8DcP2aEnauZJWSXooLe8v2Xe2pCWSFkma0VP+DnBmlpuisqUCVwDHdZH+3YiYmpabACQdCMwEDkrnXCKppVzmDnBmll+VanARcRfwXIVXPRGYHRGbImIpsAQ4vNwJlYzoK0l/L+lf0vZESWUzNbOCqzzAjZJ0f8lyaoVXOFPS/NSE3T2ljQdWlByzMqV1q5Ia3CXAkcBH0/aLwMUVFtLMCqbS5mlqoq6LiGkly6wKLvEjYF+yCa7W8PoTHV31bJStJ1byJsMREXGYpL8ARMTzafpAM+uratiLGhFPd6xLuhT4XdpcCUwoOXRvYHW5vCqpwW1JN/IiXXA0DfEarZnVSxU7GXbMWxpXsvkhoKOHdS4wU9JASZOBKfTw2mglNbjvA78Gxki6gGx0kW/mLrWZFUeVHvSVdA3ZcGyjJK0EzgGmS5qarrKMNJp4RCyQNAdYCGwFzoiItnL5V/Iu6lWSHiAbMknASRHhme3N+qpdqJ3tkFXER7tI/mmZ4y8ALqg0/0oGvJwIvAL8tjQtIpZXehEzK5hmf1WrxI28PvnMIGAysIjsYTsz64PUJHfhK2mivqV0O40yclo3h5uZNYyKJp0pFREPSvIQEmZ9WVGaqJK+VLLZDzgMeLZmJTKzxlbFToZaq6QGN7xkfSvZPbnralMcM2sKRQhw6QHfYRHx1V4qj5k1g2YPcJL6R8TWckOXm1nfI4rRi3of2f22hyTNBa4FXu7YGRHX17hsZtaICnYPbiSwnmwOho7n4QJwgDPrqwoQ4MakHtRHeT2wdWiSX8/MaqJJIkC5ANcCDGMnxmAys2IrQhN1TUR8q9dKYmbNowABrjnmBTOz3hXF6EV9d6+VwsyaS7PX4CKi0pluzKyPKcI9ODOzrjnAmVkhVTjnaSNwgDOzXISbqGZWYA5wZlZcDnBmVlgOcGZWSAUbTcTMbHsOcGZWVEV4VcvMrEvN0kTtV+8CmFmTiRxLDyRdLukZSY+WpI2UdJukxenn7iX7zpa0RNIiSTN6yt8Bzszyq1KAA64AjuuUdhZwe0RMAW5P20g6EJgJHJTOuSRNjNUtBzgzy6XjTYZKlp5ExF1A54E9TgSuTOtXAieVpM+OiE0RsRRYAhxeLn/fgzOz3NRe8U24UZLuL9meFRGzejhnbESsAYiINZLGpPTxwD0lx61Mad1ygDOzfPK9bL8uIqZV6cq5p09wE9XMcqtWE7UbT0saB5B+PpPSVwITSo7bG1hdLiMHODPLr3qdDF2ZC5yc1k8GbihJnylpoKTJwBSy+Zu75SaqmeVWrefgJF0DTCe7V7cSOAe4CJgj6RRgOfBhgIhYIGkOsBDYCpwREW3l8neAM7P8qhTgIuKj3ezqck6YiLgAuKDS/B3gzCyfgsyqZWa2A4/oa2bFFs0R4RzgzCw31+CMadM3cvr5q2npF/z+mpHM+eHYehfJgLFXLGXo/A20DW/lqfMOBmDcT5bQuvY1AFpebaNtcAvLzzl42zn9129i0jmPsv6EvXh+xri6lLtheFatbJQA4H8Cz0TEwT0dXzT9+gVnXLiKs2fuw7o1rfzgpsXcc8tuLF88qN5F6/M2vmMUG941hj0vX7otbc1p+21bHzVnOe2Dt3+He/ScFbx88G69VsZG1yydDLV80PcKdhwloM/Y/9BXWL1sAGuXD2Trln7cecMbOHLGC/UulgGvvmk4bUO7+b89guH3P8eLh++xLWnoX55ny6iBbN5rcC+VsPGpvbKl3moW4LoZJaDP2GPPLTy7esC27XVrWhk1bksdS2SVGLz4JdpGtLJlbFbT1qY2Rt68hvUn7FXnkjWQIOtkqGSps7rfg5N0KnAqwCCG1Lk01aMuXgtugO/bejD8vvXb1d72mLuK59+zJzGo7LBjfY47GSqUhk6ZBTBCI5vkY+vZujWtjN5r87btUeO2sH5tax1LZD1qC4Y9+DzLv3nQtqRBT77M8AeeZ/R1K+j3ShsIorUfG47t4x1GTfKXWvcAV1SLHhrC+MmbGTthE+vXtjL9xA1cdMYb610sK2PIYxvZPG4wW0e+fmth5dfevG19j7mraB/o4OYHfY32NnHxN8Zz4dVP0q8Fbp09kqeecA9qI9hz1n8z5IkXaXlpK5O/+hDrPziejUePzpqnbxtZ7+I1vog8A17WVS0fE9lhlICI+GmtrteI5t0xgnl3jKh3MayTtafu22X605/ep+x56z9YdvDYvqU54lvtAlyZUQLMrMm5iWpmxRRAX2+imlmBNUd8c4Azs/zcRDWzwurzvahmVlAeTcTMiip70Lc5IpwDnJnl1wAjhVTCAc7McnMNzsyKyffgzKy4/C6qmRWZm6hmVkhVnPhZ0jLgRaAN2BoR0ySNBH4FTAKWAf8rIp7fmfxrOSeDmRVVdYcsf1dETI2IaWn7LOD2iJgC3J62d4oDnJnlFxUuO+dE4Mq0fiVw0s5m5ABnZrmpvb2ihWw8yPtLllM7ZRXArZIeKNk3NiLWAKSfY3a2nL4HZ2b5BHke9F1X0vTsylERsVrSGOA2SY/vavFKuQZnZrmIQFHZ0pOIWJ1+PgP8GjgceFrSOID085mdLasDnJnlV4VOBklDJQ3vWAfeBzwKzAVOToedDNyws8V0E9XM8qvOc3BjgV8rm0S4P3B1RNwsaR4wR9IpwHLgwzt7AQc4M8sn3z247rOJeBI4pIv09cC7d/0KDnBmthNSD2nDc4Azs5xyPcRbVw5wZpZP4ABnZgXWHC1UBzgzy88DXppZcTnAmVkhRUBbc7RRHeDMLD/X4MyssBzgzKyQAvCcDGZWTAHhe3BmVkSBOxnMrMB8D87MCssBzsyKyS/bm1lRBeDhksyssFyDM7Ni8qtaZlZUAeHn4MyssPwmg5kVlu/BmVkhRbgX1cwKzDU4MyumINra6l2IijjAmVk+Hi7JzAqtSR4T6VfvAphZcwkg2qOipSeSjpO0SNISSWdVu6wOcGaWT6QBLytZypDUAlwMHA8cCHxU0oHVLKqbqGaWW5U6GQ4HlkTEkwCSZgMnAgurkTmAooG6eyU9CzxV73LUwChgXb0LYbkU9Tt7Y0SM3pUMJN1M9vlUYhDwWsn2rIiYlfL5W+C4iPhM2v44cEREnLkr5SvVUDW4Xf3gG5Wk+yNiWr3LYZXzd9a9iDiuSlmpq+yrlDfge3BmVj8rgQkl23sDq6t5AQc4M6uXecAUSZMlDQBmAnOreYGGaqIW2Kx6F8By83dWYxGxVdKZwC1AC3B5RCyo5jUaqpPBzKya3EQ1s8JygDOzwnKAq6Fav4Zi1SfpcknPSHq03mWxXecAVyO98RqK1cQVQLWe87I6c4CrnW2voUTEZqDjNRRrYBFxF/Bcvcth1eEAVzvjgRUl2ytTmpn1Ege42qn5ayhmVp4DXO3U/DUUMyvPAa52av4aipmV5wBXIxGxFeh4DeUxYE61X0Ox6pN0DfBnYH9JKyWdUu8y2c7zq1pmVliuwZlZYTnAmVlhOcCZWWE5wJlZYTnAmVlhOcA1EUltkh6S9KikayUN2YW8rkizGiHpsnIDAUiaLukdO3GNZZJ2mH2pu/ROx7yU81rnSvpK3jJasTnANZdXI2JqRBwMbAZOL92ZRjDJLSI+ExHl5qKcDuQOcGb15gDXvP4E7JdqV3+UdDXwiKQWSf8uaZ6k+ZJOA1Dmh5IWSroRGNORkaQ7JU1L68dJelDSw5JulzSJLJD+U6o9Hi1ptKTr0jXmSToqnbuHpFsl/UXST+j6fdztSPqNpAckLZB0aqd930lluV3S6JS2r6Sb0zl/knRAVT5NKyRPOtOEJPUnG2fu5pR0OHBwRCxNQeKFiHibpIHAf0m6FTgU2B94CzCWbPbwyzvlOxq4FDgm5TUyIp6T9GPgpYj4djruauC7EXG3pIlkb2u8GTgHuDsiviXpA8B2Aasbn07XGAzMk3RdRKwHhgIPRsSXJf1LyvtMsslgTo+IxZKOAC4Bjt2Jj9H6AAe45jJY0kNp/U/AT8majvdFxNKU/j7grR3314DdgCnAMcA1EdEGrJZ0Rxf5vx24qyOviOhuXLT3AAdK2ypoIyQNT9f463TujZKer+B3+rykD6X1Cams64F24Fcp/ZfA9ZKGpd/32pJrD6zgGtZHOcA1l1cjYmppQvpDf7k0CfjHiLil03Hvp+fhmlTBMZDd2jgyIl7toiwVv/snaTpZsDwyIl6RdCcwqJvDI113Q+fPwKw7vgdXPLcAn5XUCiDpTZKGAncBM9M9unHAu7o498/AOyVNTueOTOkvAsNLjruVrLlIOm5qWr0L+FhKOx7YvYey7gY8n4LbAWQ1yA79gI5a6N+RNX03AkslfThdQ5IO6eEa1oc5wBXPZWT31x5ME6f8hKym/mtgMfAI8CPgPzufGBHPkt03u17Sw7zeRPwt8KGOTgbg88C01ImxkNd7c88DjpH0IFlTeXkPZb0Z6C9pPnA+cE/JvpeBgyQ9QHaP7Vsp/WPAKal8C/Aw8FaGRxMxs8JyDc7MCssBzswKywHOzArLAc7MCssBzswKywHOzArLAc7MCuv/AwUm3XKPgp5KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  1.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision  recall  f1-score  support\n",
       "0        1.0     1.0       1.0    315.0\n",
       "1        1.0     1.0       1.0    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9571428571428572\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952703</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.969072</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.967742</td>\n",
       "      <td>0.895522</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.952703  0.986014  0.969072    143.0\n",
       "1   0.967742  0.895522  0.930233     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9496523615439942\n",
      "\n",
      "When k =  2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.9713701431492843\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.957447</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.91954</td>\n",
       "      <td>0.958084</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision   recall  f1-score  support\n",
       "0   0.957447  1.00000  0.978261    315.0\n",
       "1   1.000000  0.91954  0.958084    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9681723509502733\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9380952380952381\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.927632</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.955932</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.965517</td>\n",
       "      <td>0.835821</td>\n",
       "      <td>0.896000</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.927632  0.986014  0.955932    143.0\n",
       "1   0.965517  0.835821  0.896000     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9259661016949153\n",
      "\n",
      "When k =  3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.9795501022494888\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.987220</td>\n",
       "      <td>0.980952</td>\n",
       "      <td>0.984076</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.965909</td>\n",
       "      <td>0.977011</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.987220  0.980952  0.984076    315.0\n",
       "1   0.965909  0.977011  0.971429    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9777525022747953\n"
     ]
    },
    {
     "data": {
      "image/png": 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SDpO0EHg43T5Y0sVVj8zM6leUuNRYKaP1fghMANYCRMQDwBFVjMnM6pqIKG2ptZJ6USNimfSKYIu+i9DMcq4OamelKKUGt0zS4UBI2knSl0mbq2a2AwqIVpW0dEXSFyQtkPSQpOmSekkaIOlWSY+mP3crN9RSEtxpwOnAMOBJ4JB028x2WCpxKVKCNAz4HDAmIg4EmoBJwJnArIgYCcxKt8vSZRM1ItYAJ5Z7ATPLoco1UbsDO0vaCvQGVgBnAePT45cDtwNfLafwUnpRXyPpJkmrJa2SdIOk15RzMTPLidJ7UQdKmluwnPJSERFPAt8DlgIrgeci4hZgcESsTM9ZCexRbpildDJcDfwUeH+6PQmYDhxa7kXNrIFlG+i7JiLGdHQgvbc2EdgXeBa4TtJHKxFim1LuwSkiroiI5nS5kobpQzGzaogobenC0cCSiFgdEVuB64HDgaclDQFIf64qN85OE1zakzEAuE3SmZJGSNpH0hnA78u9oJnlQKtKW4pbCoyT1FvJOLSjSEZo3AhMTs+ZDNxQbpjFmqj3ktTU2qI8teBYAN8s96Jm1thUgTZcRNwt6TfAPKAZuA+YCvQFZkiaQpIEjy/3GsXmou5bbqFmlmMVnIYVEecA57TbvZmkNrfNSprJIOlAYDTQqyCwX1ciADNrNGr8p4m0kXQOyZiU0cAfgGOBOwAnOLMdVYN0M5bSi/pBkuriUxFxMnAw0LOqUZlZfWstcamxUpqomyKiVVKzpF1Iumw90NdsR5WHB14WmCtpV+CXJD2rLwBzqhmUmdW3SvSibg+lzEX9dLr6c0k3A7tExPzqhmVmda3RE5ykNxU7FhHzqhOSmVllFKvBXVjkWADvqHAs/GN+byYMe2Oli7UqeuK8cbUOwTLY8rO7KlJOwzdRI+LI7RmImTWIoJRpWHXBL342s+wavQZnZtaZhm+impl1qkESXClP9JWkj0r6Rrq9t6Sx1Q/NzOpWjt6LejFwGHBCur2e5Am/ZrYDUpS+1FopTdRDI+JNku4DiIhnJO1U5bjMrJ7lqBd1q6Qm0gqnpEHUxTRaM6uVeqidlaKUJuqPgd8Be0i6gORRSd+qalRmVt8a5B5cKXNRr5J0L8kjkwQcFxF+s73ZjqpO7q+VopQHXu4NbARuKtwXEUurGZiZ1bG8JDiSN2i1vXymF8k7DBcBB1QxLjOrY2qQu/ClNFEPKtxOnzJyaienm5nVjcwzGSJinqQ3VyMYM2sQeWmiSvpiwWY34E3A6qpFZGb1rYKdDOnTwi8BDkxK5hMkt8CuBUYAjwMfiohnyim/lGEi/QqWniT35CaWczEzy4nKDRP5EXBzROxP8kKrh4EzgVkRMRKYlW6XpWgNLh3g2zcivlLuBcwshypQg0tfYnUEcBJARGwBtkiaSPKqUoDLgduBr5ZzjU5rcJK6R0QLSZPUzAxIhlOotbQFGChpbsFySkFRryG53fUrSfdJukRSH2BwRKwESH/uUW6sxWpwc0iS2/2SbgSuAza0HYyI68u9qJk1sGz34NZExJhOjnUnyTGfjYi7Jf2IbWiOdnaBrgwA1pK8g6FtPFwATnBmO6rKdDIsB5ZHxN3p9m9IEtzTkoZExEpJQ0jexVyWYgluj7QH9SFeTmxtGqST2MyqogIZICKekrRM0qiIWEQyHXRhukwGvpP+vKHcaxRLcE1AX16Z2F6KrdwLmlnjq+Bc1M8CV6WPYHsMOJmkb2CGpCnAUuD4cgsvluBWRsR55RZsZjlWoQQXEfcDHd2jO6oS5RdLcI3xRDsz274iH3NRK5JBzSyHGuQmVbEXP6/bnoGYWePIzfPgzMxexQnOzHKpTh5HXgonODPLRLiJamY55gRnZvnlBGdmueUEZ2a5lKfXBpqZvYoTnJnlVR6mapmZdchNVDPLJw/0NbNcc4IzszzyTAYzyzW1NkaGc4Izs2x8D87M8sxNVDPLLyc4M8sr1+DMLL8aJMF1q3UAZtZg0rdqlbKUQlKTpPsk/V+6PUDSrZIeTX/uVm6oTnBmlknbOLhSlhJ9Hni4YPtMYFZEjARmpdtlcYIzs+wiSlu6IGkv4N3AJQW7JwKXp+uXA8eVG6bvwZlZZhlqZwMlzS3YnhoRUwu2fwicAfQr2Dc4IlYCRMRKSXuUG6cTXJX06NnKhb9dTI+erTQ1wV9/358rLhxS67CsA/122sz5b/sLI3dbRwBfmz2e+1ftCcAnDrqfMw69i3FXTObZzTvXNtB6kW2g75qIGNPRAUnvAVZFxL2SxlcktnaqluAkXQa0/QIHVus69WrrZnHGh/bjxY1NNHUPvv+7R7nntl14ZF6fWodm7Xxt3N/46/LhfH7WO+nRrYVe3ZsB2LPPCxw+bDlPru9b4wjrT4WeB/cW4H2S3gX0AnaRdCXwtKQhae1tCLCq3AtU8x7cNOCYKpZf58SLG5sA6N49aOoRpdySsO2sT48tjBmykt8s2h+Ara1NrN/SE4Czxv2d784ZV8vw6lYlelEj4qyI2CsiRgCTgD9HxEeBG4HJ6WmTgRvKjbNqNbiImC1pRLXKbwTdugUX3byIoSO2cNO0gSy6z7W3ejO83/Os29SLbx9xG6MGrGXB2kF86863MG7okzy9oTeL1g2sdYj1JyipA2EbfAeYIWkKsBQ4vtyCan4PTtIpwCkAvehd42gqq7VVfPqd+9Nnl2bOufRx9hm1iScW+T5OPenerZXRA9dw/p1vZf7qwZw97g4+86a5jNlzJVP++O5ah1e3Kj2TISJuB25P19cCR1Wi3JoPE4mIqRExJiLG9KBnrcOpig3Pd+eBv/flzePX1zoUa+epDX15ekMf5q8eDMDMJfsxevc17NXveW74t+uY9eErGdxnA9e//7cM3HljjaOtI1HiUmM1r8HlVf8BzTQ3J8ltp16tvOlt65lxcdm93VYlazb1ZuWGvuzb/1mWPLcrhw1bzsK1Azn5j+996ZxZH76SD/zvB9yLmvIDL40Bg7fy5R8upVu3oFs3mH3Trtz9p/61Dss6cP7f38p3x8+iR1MLy57fhbNnH1nrkOpbhB94KWk6MJ5koN9y4JyIuLRa16s3Sx7emdMnjKp1GFaCR9YN5IM3fKDT40dd+9HtGE2DaIz8VtVe1BOqVbaZ1ZabqGaWTwHs6E1UM8uxxshvTnBmlp2bqGaWWzt8L6qZ5VSdDOIthROcmWWSDPRtjAznBGdm2VXmcUlV5wRnZpm5Bmdm+eR7cGaWX56LamZ55iaqmeVSVOydDFXnBGdm2bkGZ2a51Rj5zQnOzLJTa2O0UZ3gzCybwAN9zSyfRDTMQN+av1XLzBpQRGlLEZKGS7pN0sOSFkj6fLp/gKRbJT2a/tyt3DCd4MwsuwokOKAZ+FJEvB4YB5wuaTRwJjArIkYCs9LtsjjBmVk2bffgSlmKFROxMiLmpevrgYeBYcBE4PL0tMuB48oN1ffgzCyzSveiShoBvBG4GxgcESshSYKSyn6hsBOcmWVUUvOzzUBJcwu2p0bE1MITJPUFfgv8R0Q8L6lCcTrBmVlWQZYEtyYixnR2UFIPkuR2VURcn+5+WtKQtPY2BFhVbqi+B2dm2VXgHpySqtqlwMMR8f2CQzcCk9P1ycAN5YbpGpyZZVahcXBvAT4GPCjp/nTf2cB3gBmSpgBLgePLvYATnJllV4EEFxF3kLzioSNHbfMFcIIzs6wioKUx5mo5wZlZdg0yVcsJzsyyc4Izs1wKwO9kMLN8CgjfgzOzPArcyWBmOeZ7cGaWW05wZpZPmSbb15QTnJllE4BfOmNmueUanJnlk6dqmVleBYTHwZlZbnkmg5nllu/BmVkuRbgX1cxyzDU4M8unIFpaah1ESZzgzCwbPy7JzHLNw0TMLI8CCNfgzCyXwg+8NLMca5ROBkUddfdKWg08Ues4qmAgsKbWQVgmef0z2yciBm1LAZJuJvl+SrEmIo7Zlutti7pKcHklaW5EjKl1HFY6/5nlQ7daB2BmVi1OcGaWW05w28fUWgdgmfnPLAd8D87Mcss1ODPLLSc4M8stJ7gqknSMpEWSFks6s9bxWNckXSZplaSHah2LbTsnuCqR1AT8FDgWGA2cIGl0baOyEkwDajYw1SrLCa56xgKLI+KxiNgCXANMrHFM1oWImA2sq3UcVhlOcNUzDFhWsL083Wdm24kTXPWog30ek2O2HTnBVc9yYHjB9l7AihrFYrZDcoKrnnuAkZL2lbQTMAm4scYxme1QnOCqJCKagc8AM4GHgRkRsaC2UVlXJE0H7gRGSVouaUqtY7LyeaqWmeWWa3BmlltOcGaWW05wZpZbTnBmlltOcGaWW05wDURSi6T7JT0k6TpJvbehrGmSPpiuX1LsQQCSxks6vIxrPC7pVW9f6mx/u3NeyHitcyV9OWuMlm9OcI1lU0QcEhEHAluA0woPpk8wySwi/j0iFhY5ZTyQOcGZ1ZoTXOP6K/DatHZ1m6SrgQclNUn6rqR7JM2XdCqAEhdJWijp98AebQVJul3SmHT9GEnzJD0gaZakESSJ9Atp7fFtkgZJ+m16jXskvSX97O6SbpF0n6Rf0PF83FeQ9L+S7pW0QNIp7Y5dmMYyS9KgdN9+km5OP/NXSftX5Nu0XPKb7RuQpO4kz5m7Od01FjgwIpakSeK5iHizpJ7A3yTdArwRGAUcBAwGFgKXtSt3EPBL4Ii0rAERsU7Sz4EXIuJ76XlXAz+IiDsk7U0yW+P1wDnAHRFxnqR3A69IWJ34RHqNnYF7JP02ItYCfYB5EfElSd9Iy/4MyctgTouIRyUdClwMvKOMr9F2AE5wjWVnSfen638FLiVpOs6JiCXp/ncCb2i7vwb0B0YCRwDTI6IFWCHpzx2UPw6Y3VZWRHT2XLSjgdHSSxW0XST1S6/xb+lnfy/pmRJ+p89Jen+6PjyNdS3QClyb7r8SuF5S3/T3va7g2j1LuIbtoJzgGsumiDikcEf6D31D4S7gsxExs91576LrxzWphHMgubVxWERs6iCWkuf+SRpPkiwPi4iNkm4HenVyeqTXfbb9d2DWGd+Dy5+ZwKck9QCQ9DpJfYDZwKT0Ht0Q4MgOPnsn8HZJ+6afHZDuXw/0KzjvFpLmIul5h6Srs4ET033HArt1EWt/4Jk0ue1PUoNs0w1oq4V+hKTp+zywRNLx6TUk6eAurmE7MCe4/LmE5P7avPTFKb8gqan/DngUeBD4GfCX9h+MiNUk982ul/QALzcRbwLe39bJAHwOGJN2Yizk5d7c/wKOkDSPpKm8tItYbwa6S5oPfBO4q+DYBuAASfeS3GM7L91/IjAljW8Bfgy8FeGniZhZbrkGZ2a55QRnZrnlBGdmueUEZ2a55QRnZrnlBGdmueUEZ2a59f8xldp37CU86wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9761904761904762\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.979167</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.982578</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.955224</td>\n",
       "      <td>0.962406</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.979167  0.986014  0.982578    143.0\n",
       "1   0.969697  0.955224  0.962406     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9724922061250687\n",
      "\n",
      "When k =  4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.9631901840490797\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.959752</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.971787</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.969880</td>\n",
       "      <td>0.925287</td>\n",
       "      <td>0.947059</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.959752  0.984127  0.971787    315.0\n",
       "1   0.969880  0.925287  0.947059    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9594228286926054\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9619047619047619\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.959184</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.972414</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968254</td>\n",
       "      <td>0.910448</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.959184  0.986014  0.972414    143.0\n",
       "1   0.968254  0.910448  0.938462     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9554376657824935\n",
      "\n",
      "When k =  5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.967280163599182\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977636</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.974522</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.948864</td>\n",
       "      <td>0.959770</td>\n",
       "      <td>0.954286</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.977636  0.971429  0.974522    315.0\n",
       "1   0.948864  0.959770  0.954286    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9644040036396724\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9666666666666667\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.965753</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.975779</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.925373</td>\n",
       "      <td>0.946565</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.965753  0.986014  0.975779    143.0\n",
       "1   0.968750  0.925373  0.946565     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.961171716104493\n",
      "\n",
      "When k =  6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.9652351738241309\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.971519</td>\n",
       "      <td>0.974603</td>\n",
       "      <td>0.973059</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.953757</td>\n",
       "      <td>0.948276</td>\n",
       "      <td>0.951009</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.971519  0.974603  0.973059    315.0\n",
       "1   0.953757  0.948276  0.951009    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9620336413085675\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9666666666666667\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.965753</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.975779</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.925373</td>\n",
       "      <td>0.946565</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.965753  0.986014  0.975779    143.0\n",
       "1   0.968750  0.925373  0.946565     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.961171716104493\n",
      "\n",
      "When k =  7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.9652351738241309\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977564</td>\n",
       "      <td>0.968254</td>\n",
       "      <td>0.972887</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.943503</td>\n",
       "      <td>0.959770</td>\n",
       "      <td>0.951567</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.977564  0.968254  0.972887    315.0\n",
       "1   0.943503  0.959770  0.951567    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9622268569636989\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9761904761904762\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.979167</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.982578</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.955224</td>\n",
       "      <td>0.962406</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.979167  0.986014  0.982578    143.0\n",
       "1   0.969697  0.955224  0.962406     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9724922061250687\n",
      "\n",
      "When k =  8\n"
     ]
    },
    {
     "data": {
      "image/png": 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Our4OeJLkZVUnAlenh10NnJSunwjMjIjNEbEQWEDy9r4OOcGZWdnKaKIOkfRw0XJme+eTNBY4GHgQGB4RS5PrxFKg9d2a7b2pb2SpON3JYGZlK6MXdUVETCp1gKR+wA3AZyNirdThuTO9qa+Ya3BmVpakdqZMS2ck9SBJbtdGxI1p8TJJI9L9I4DlaXnZb+pzgjOzslXitYFKqmpXAE9GxP8U7boFOD1dPx34TVH5dEm90rf1jQNml7qGm6hmVrbOHgHJ6HDgw8Djkh5Nyy4EvgvMknQG8AJwSnLNmCdpFjCfpAf2nIgolLqAE5yZlSUQLRUYqhUR99P+fTWAozv4zAxgRtZrOMGZWdkqU4GrPic4MytPeCyqmeVZg1ThnODMrGwNX4OT9GNK5OmI+HRVIjKzuhZAS0uDJzjg4V0WhZk1jgAavQYXEVcXb0vqGxEbqh+SmdW7Cj0HV3WdPswiaYqk+SQj/ZF0kKSLqx6ZmdWvyLjUWJan9X4IHAesBIiIx4AjqxiTmdW1bONQ66EjIlMvakQsbjPCv+TwCDPLuTqonWWRJcEtlvROICT1BD5N2lw1sy4oIBqkFzVLE/Vs4BySieVeBCam22bWZSnjUlud1uAiYgXwoV0Qi5k1igZpombpRd1H0m8lvSJpuaTfSNpnVwRnZnUqR72ovwJmASOAvYDrgeuqGZSZ1bHWB32zLDWWJcEpIq6JiOZ0+SV1kZvNrFYq8V7UXaHUWNRB6eo9ks4HZpIktlOBW3dBbGZWrxqkF7VUJ8MjJAmt9Ts5q2hfAN+qVlBmVt9UB7WzLEqNRd17VwZiZg2iTjoQssg0kkHSgcB4oHdrWUT8olpBmVk9q48OhCw6TXCSvgZMJUlwtwHHA/cDTnBmXVWD1OCy9KL+C8kbbl6OiI8CBwG9qhqVmdW3loxLjWVpom6MiBZJzZJ2J3nLtB/0Neuq8jDhZZGHJe0BXEbSs7qeTt4mbWb51vC9qK0i4pPp6k8l3Q7sHhFzqxuWmdW1Rk9wkg4ptS8i5lQnJDOzyihVg/t+iX0BHFXhWHhm7m4ct9fESp/WquiZSw6tdQhWhs3f/v8VOU/DN1Ej4l27MhAzaxBBwwzVyvKYiJnZtio0XZKkK9Np2J4oKvu6pBclPZouJxTtu0DSAklPSzqus/M7wZlZ2RTZlgyuAqa1U/6DiJiYLrcBSBoPTAcmpJ+5WFJTqZM7wZlZ+SpUg4uI+4BVGa96IjAzIjZHxEJgATC51AeyzOgrSf8q6avp9hhJJU9qZjmXPcENkfRw0XJmxiucK2lu2oQdmJaNBBYXHbMkLetQlhrcxcAU4LR0ex1wUcYgzSxnsjZP0ybqioiYVLRcmuESlwD7krzgailvPNHRXs9GyXpilpEM74iIQyT9DSAiVqevDzSzrqqKvagRsax1XdJlwO/SzSXA6KJDRwEvlTpXlhrc1vRGXqQXHEpdDKM1s1qpYCfD9ueWRhRtvg9o7WG9BZguqZekvYFxdDJsNEsN7kfATcAwSTNIZhf5StlRm1l+VOhBX0nXkUzHNkTSEuBrwFRJE9OrLCKdTTwi5kmaBcwHmoFzIqJQ6vxZxqJeK+kRkimTBJwUEX6zvVlXtRO1s+1OFXFaO8VXlDh+BjAj6/mzTHg5BngN+G1xWUS8kPUiZpYzjT5Uq8itvPHymd7A3sDTJA/bmVkXpAa5C5+lifrW4u10lpGzOjjczKxuZHrpTLGImCPJU0iYdWV5aaJK+lzRZjfgEOCVqkVkZvWtgp0M1ZalBte/aL2Z5J7cDdUJx8waQh4SXPqAb7+I+MIuisfMGkGjJzhJ3SOiudTU5WbW9Yh89KLOJrnf9qikW4DrgQ2tOyPixirHZmb1KGf34AYBK0newdD6PFwATnBmXVUOEtywtAf1Cd5IbK0a5Nszs6pokAxQKsE1Af3YgTmYzCzf8tBEXRoR39xlkZhZ48hBgmuM94KZ2a4V+ehFPXqXRWFmjaXRa3ARkfVNN2bWxeThHpyZWfuc4MwslzK+87QeOMGZWVmEm6hmlmNOcGaWX05wZpZbTnBmlks5m03EzGxbTnBmlld5GKplZtYuN1HNLJ8a6EHfbrUOwMwaUGRcOiHpSknLJT1RVDZI0p2Snk2/Dizad4GkBZKelnRcZ+d3gjOzsrSOZMiyZHAVMK1N2fnA3RExDrg73UbSeGA6MCH9zMXpm/865ARnZmVTS2RaOhMR9wFtZy46Ebg6Xb8aOKmofGZEbI6IhcACYHKp8zvBmVl5sjZPk/w2RNLDRcuZGa4wPCKWAqRfh6XlI4HFRcctScs65E4GMytbGb2oKyJiUqUu205ZyUhcgzOz8lWok6EDyySNAEi/Lk/LlwCji44bBbxU6kROcGZWtgp2MrTnFuD0dP104DdF5dMl9ZK0NzCO5AX1HXIT1czKV6Hn4CRdB0wluVe3BPga8F1glqQzgBeAUwAiYp6kWcB8oBk4JyIKpc7vBGdm5angW7Ui4rQOdrX70quImAHMyHp+JzgzK4tn9DWzfIvGyHBOcGZWNtfgjFH7buLCnz7/+vaeY7ZwzX/vyU2XD61hVDb8F8/R9/E1FPr34PmvvvX18j3ueZk97l1ONIkNBw5gxclj6D97BQPvfPn1Y3q9+BovXDCBzaP71iL0+tBAg+2rluAkXQn8E7A8Ig6s1nXq2ZK/9+aT7z4AgG7dgmvnzOcvvx9Q46hs7ZQhrJk6nD2veu71sj5Pr6XvY2t4/isHEj260bR2KwDrJg9h3eQhAPR88TX2uuTZrp3cUo0yH1w1n4O7iu0H0XZZE49Yz9Lne7L8xZ61DqXL2zhudwp9t/1/+x73LWf1cSOIHsmfRGH3Htt9rv9DK1l36KBdEmO9U0u2pdaqVoOLiPskja3W+RvN1BNXc+/NAzs/0Gqix/JN9FmwjsG3LCF6dOOVk0ezeWy/bY7p/8gqXjp7XI0irCNBw3Qy1Hwkg6QzWwfibmVzrcOpiu49Wjjs2LXc91s3T+uVCkG31wos/uJ4Vpw8mr0uX7DNH3HvheuJnt3YMnK3GkZZP6o8kqFiap7gIuLSiJgUEZN60KvW4VTFoUetY8HjfVizYvtmj9WH5oE9WX/wQJDYNLYfIdG0vvn1/f0fXsm6SYNrGGGdqe5Y1IqpeYLrCqaetMbN0zq3/qCB7Pb0WgB6LNuICkGhX3oHpyXoN2cV6yb5/htUfMLLqvJjIlXWq08Lhxyxjv/94qhah2KpPa9YwG7PrKNpfTN7X/A3Vv7TKF595xD2vGYhb/rm40R38fJH9gEls/P0WbCO5j16snVo7xpHXici22SW9aCaj4lsN4g2Iq6o1vXq1eaN3TjlwC75lEzdevmM/dov/+i+7ZZv3H93Fn9pQjVDajyNkd+q2ova0SBaM2tw9dD8zMJNVDMrTwBdvYlqZjnWGPnNCc7MyucmqpnlVpfvRTWznKqTh3izcIIzs7IkD/o2RoZzgjOz8tXBTCFZOMGZWdlcgzOzfPI9ODPLL49FNbM8cxPVzHKpgi9+rjYnODMrn2twZpZbjZHfnODMrHxqqUwbVdIiYB1QAJojYpKkQcCvgbHAIuADEbF6R87vKcvNrDxB8qBvliWbd0XExIiYlG6fD9wdEeOAu9PtHeIEZ2ZlEYEi27KDTgSuTtevBk7a0RM5wZlZ+SKyLRnOBPxB0iOSzkzLhkfE0uQysRQYtqNh+h6cmZUve+1siKSHi7YvjYhLi7YPj4iXJA0D7pT0VMVixAnOzMrVeg8umxVF99a2P1XES+nX5ZJuAiYDyySNiIilkkYAy3c0VDdRzaxsamnJtJQ8h9RXUv/WdeBY4AngFuD09LDTgd/saJyuwZlZmTLfX+vMcOAmJe+f7Q78KiJul/QQMEvSGcALwCk7egEnODMrT1CRBBcRzwEHtVO+Ejh6py+AE5yZ7QiPRTWzvPKEl2aWX05wZpZLEVBojDaqE5yZlc81ODPLLSc4M8ulAPxOBjPLp4DwPTgzy6PAnQxmlmO+B2dmueUEZ2b5VLHB9lXnBGdm5QmgQi+dqTYnODMrn2twZpZPHqplZnkVEH4OzsxyyyMZzCy3fA/OzHIpwr2oZpZjrsGZWT4FUSjUOohMnODMrDyeLsnMcs2PiZhZHgUQrsGZWS6FJ7w0sxxrlE4GRR1190p6BXi+1nFUwRBgRa2DsLLk9Xf2pogYujMnkHQ7yc8nixURMW1nrrcz6irB5ZWkhyNiUq3jsOz8O8uHbrUOwMysWpzgzCy3nOB2jUtrHYCVzb+zHPA9ODPLLdfgzCy3nODMLLec4KpI0jRJT0taIOn8WsdjnZN0paTlkp6odSy285zgqkRSE3ARcDwwHjhN0vjaRmUZXAXU7MFUqywnuOqZDCyIiOciYgswEzixxjFZJyLiPmBVreOwynCCq56RwOKi7SVpmZntIk5w1aN2yvxMjtku5ARXPUuA0UXbo4CXahSLWZfkBFc9DwHjJO0tqScwHbilxjGZdSlOcFUSEc3AucAdwJPArIiYV9uorDOSrgMeAA6QtETSGbWOyXach2qZWW65BmdmueUEZ2a55QRnZrnlBGdmueUEZ2a55QTXQCQVJD0q6QlJ10vabSfOdZWkf0nXLy81EYCkqZLeuQPXWCRpu7cvdVTe5pj1ZV7r65I+X26Mlm9OcI1lY0RMjIgDgS3A2cU70xlMyhYRH4+I+SUOmQqUneDMas0JrnH9GdgvrV3dI+lXwOOSmiT9t6SHJM2VdBaAEj+RNF/SrcCw1hNJulfSpHR9mqQ5kh6TdLeksSSJ9D/S2uMRkoZKuiG9xkOSDk8/O1jSHyT9TdLPaH887jYk3SzpEUnzJJ3ZZt/301juljQ0LdtX0u3pZ/4s6c0V+WlaLvnN9g1IUneSeeZuT4smAwdGxMI0SbwaEYdK6gX8RdIfgIOBA4C3AsOB+cCVbc47FLgMODI916CIWCXpp8D6iPheetyvgB9ExP2SxpCM1ngL8DXg/oj4pqT3ANskrA58LL1GH+AhSTdExEqgLzAnIs6T9NX03OeSvAzm7Ih4VtI7gIuBo3bgx2hdgBNcY+kj6dF0/c/AFSRNx9kRsTAtPxZ4W+v9NWAAMA44ErguIgrAS5L+2M75DwPuaz1XRHQ0L9oxwHjp9Qra7pL6p9c4Of3srZJWZ/iePi3pfen66DTWlUAL8Ou0/JfAjZL6pd/v9UXX7pXhGtZFOcE1lo0RMbG4IP1D31BcBHwqIu5oc9wJdD5dkzIcA8mtjSkRsbGdWDKP/ZM0lSRZTomI1yTdC/Tu4PBIr7um7c/ArCO+B5c/dwCfkNQDQNL+kvoC9wHT03t0I4B3tfPZB4B/lLR3+tlBafk6oH/RcX8gaS6SHjcxXb0P+FBadjwwsJNYBwCr0+T2ZpIaZKtuQGst9IMkTd+1wEJJp6TXkKSDOrmGdWFOcPlzOcn9tTnpi1N+RlJTvwl4FngcuAT4U9sPRsQrJPfNbpT0GG80EX8LvK+1kwH4NDAp7cSYzxu9ud8AjpQ0h6Sp/EInsd4OdJc0F/gW8NeifRuACZIeIbnH9s20/EPAGWl88/A08FaCZxMxs9xyDc7McssJzsxyywnOzHLLCc7McssJzsxyywnOzHLLCc7Mcuv/ANY2CxqTDt89AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.967280163599182\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977636</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.974522</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.948864</td>\n",
       "      <td>0.959770</td>\n",
       "      <td>0.954286</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.977636  0.971429  0.974522    315.0\n",
       "1   0.948864  0.959770  0.954286    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9644040036396724\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9666666666666667\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.965753</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.975779</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.925373</td>\n",
       "      <td>0.946565</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.965753  0.986014  0.975779    143.0\n",
       "1   0.968750  0.925373  0.946565     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.961171716104493\n",
      "\n",
      "When k =  9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.967280163599182\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.980707</td>\n",
       "      <td>0.968254</td>\n",
       "      <td>0.974441</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.943820</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.980707  0.968254  0.974441    315.0\n",
       "1   0.943820  0.965517  0.954545    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9644931745570724\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9761904761904762\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.979167</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.982578</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.955224</td>\n",
       "      <td>0.962406</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.979167  0.986014  0.982578    143.0\n",
       "1   0.969697  0.955224  0.962406     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9724922061250687\n",
      "\n",
      "When k =  10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.967280163599182\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977636</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.974522</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.948864</td>\n",
       "      <td>0.959770</td>\n",
       "      <td>0.954286</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.977636  0.971429  0.974522    315.0\n",
       "1   0.948864  0.959770  0.954286    174.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9644040036396724\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.9714285714285714\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.972414</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.979167</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.969231</td>\n",
       "      <td>0.940299</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall  f1-score  support\n",
       "0   0.972414  0.986014  0.979167    143.0\n",
       "1   0.969231  0.940299  0.954545     67.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average f1-score = 0.9668560606060607\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Ks = 11\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    y_testpre = neigh.predict(X_train)\n",
    "    yhat = neigh.predict(X_test)\n",
    "    a = pd.DataFrame(precision_recall_fscore_support(y_train,y_testpre)).transpose()\n",
    "    a.rename(columns={0:'precision',1:'recall',2:'f1-score',3:'support'}, inplace= True)\n",
    "    avg_f1a = a['f1-score'].sum() / 2\n",
    "    b = pd.DataFrame(precision_recall_fscore_support(y_test,yhat)).transpose()\n",
    "    b.rename(columns={0:'precision',1:'recall',2:'f1-score',3:'support'}, inplace = True)\n",
    "    avg_f1b = b['f1-score'].sum() / 2\n",
    "    print(\"When k = \",n)\n",
    "    matrix1 = plot_confusion_matrix(neigh, X_train,y_train)\n",
    "    matrix1.ax_.set_title(\"Train confusion matrix\")\n",
    "    plt.show()\n",
    "    print(\"Train accuracy: \", metrics.accuracy_score(y_train, y_testpre))\n",
    "    display(a)\n",
    "    print(\"Average f1-score =\",avg_f1a)\n",
    "    matrix2 = plot_confusion_matrix(neigh, X_test, y_test)\n",
    "    matrix2.ax_.set_title('Test confusion matrix')\n",
    "    plt.show()\n",
    "    print(\"Test accuracy: \", metrics.accuracy_score(y_test, yhat))\n",
    "    display(b)\n",
    "    print(\"Average f1-score =\",avg_f1b)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จะเห็นได้ว่า เมื่อค่า k  มีค่าน้อย จะทำให้เกิด overfitting ซึ่งปัญหานี้จะค่อยๆ ลดลงเมื่อเพิ่มค่า k ให้มากขึ้น\n",
    "จากการลองปรับค่า k พบว่า model ทำงานได้ดีที่สุดเมื่อ k = 3 เพราะมีค่า train/test accuracy ดีมากกว่าการทำ k-nearest classification ที่มีค่า k มากขึ้น 2-3 ค่า นอกจากนี้ ค่า average-f1-score ยังมีค่าที่ดีกว่าการทำ k-nearest classification เมื่อ k มีค่า 4-8 อีกด้วย"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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