ML CW KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# K Nearest Neighbors (KNN)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "#import necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92d19dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset\n",
    "dataset = pd.read_csv('spambase.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(4600, 58)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#before removing duplicates\n",
    "dataset.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "#data cleaning\n",
    "# Remove duplicate rows\n",
    "dataset.drop_duplicates(inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d70dd728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(4209, 58)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#after removing duplicates\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7158bff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove missing values\n",
    "dataset.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34e7c7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(4209, 58)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#after removing missing values\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e26d4b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4209 entries, 0 to 4599\n",
      "Data columns (total 58 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       4209 non-null   float64\n",
      " 1   0.64    4209 non-null   float64\n",
      " 2   0.64.1  4209 non-null   float64\n",
      " 3   0.1     4209 non-null   float64\n",
      " 4   0.32    4209 non-null   float64\n",
      " 5   0.2     4209 non-null   float64\n",
      " 6   0.3     4209 non-null   float64\n",
      " 7   0.4     4209 non-null   float64\n",
      " 8   0.5     4209 non-null   float64\n",
      " 9   0.6     4209 non-null   float64\n",
      " 10  0.7     4209 non-null   float64\n",
      " 11  0.64.2  4209 non-null   float64\n",
      " 12  0.8     4209 non-null   float64\n",
      " 13  0.9     4209 non-null   float64\n",
      " 14  0.10    4209 non-null   float64\n",
      " 15  0.32.1  4209 non-null   float64\n",
      " 16  0.11    4209 non-null   float64\n",
      " 17  1.29    4209 non-null   float64\n",
      " 18  1.93    4209 non-null   float64\n",
      " 19  0.12    4209 non-null   float64\n",
      " 20  0.96    4209 non-null   float64\n",
      " 21  0.13    4209 non-null   float64\n",
      " 22  0.14    4209 non-null   float64\n",
      " 23  0.15    4209 non-null   float64\n",
      " 24  0.16    4209 non-null   float64\n",
      " 25  0.17    4209 non-null   float64\n",
      " 26  0.18    4209 non-null   float64\n",
      " 27  0.19    4209 non-null   float64\n",
      " 28  0.20    4209 non-null   float64\n",
      " 29  0.21    4209 non-null   float64\n",
      " 30  0.22    4209 non-null   float64\n",
      " 31  0.23    4209 non-null   float64\n",
      " 32  0.24    4209 non-null   float64\n",
      " 33  0.25    4209 non-null   float64\n",
      " 34  0.26    4209 non-null   float64\n",
      " 35  0.27    4209 non-null   float64\n",
      " 36  0.28    4209 non-null   float64\n",
      " 37  0.29    4209 non-null   float64\n",
      " 38  0.30    4209 non-null   float64\n",
      " 39  0.31    4209 non-null   float64\n",
      " 40  0.33    4209 non-null   float64\n",
      " 41  0.34    4209 non-null   float64\n",
      " 42  0.35    4209 non-null   float64\n",
      " 43  0.36    4209 non-null   float64\n",
      " 44  0.37    4209 non-null   float64\n",
      " 45  0.38    4209 non-null   float64\n",
      " 46  0.39    4209 non-null   float64\n",
      " 47  0.40    4209 non-null   float64\n",
      " 48  0.41    4209 non-null   float64\n",
      " 49  0.42    4209 non-null   float64\n",
      " 50  0.43    4209 non-null   float64\n",
      " 51  0.778   4209 non-null   float64\n",
      " 52  0.44    4209 non-null   float64\n",
      " 53  0.45    4209 non-null   float64\n",
      " 54  3.756   4209 non-null   float64\n",
      " 55  61      4209 non-null   int64  \n",
      " 56  278     4209 non-null   int64  \n",
      " 57  1       4209 non-null   int64  \n",
      "dtypes: float64(55), int64(3)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "#check data types\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "884b4d5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save cleaned dataset\n",
    "dataset.to_csv('cleaned_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "acbcb185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      0  0.64  0.64.1  0.1  0.32   0.2   0.3   0.4   0.5   0.6  ...  0.41  \\\n0  0.21  0.28    0.50  0.0  0.14  0.28  0.21  0.07  0.00  0.94  ...  0.00   \n1  0.06  0.00    0.71  0.0  1.23  0.19  0.19  0.12  0.64  0.25  ...  0.01   \n2  0.00  0.00    0.00  0.0  0.63  0.00  0.31  0.63  0.31  0.63  ...  0.00   \n3  0.00  0.00    0.00  0.0  0.63  0.00  0.31  0.63  0.31  0.63  ...  0.00   \n4  0.00  0.00    0.00  0.0  1.85  0.00  0.00  1.85  0.00  0.00  ...  0.00   \n\n    0.42  0.43  0.778   0.44   0.45  3.756   61   278  1  \n0  0.132   0.0  0.372  0.180  0.048  5.114  101  1028  1  \n1  0.143   0.0  0.276  0.184  0.010  9.821  485  2259  1  \n2  0.137   0.0  0.137  0.000  0.000  3.537   40   191  1  \n3  0.135   0.0  0.135  0.000  0.000  3.537   40   191  1  \n4  0.223   0.0  0.000  0.000  0.000  3.000   15    54  1  \n\n[5 rows x 58 columns]",
      "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>0.64</th>\n      <th>0.64.1</th>\n      <th>0.1</th>\n      <th>0.32</th>\n      <th>0.2</th>\n      <th>0.3</th>\n      <th>0.4</th>\n      <th>0.5</th>\n      <th>0.6</th>\n      <th>...</th>\n      <th>0.41</th>\n      <th>0.42</th>\n      <th>0.43</th>\n      <th>0.778</th>\n      <th>0.44</th>\n      <th>0.45</th>\n      <th>3.756</th>\n      <th>61</th>\n      <th>278</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.21</td>\n      <td>0.28</td>\n      <td>0.50</td>\n      <td>0.0</td>\n      <td>0.14</td>\n      <td>0.28</td>\n      <td>0.21</td>\n      <td>0.07</td>\n      <td>0.00</td>\n      <td>0.94</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.132</td>\n      <td>0.0</td>\n      <td>0.372</td>\n      <td>0.180</td>\n      <td>0.048</td>\n      <td>5.114</td>\n      <td>101</td>\n      <td>1028</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.06</td>\n      <td>0.00</td>\n      <td>0.71</td>\n      <td>0.0</td>\n      <td>1.23</td>\n      <td>0.19</td>\n      <td>0.19</td>\n      <td>0.12</td>\n      <td>0.64</td>\n      <td>0.25</td>\n      <td>...</td>\n      <td>0.01</td>\n      <td>0.143</td>\n      <td>0.0</td>\n      <td>0.276</td>\n      <td>0.184</td>\n      <td>0.010</td>\n      <td>9.821</td>\n      <td>485</td>\n      <td>2259</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.63</td>\n      <td>0.00</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.137</td>\n      <td>0.0</td>\n      <td>0.137</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.537</td>\n      <td>40</td>\n      <td>191</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.63</td>\n      <td>0.00</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.135</td>\n      <td>0.0</td>\n      <td>0.135</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.537</td>\n      <td>40</td>\n      <td>191</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>1.85</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.85</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.223</td>\n      <td>0.0</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.000</td>\n      <td>15</td>\n      <td>54</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 58 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print first five data points \n",
    "dataset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad1bc6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "         0  0.64  0.64.1  0.1  0.32   0.2  0.3  0.4  0.5  0.6  ...   0.41  \\\n4595  0.31   0.0    0.62  0.0  0.00  0.31  0.0  0.0  0.0  0.0  ...  0.000   \n4596  0.00   0.0    0.00  0.0  0.00  0.00  0.0  0.0  0.0  0.0  ...  0.000   \n4597  0.30   0.0    0.30  0.0  0.00  0.00  0.0  0.0  0.0  0.0  ...  0.102   \n4598  0.96   0.0    0.00  0.0  0.32  0.00  0.0  0.0  0.0  0.0  ...  0.000   \n4599  0.00   0.0    0.65  0.0  0.00  0.00  0.0  0.0  0.0  0.0  ...  0.000   \n\n       0.42  0.43  0.778  0.44  0.45  3.756  61  278  1  \n4595  0.232   0.0  0.000   0.0   0.0  1.142   3   88  0  \n4596  0.000   0.0  0.353   0.0   0.0  1.555   4   14  0  \n4597  0.718   0.0  0.000   0.0   0.0  1.404   6  118  0  \n4598  0.057   0.0  0.000   0.0   0.0  1.147   5   78  0  \n4599  0.000   0.0  0.125   0.0   0.0  1.250   5   40  0  \n\n[5 rows x 58 columns]",
      "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>0.64</th>\n      <th>0.64.1</th>\n      <th>0.1</th>\n      <th>0.32</th>\n      <th>0.2</th>\n      <th>0.3</th>\n      <th>0.4</th>\n      <th>0.5</th>\n      <th>0.6</th>\n      <th>...</th>\n      <th>0.41</th>\n      <th>0.42</th>\n      <th>0.43</th>\n      <th>0.778</th>\n      <th>0.44</th>\n      <th>0.45</th>\n      <th>3.756</th>\n      <th>61</th>\n      <th>278</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4595</th>\n      <td>0.31</td>\n      <td>0.0</td>\n      <td>0.62</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.31</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.232</td>\n      <td>0.0</td>\n      <td>0.000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.142</td>\n      <td>3</td>\n      <td>88</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4596</th>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.0</td>\n      <td>0.353</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.555</td>\n      <td>4</td>\n      <td>14</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4597</th>\n      <td>0.30</td>\n      <td>0.0</td>\n      <td>0.30</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.102</td>\n      <td>0.718</td>\n      <td>0.0</td>\n      <td>0.000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.404</td>\n      <td>6</td>\n      <td>118</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4598</th>\n      <td>0.96</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.32</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.057</td>\n      <td>0.0</td>\n      <td>0.000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.147</td>\n      <td>5</td>\n      <td>78</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4599</th>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.65</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.0</td>\n      <td>0.125</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.250</td>\n      <td>5</td>\n      <td>40</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 58 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print last five data points\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdf53356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4209, 58)\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d31ba8fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                 0         0.64       0.64.1          0.1         0.32  \\\ncount  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000   \nmean      0.104391     0.112530     0.291390     0.063093     0.325322   \nstd       0.300036     0.454241     0.515752     1.352647     0.687887   \nmin       0.000000     0.000000     0.000000     0.000000     0.000000   \n25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n75%       0.000000     0.000000     0.440000     0.000000     0.410000   \nmax       4.540000    14.280000     5.100000    42.810000    10.000000   \n\n               0.2          0.3          0.4          0.5          0.6  ...  \\\ncount  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000  ...   \nmean      0.096679     0.117503     0.108026     0.091882     0.248479  ...   \nstd       0.276059     0.397327     0.410328     0.282174     0.656705  ...   \nmin       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n75%       0.000000     0.000000     0.000000     0.000000     0.190000  ...   \nmax       5.880000     7.270000    11.110000     5.260000    18.180000  ...   \n\n              0.41         0.42         0.43        0.778         0.44  \\\ncount  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000   \nmean      0.040413     0.144082     0.017380     0.281018     0.076075   \nstd       0.252562     0.274280     0.105743     0.843387     0.239734   \nmin       0.000000     0.000000     0.000000     0.000000     0.000000   \n25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n50%       0.000000     0.073000     0.000000     0.016000     0.000000   \n75%       0.000000     0.194000     0.000000     0.331000     0.053000   \nmax       4.385000     9.752000     4.081000    32.478000     6.003000   \n\n              0.45        3.756           61          278            1  \ncount  4209.000000  4209.000000  4209.000000   4209.00000  4209.000000  \nmean      0.045809     5.384282    52.137800    291.18508     0.398670  \nstd       0.435976    33.151287   199.605834    618.72831     0.489683  \nmin       0.000000     1.000000     1.000000      1.00000     0.000000  \n25%       0.000000     1.627000     7.000000     40.00000     0.000000  \n50%       0.000000     2.297000    15.000000    101.00000     0.000000  \n75%       0.000000     3.706000    44.000000    273.00000     1.000000  \nmax      19.829000  1102.500000  9989.000000  15841.00000     1.000000  \n\n[8 rows x 58 columns]",
      "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>0.64</th>\n      <th>0.64.1</th>\n      <th>0.1</th>\n      <th>0.32</th>\n      <th>0.2</th>\n      <th>0.3</th>\n      <th>0.4</th>\n      <th>0.5</th>\n      <th>0.6</th>\n      <th>...</th>\n      <th>0.41</th>\n      <th>0.42</th>\n      <th>0.43</th>\n      <th>0.778</th>\n      <th>0.44</th>\n      <th>0.45</th>\n      <th>3.756</th>\n      <th>61</th>\n      <th>278</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>...</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.000000</td>\n      <td>4209.00000</td>\n      <td>4209.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.104391</td>\n      <td>0.112530</td>\n      <td>0.291390</td>\n      <td>0.063093</td>\n      <td>0.325322</td>\n      <td>0.096679</td>\n      <td>0.117503</td>\n      <td>0.108026</td>\n      <td>0.091882</td>\n      <td>0.248479</td>\n      <td>...</td>\n      <td>0.040413</td>\n      <td>0.144082</td>\n      <td>0.017380</td>\n      <td>0.281018</td>\n      <td>0.076075</td>\n      <td>0.045809</td>\n      <td>5.384282</td>\n      <td>52.137800</td>\n      <td>291.18508</td>\n      <td>0.398670</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.300036</td>\n      <td>0.454241</td>\n      <td>0.515752</td>\n      <td>1.352647</td>\n      <td>0.687887</td>\n      <td>0.276059</td>\n      <td>0.397327</td>\n      <td>0.410328</td>\n      <td>0.282174</td>\n      <td>0.656705</td>\n      <td>...</td>\n      <td>0.252562</td>\n      <td>0.274280</td>\n      <td>0.105743</td>\n      <td>0.843387</td>\n      <td>0.239734</td>\n      <td>0.435976</td>\n      <td>33.151287</td>\n      <td>199.605834</td>\n      <td>618.72831</td>\n      <td>0.489683</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.627000</td>\n      <td>7.000000</td>\n      <td>40.00000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.073000</td>\n      <td>0.000000</td>\n      <td>0.016000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.297000</td>\n      <td>15.000000</td>\n      <td>101.00000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.440000</td>\n      <td>0.000000</td>\n      <td>0.410000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.190000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.194000</td>\n      <td>0.000000</td>\n      <td>0.331000</td>\n      <td>0.053000</td>\n      <td>0.000000</td>\n      <td>3.706000</td>\n      <td>44.000000</td>\n      <td>273.00000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>4.540000</td>\n      <td>14.280000</td>\n      <td>5.100000</td>\n      <td>42.810000</td>\n      <td>10.000000</td>\n      <td>5.880000</td>\n      <td>7.270000</td>\n      <td>11.110000</td>\n      <td>5.260000</td>\n      <td>18.180000</td>\n      <td>...</td>\n      <td>4.385000</td>\n      <td>9.752000</td>\n      <td>4.081000</td>\n      <td>32.478000</td>\n      <td>6.003000</td>\n      <td>19.829000</td>\n      <td>1102.500000</td>\n      <td>9989.000000</td>\n      <td>15841.00000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 58 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataset['57'].value_count()\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92728ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      0  0.64  0.64.1  0.1  0.32   0.2   0.3   0.4   0.5   0.6  ...  0.40  \\\n0  0.21  0.28    0.50  0.0  0.14  0.28  0.21  0.07  0.00  0.94  ...   0.0   \n1  0.06  0.00    0.71  0.0  1.23  0.19  0.19  0.12  0.64  0.25  ...   0.0   \n2  0.00  0.00    0.00  0.0  0.63  0.00  0.31  0.63  0.31  0.63  ...   0.0   \n3  0.00  0.00    0.00  0.0  0.63  0.00  0.31  0.63  0.31  0.63  ...   0.0   \n4  0.00  0.00    0.00  0.0  1.85  0.00  0.00  1.85  0.00  0.00  ...   0.0   \n\n   0.41   0.42  0.43  0.778   0.44   0.45  3.756   61   278  \n0  0.00  0.132   0.0  0.372  0.180  0.048  5.114  101  1028  \n1  0.01  0.143   0.0  0.276  0.184  0.010  9.821  485  2259  \n2  0.00  0.137   0.0  0.137  0.000  0.000  3.537   40   191  \n3  0.00  0.135   0.0  0.135  0.000  0.000  3.537   40   191  \n4  0.00  0.223   0.0  0.000  0.000  0.000  3.000   15    54  \n\n[5 rows x 57 columns]",
      "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>0.64</th>\n      <th>0.64.1</th>\n      <th>0.1</th>\n      <th>0.32</th>\n      <th>0.2</th>\n      <th>0.3</th>\n      <th>0.4</th>\n      <th>0.5</th>\n      <th>0.6</th>\n      <th>...</th>\n      <th>0.40</th>\n      <th>0.41</th>\n      <th>0.42</th>\n      <th>0.43</th>\n      <th>0.778</th>\n      <th>0.44</th>\n      <th>0.45</th>\n      <th>3.756</th>\n      <th>61</th>\n      <th>278</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.21</td>\n      <td>0.28</td>\n      <td>0.50</td>\n      <td>0.0</td>\n      <td>0.14</td>\n      <td>0.28</td>\n      <td>0.21</td>\n      <td>0.07</td>\n      <td>0.00</td>\n      <td>0.94</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.132</td>\n      <td>0.0</td>\n      <td>0.372</td>\n      <td>0.180</td>\n      <td>0.048</td>\n      <td>5.114</td>\n      <td>101</td>\n      <td>1028</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.06</td>\n      <td>0.00</td>\n      <td>0.71</td>\n      <td>0.0</td>\n      <td>1.23</td>\n      <td>0.19</td>\n      <td>0.19</td>\n      <td>0.12</td>\n      <td>0.64</td>\n      <td>0.25</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.01</td>\n      <td>0.143</td>\n      <td>0.0</td>\n      <td>0.276</td>\n      <td>0.184</td>\n      <td>0.010</td>\n      <td>9.821</td>\n      <td>485</td>\n      <td>2259</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.63</td>\n      <td>0.00</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.137</td>\n      <td>0.0</td>\n      <td>0.137</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.537</td>\n      <td>40</td>\n      <td>191</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.63</td>\n      <td>0.00</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>0.31</td>\n      <td>0.63</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.135</td>\n      <td>0.0</td>\n      <td>0.135</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.537</td>\n      <td>40</td>\n      <td>191</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>1.85</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>1.85</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.223</td>\n      <td>0.0</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.000</td>\n      <td>15</td>\n      <td>54</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 57 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#separate index location for X (:-all rows)\n",
    "x=dataset.iloc[:,0:57]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0fb197fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0    1\n1    1\n2    1\n3    1\n4    1\nName: 1, dtype: int64"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#separate index location for Y (:-all rows)\n",
    "y=dataset.iloc[:,-1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5de3bf08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ce1d8bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 3.52030682e-01,  3.68723902e-01,  4.04525775e-01,\n        -4.66499100e-02, -2.69439563e-01,  6.64145010e-01,\n         2.32826054e-01, -9.26824327e-02, -3.25659622e-01,\n         1.05314009e+00,  8.32408230e-01,  2.54742619e-01,\n         1.78568086e+00,  4.29902786e-01,  3.93032219e-01,\n        -1.42706498e-01, -1.73030774e-01,  1.69184472e-01,\n         9.90346442e-01, -1.68088139e-01,  6.78640440e-01,\n        -1.22863174e-01,  9.37440590e-01,  7.85252199e-01,\n        -3.42455326e-01, -3.08540545e-01, -2.08371511e-01,\n        -2.40192753e-01, -1.70608095e-01, -2.36150798e-01,\n        -1.64122373e-01, -1.49386289e-01, -1.79843671e-01,\n        -1.51709366e-01, -2.03497956e-01, -2.57180362e-01,\n        -1.73182266e-01, -6.15733762e-02, -1.87676280e-01,\n        -1.91827641e-01, -1.30248924e-01, -1.78351105e-01,\n        -2.10391774e-01, -1.32002786e-01, -3.06146600e-01,\n        -2.04050036e-01, -7.31030494e-02, -1.16421100e-01,\n        -1.60029911e-01, -4.40567113e-02, -1.64384139e-01,\n         1.07890277e-01,  4.33554343e-01,  5.02723981e-03,\n        -8.15397006e-03,  2.44822531e-01,  1.19099523e+00],\n       [-1.47968353e-01, -2.47761873e-01,  8.11746731e-01,\n        -4.66499100e-02,  1.31531149e+00,  3.38089060e-01,\n         1.82483669e-01,  2.91858457e-02,  1.94271590e+00,\n         2.31569896e-03,  1.75549586e+00, -1.30521133e-01,\n         7.21655584e-02, -1.76920660e-01,  7.04084298e+00,\n        -2.43015725e-01, -1.94880900e-01,  1.55584703e+00,\n        -2.02581762e-01,  4.64846952e-01, -2.61021278e-01,\n        -1.22863174e-01,  3.01524173e+00, -7.17842979e-02,\n        -3.42455326e-01, -3.08540545e-01, -2.08371511e-01,\n        -2.40192753e-01, -1.70608095e-01, -2.36150798e-01,\n        -1.64122373e-01, -1.49386289e-01, -1.79843671e-01,\n        -1.51709366e-01, -2.03497956e-01, -2.57180362e-01,\n        -3.38081246e-01, -6.15733762e-02, -1.87676280e-01,\n        -4.04505142e-03, -1.30248924e-01, -1.78351105e-01,\n         3.07139406e-01, -1.32002786e-01, -2.48779890e-01,\n        -1.39415977e-01, -7.31030494e-02, -1.16421100e-01,\n        -1.20430982e-01, -3.94695461e-03, -1.64384139e-01,\n        -5.95003134e-03,  4.50241480e-01, -8.21439213e-02,\n         1.33848337e-01,  2.16884257e+00,  3.18079631e+00],\n       [-3.47967967e-01, -2.47761873e-01, -5.65047928e-01,\n        -4.66499100e-02,  4.42971463e-01, -3.50251280e-01,\n         4.84537977e-01,  1.27224229e+00,  7.73084770e-01,\n         5.81030578e-01,  1.37540095e+00, -2.89159149e-01,\n         6.86444629e-01, -1.76920660e-01, -1.85038282e-01,\n         7.04506090e-02, -3.25981652e-01, -3.48502882e-01,\n         8.26389485e-01, -1.68088139e-01, -4.35032708e-01,\n        -1.22863174e-01, -2.86469672e-01, -2.10763189e-01,\n        -3.42455326e-01, -3.08540545e-01, -2.08371511e-01,\n        -2.40192753e-01, -1.70608095e-01, -2.36150798e-01,\n        -1.64122373e-01, -1.49386289e-01, -1.79843671e-01,\n        -1.51709366e-01, -2.03497956e-01, -2.57180362e-01,\n        -3.38081246e-01, -6.15733762e-02, -1.87676280e-01,\n        -1.91827641e-01, -1.30248924e-01, -1.78351105e-01,\n        -2.10391774e-01, -1.32002786e-01, -3.06146600e-01,\n        -2.04050036e-01, -7.31030494e-02, -1.16421100e-01,\n        -1.60029911e-01, -2.58250037e-02, -1.64384139e-01,\n        -1.70781311e-01, -3.17366830e-01, -1.05083701e-01,\n        -5.57294190e-02, -6.08160685e-02, -1.61940195e-01],\n       [-3.47967967e-01, -2.47761873e-01, -5.65047928e-01,\n        -4.66499100e-02,  4.42971463e-01, -3.50251280e-01,\n         4.84537977e-01,  1.27224229e+00,  7.73084770e-01,\n         5.81030578e-01,  1.37540095e+00, -2.89159149e-01,\n         6.86444629e-01, -1.76920660e-01, -1.85038282e-01,\n         7.04506090e-02, -3.25981652e-01, -3.48502882e-01,\n         8.26389485e-01, -1.68088139e-01, -4.35032708e-01,\n        -1.22863174e-01, -2.86469672e-01, -2.10763189e-01,\n        -3.42455326e-01, -3.08540545e-01, -2.08371511e-01,\n        -2.40192753e-01, -1.70608095e-01, -2.36150798e-01,\n        -1.64122373e-01, -1.49386289e-01, -1.79843671e-01,\n        -1.51709366e-01, -2.03497956e-01, -2.57180362e-01,\n        -3.38081246e-01, -6.15733762e-02, -1.87676280e-01,\n        -1.91827641e-01, -1.30248924e-01, -1.78351105e-01,\n        -2.10391774e-01, -1.32002786e-01, -3.06146600e-01,\n        -2.04050036e-01, -7.31030494e-02, -1.16421100e-01,\n        -1.60029911e-01, -3.31176867e-02, -1.64384139e-01,\n        -1.73152984e-01, -3.17366830e-01, -1.05083701e-01,\n        -5.57294190e-02, -6.08160685e-02, -1.61940195e-01],\n       [-3.47967967e-01, -2.47761873e-01, -5.65047928e-01,\n        -4.66499100e-02,  2.21672953e+00, -3.50251280e-01,\n        -2.95768985e-01,  4.24582828e+00, -3.25659622e-01,\n        -3.78417774e-01, -3.07876486e-01, -6.40429040e-01,\n        -3.15800170e-01, -1.76920660e-01, -1.85038282e-01,\n        -3.18247645e-01, -3.25981652e-01, -3.48502882e-01,\n        -9.71483353e-01, -1.68088139e-01, -7.04750423e-01,\n        -1.22863174e-01, -2.86469672e-01, -2.10763189e-01,\n        -3.42455326e-01, -3.08540545e-01, -2.08371511e-01,\n        -2.40192753e-01, -1.70608095e-01, -2.36150798e-01,\n        -1.64122373e-01, -1.49386289e-01, -1.79843671e-01,\n        -1.51709366e-01, -2.03497956e-01, -2.57180362e-01,\n        -3.38081246e-01, -6.15733762e-02, -1.87676280e-01,\n        -1.91827641e-01, -1.30248924e-01, -1.78351105e-01,\n        -2.10391774e-01, -1.32002786e-01, -3.06146600e-01,\n        -2.04050036e-01, -7.31030494e-02, -1.16421100e-01,\n        -1.60029911e-01,  2.87760367e-01, -1.64384139e-01,\n        -3.33240917e-01, -3.17366830e-01, -1.05083701e-01,\n        -7.19298096e-02, -1.86077790e-01, -3.83388406e-01]])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=scaler.fit_transform(x)\n",
    "x[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07d64a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split into 2 parts for training and testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5fef3191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(3367, 57)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train data\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56855325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(842, 57)"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test data\n",
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0657a79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preprocess the data(Standardize the features)\n",
    "\n",
    "scaler=StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b779af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "KNeighborsClassifier(n_neighbors=1)"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train the model using KNN algorithm\n",
    "#k=1\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier(n_neighbors=1)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee831e17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([0, 1, 1, 1, 1], dtype=int64)"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict values from model\n",
    "\n",
    "#from scipy import stats\n",
    "#mode, _ = stats.mode(_y[neigh_ind, k], axis=1, keepdims=False)\n",
    "pred = model.predict(x_test)\n",
    "pred[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "45970845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "4316    0\n95      1\n1213    1\n246     1\n52      1\nName: 1, dtype: int64"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare with dataset\n",
    "y_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb3040a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.8954869358669834"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the accuracy of the model\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test,pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a9a0afcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "#find the most suitable value for k\n",
    "\n",
    "correct_sum = []\n",
    "for i in range(1,20):\n",
    "    model = KNeighborsClassifier(n_neighbors=i)\n",
    "    model.fit(x_train,y_train)\n",
    "    pred = model.predict(x_test)\n",
    "    correct = np.sum(pred == y_test)\n",
    "    correct_sum.append(correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cac64528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[754,\n 743,\n 749,\n 745,\n 753,\n 752,\n 757,\n 749,\n 751,\n 746,\n 748,\n 742,\n 745,\n 743,\n 745,\n 742,\n 743,\n 742,\n 745]"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2e5fed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    1    2    3    4    5    6    7    8    9    10   11   12   13   14   15  \\\n0  754  743  749  745  753  752  757  749  751  746  748  742  745  743  745   \n\n    16   17   18   19  \n0  742  743  742  745  ",
      "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>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>754</td>\n      <td>743</td>\n      <td>749</td>\n      <td>745</td>\n      <td>753</td>\n      <td>752</td>\n      <td>757</td>\n      <td>749</td>\n      <td>751</td>\n      <td>746</td>\n      <td>748</td>\n      <td>742</td>\n      <td>745</td>\n      <td>743</td>\n      <td>745</td>\n      <td>742</td>\n      <td>743</td>\n      <td>742</td>\n      <td>745</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(data=correct_sum)\n",
    "result.index = result.index+1\n",
    "result.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a46399f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dilin\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=9)\n",
    "model.fit(x_train,y_train)\n",
    "pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f7e2af8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.8919239904988123"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy score after find the most suitable value for k\n",
    "accuracy_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a952ec48",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9f039d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "Precision = metrics.precision_score(y_test, pred)\n",
    "Sensitivity_recall = metrics.recall_score(y_test, pred)\n",
    "Specificity = metrics.recall_score(y_test, pred)\n",
    "F1_score = metrics.f1_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8029b315",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Accuracy': 0.8954869358669834, 'Precision': 0.8993288590604027, 'Sensitivity_recall': 0.8145896656534954, 'Specificity': 0.8145896656534954, 'F1_score': 0.8548644338118022}\n"
     ]
    }
   ],
   "source": [
    "print({\"Accuracy\":accuracy,\"Precision\":Precision,\"Sensitivity_recall\":Sensitivity_recall,\"Specificity\":Specificity,\"F1_score\":F1_score})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4b51dcee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = metrics.confusion_matrix(y_test, pred)\n",
    "\n",
    "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
    "\n",
    "cm_display.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "48d21bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'True Positive')"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve,auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# get false +ve and true + rate for different threshold vales\n",
    "fpr,tpr,thresholds = roc_curve(y_test,pred)\n",
    "#auc= area under the curve\n",
    "roc_auc=auc (fpr,tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}