Assignment 13-Glass Data-KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3feb9580",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the required libraries for the dataset\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a0f7d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "glass=pd.read_csv(\"glass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "452b84a5",
   "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>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1.51623</td>\n",
       "      <td>14.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.88</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.08</td>\n",
       "      <td>9.18</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1.51685</td>\n",
       "      <td>14.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.99</td>\n",
       "      <td>73.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1.52065</td>\n",
       "      <td>14.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.02</td>\n",
       "      <td>73.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.44</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>1.51651</td>\n",
       "      <td>14.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.94</td>\n",
       "      <td>73.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.48</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1.51711</td>\n",
       "      <td>14.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>73.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.62</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          RI     Na    Mg    Al     Si     K    Ca    Ba   Fe  Type\n",
       "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0     1\n",
       "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0     1\n",
       "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0     1\n",
       "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0     1\n",
       "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0     1\n",
       "..       ...    ...   ...   ...    ...   ...   ...   ...  ...   ...\n",
       "209  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0     7\n",
       "210  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0     7\n",
       "211  1.52065  14.36  0.00  2.02  73.42  0.00  8.44  1.64  0.0     7\n",
       "212  1.51651  14.38  0.00  1.94  73.61  0.00  8.48  1.57  0.0     7\n",
       "213  1.51711  14.23  0.00  2.08  73.36  0.00  8.62  1.67  0.0     7\n",
       "\n",
       "[214 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2eb55b50",
   "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>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type\n",
       "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1\n",
       "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1\n",
       "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1\n",
       "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1\n",
       "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51994ee6",
   "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>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1.51623</td>\n",
       "      <td>14.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.88</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.08</td>\n",
       "      <td>9.18</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1.51685</td>\n",
       "      <td>14.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>73.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1.52065</td>\n",
       "      <td>14.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.02</td>\n",
       "      <td>73.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.44</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>1.51651</td>\n",
       "      <td>14.38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.94</td>\n",
       "      <td>73.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.48</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1.51711</td>\n",
       "      <td>14.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>73.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.62</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RI     Na   Mg    Al     Si     K    Ca    Ba   Fe  Type\n",
       "209  1.51623  14.14  0.0  2.88  72.61  0.08  9.18  1.06  0.0     7\n",
       "210  1.51685  14.92  0.0  1.99  73.06  0.00  8.40  1.59  0.0     7\n",
       "211  1.52065  14.36  0.0  2.02  73.42  0.00  8.44  1.64  0.0     7\n",
       "212  1.51651  14.38  0.0  1.94  73.61  0.00  8.48  1.57  0.0     7\n",
       "213  1.51711  14.23  0.0  2.08  73.36  0.00  8.62  1.67  0.0     7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0f72e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   RI      214 non-null    float64\n",
      " 1   Na      214 non-null    float64\n",
      " 2   Mg      214 non-null    float64\n",
      " 3   Al      214 non-null    float64\n",
      " 4   Si      214 non-null    float64\n",
      " 5   K       214 non-null    float64\n",
      " 6   Ca      214 non-null    float64\n",
      " 7   Ba      214 non-null    float64\n",
      " 8   Fe      214 non-null    float64\n",
      " 9   Type    214 non-null    int64  \n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 16.8 KB\n"
     ]
    }
   ],
   "source": [
    "glass.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ec63fcf",
   "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>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.518365</td>\n",
       "      <td>13.407850</td>\n",
       "      <td>2.684533</td>\n",
       "      <td>1.444907</td>\n",
       "      <td>72.650935</td>\n",
       "      <td>0.497056</td>\n",
       "      <td>8.956963</td>\n",
       "      <td>0.175047</td>\n",
       "      <td>0.057009</td>\n",
       "      <td>2.780374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.003037</td>\n",
       "      <td>0.816604</td>\n",
       "      <td>1.442408</td>\n",
       "      <td>0.499270</td>\n",
       "      <td>0.774546</td>\n",
       "      <td>0.652192</td>\n",
       "      <td>1.423153</td>\n",
       "      <td>0.497219</td>\n",
       "      <td>0.097439</td>\n",
       "      <td>2.103739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.511150</td>\n",
       "      <td>10.730000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>69.810000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.516522</td>\n",
       "      <td>12.907500</td>\n",
       "      <td>2.115000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>72.280000</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>8.240000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.517680</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>72.790000</td>\n",
       "      <td>0.555000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.519157</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>73.087500</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>9.172500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.533930</td>\n",
       "      <td>17.380000</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>75.410000</td>\n",
       "      <td>6.210000</td>\n",
       "      <td>16.190000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RI          Na          Mg          Al          Si           K  \\\n",
       "count  214.000000  214.000000  214.000000  214.000000  214.000000  214.000000   \n",
       "mean     1.518365   13.407850    2.684533    1.444907   72.650935    0.497056   \n",
       "std      0.003037    0.816604    1.442408    0.499270    0.774546    0.652192   \n",
       "min      1.511150   10.730000    0.000000    0.290000   69.810000    0.000000   \n",
       "25%      1.516522   12.907500    2.115000    1.190000   72.280000    0.122500   \n",
       "50%      1.517680   13.300000    3.480000    1.360000   72.790000    0.555000   \n",
       "75%      1.519157   13.825000    3.600000    1.630000   73.087500    0.610000   \n",
       "max      1.533930   17.380000    4.490000    3.500000   75.410000    6.210000   \n",
       "\n",
       "               Ca          Ba          Fe        Type  \n",
       "count  214.000000  214.000000  214.000000  214.000000  \n",
       "mean     8.956963    0.175047    0.057009    2.780374  \n",
       "std      1.423153    0.497219    0.097439    2.103739  \n",
       "min      5.430000    0.000000    0.000000    1.000000  \n",
       "25%      8.240000    0.000000    0.000000    1.000000  \n",
       "50%      8.600000    0.000000    0.000000    2.000000  \n",
       "75%      9.172500    0.000000    0.100000    3.000000  \n",
       "max     16.190000    3.150000    0.510000    7.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "490ea35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2140"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "486638d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RI      0\n",
       "Na      0\n",
       "Mg      0\n",
       "Al      0\n",
       "Si      0\n",
       "K       0\n",
       "Ca      0\n",
       "Ba      0\n",
       "Fe      0\n",
       "Type    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glass.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c0beeca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    76\n",
      "1    70\n",
      "7    29\n",
      "3    17\n",
      "5    13\n",
      "6     9\n",
      "Name: Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "glass[\"Type\"].value_counts(sort=True).plot.bar()\n",
    "plt.show()\n",
    "print(glass[\"Type\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd210aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "array=glass.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19b3934f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.52101, 13.64   ,  4.49   , ...,  0.     ,  0.     ,  1.     ],\n",
       "       [ 1.51761, 13.89   ,  3.6    , ...,  0.     ,  0.     ,  1.     ],\n",
       "       [ 1.51618, 13.53   ,  3.55   , ...,  0.     ,  0.     ,  1.     ],\n",
       "       ...,\n",
       "       [ 1.52065, 14.36   ,  0.     , ...,  1.64   ,  0.     ,  7.     ],\n",
       "       [ 1.51651, 14.38   ,  0.     , ...,  1.57   ,  0.     ,  7.     ],\n",
       "       [ 1.51711, 14.23   ,  0.     , ...,  1.67   ,  0.     ,  7.     ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0ac9a294",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=glass.iloc[:,:9]\n",
    "y=glass.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc524208",
   "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>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1.51623</td>\n",
       "      <td>14.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.88</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.08</td>\n",
       "      <td>9.18</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1.51685</td>\n",
       "      <td>14.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.99</td>\n",
       "      <td>73.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1.52065</td>\n",
       "      <td>14.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.02</td>\n",
       "      <td>73.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.44</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>1.51651</td>\n",
       "      <td>14.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.94</td>\n",
       "      <td>73.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.48</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1.51711</td>\n",
       "      <td>14.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>73.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.62</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          RI     Na    Mg    Al     Si     K    Ca    Ba   Fe\n",
       "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0\n",
       "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0\n",
       "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0\n",
       "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0\n",
       "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0\n",
       "..       ...    ...   ...   ...    ...   ...   ...   ...  ...\n",
       "209  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0\n",
       "210  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0\n",
       "211  1.52065  14.36  0.00  2.02  73.42  0.00  8.44  1.64  0.0\n",
       "212  1.51651  14.38  0.00  1.94  73.61  0.00  8.48  1.57  0.0\n",
       "213  1.51711  14.23  0.00  2.08  73.36  0.00  8.62  1.67  0.0\n",
       "\n",
       "[214 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "958a96d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "209    7\n",
       "210    7\n",
       "211    7\n",
       "212    7\n",
       "213    7\n",
       "Name: Type, Length: 214, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114a604f",
   "metadata": {},
   "source": [
    "#  Grid Search for algorithm tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3886b22e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grid search for algorithm tuning\n",
    "\n",
    "num_folds=10\n",
    "kfold=KFold(n_splits=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4fb475ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neighbors=np.array(range(1,50))\n",
    "param_grid=dict(n_neighbors=n_neighbors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b7130f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=KNeighborsClassifier()\n",
    "grid=GridSearchCV(estimator=model,param_grid=param_grid)\n",
    "grid.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae31fcf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.640531561461794\n",
      "{'n_neighbors': 1}\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c2bbf0",
   "metadata": {},
   "source": [
    "#  Grid Search for Train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "286a8c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9fbcbdff",
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test=train_test_split(glass,test_size=0.2,random_state=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d3b80f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3aaa1ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neighbors=np.array(range(1,40))\n",
    "param_grid=dict(n_neighbors=n_neighbors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de818607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1=KNeighborsClassifier()\n",
    "grid1=GridSearchCV(estimator=model1,param_grid=param_grid)\n",
    "grid1.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fa351b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7186206896551723\n",
      "{'n_neighbors': 1}\n"
     ]
    }
   ],
   "source": [
    "print(grid1.best_score_)\n",
    "print(grid1.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e8ba8fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6443678160919539\n"
     ]
    }
   ],
   "source": [
    "KNN_results1 = cross_val_score(grid1, xtrain, ytrain, cv=kfold)\n",
    "print(KNN_results1.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23919d81",
   "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>col_0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0   1   2  3   5  6   7\n",
       "Type                       \n",
       "1      51   0  0   0  0   0\n",
       "2       0  51  0   0  0   0\n",
       "3       0   0  9   0  0   0\n",
       "5       0   0  0  10  0   0\n",
       "6       0   0  0   0  6   0\n",
       "7       0   0  0   0  0  22"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_preds1=grid1.predict(xtrain)\n",
    "pd.crosstab(ytrain,KNN_preds1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcf82e2",
   "metadata": {},
   "source": [
    "#  Grid Search on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce39d9de",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neighbors=np.array(range(1,40))\n",
    "param_grid=dict(n_neighbors=n_neighbors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "588e72fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=KNeighborsClassifier()\n",
    "grid2=GridSearchCV(estimator=model2,param_grid=param_grid)\n",
    "grid2.fit(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fdb46e64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6615384615384615\n",
      "{'n_neighbors': 1}\n"
     ]
    }
   ],
   "source": [
    "print(grid2.best_score_)\n",
    "print(grid2.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbcc8577",
   "metadata": {},
   "source": [
    "#  Visualising the CV results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7ebdab5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Choosing the K between  1 to 50\n",
    "\n",
    "k_range=range(1,50)\n",
    "k_scores=[]\n",
    "\n",
    "# use iteration to caclulator different k in models, then return the average accuracy based on the cross validation\n",
    "\n",
    "for k in k_range:\n",
    "    knn=KNeighborsClassifier(n_neighbors=k)\n",
    "    scores=cross_val_score(knn,x,y,cv=4)\n",
    "    k_scores.append(scores.mean())\n",
    "    \n",
    "#Visualising the plot\n",
    "\n",
    "plt.plot(k_range,k_scores)\n",
    "plt.xlabel(\"Value of K for KNN\")\n",
    "plt.ylabel(\"Cross-Validated Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e6df414",
   "metadata": {},
   "source": [
    "#  Plotting the Testing and Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "586a76f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(171, 10)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "05aca00f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43, 10)\n"
     ]
    }
   ],
   "source": [
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "476a9c02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as KNC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4cc638c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39])})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=KNeighborsClassifier()\n",
    "grid2=GridSearchCV(estimator=model2,param_grid=param_grid)\n",
    "grid2.fit(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1ddb73ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5846153846153846\n"
     ]
    }
   ],
   "source": [
    "KNN_results2=cross_val_score(grid1,xtest,ytest,cv=kfold)\n",
    "print(KNN_results2.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1d42740a",
   "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>col_0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0   1   2  3  5  6  7\n",
       "Type                     \n",
       "1      14   2  3  0  0  0\n",
       "2      10  14  0  1  0  0\n",
       "3       5   0  3  0  0  0\n",
       "5       0   0  0  3  0  0\n",
       "6       0   1  0  0  2  0\n",
       "7       0   1  0  0  1  5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_preds2=grid1.predict(xtest)\n",
    "pd.crosstab(ytest,KNN_preds2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "be60cba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.8245614035087719, 0.7674418604651163], [0.7192982456140351, 0.6976744186046512], [0.7134502923976608, 0.6046511627906976], [0.695906432748538, 0.627906976744186], [0.672514619883041, 0.6511627906976745], [0.6549707602339181, 0.627906976744186], [0.6608187134502924, 0.627906976744186], [0.6432748538011696, 0.6511627906976745], [0.6374269005847953, 0.6046511627906976], [0.6491228070175439, 0.5581395348837209], [0.6432748538011696, 0.6046511627906976], [0.6432748538011696, 0.5581395348837209], [0.6374269005847953, 0.5581395348837209], [0.6140350877192983, 0.5348837209302325], [0.631578947368421, 0.5116279069767442], [0.6198830409356725, 0.5116279069767442], [0.6257309941520468, 0.5116279069767442], [0.6198830409356725, 0.5116279069767442], [0.6081871345029239, 0.4883720930232558], [0.6023391812865497, 0.46511627906976744], [0.6023391812865497, 0.4418604651162791], [0.5906432748538012, 0.46511627906976744], [0.5789473684210527, 0.46511627906976744], [0.5847953216374269, 0.46511627906976744]]\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "accuracy=[]\n",
    "for i in range(3,50,2):\n",
    "    neighbour = KNC(n_neighbors=i)\n",
    "    neighbour.fit(train.iloc[:,:9],train.iloc[:,-1])\n",
    "    train_acc= np.mean(neighbour.predict(train.iloc[:,:9])==train.iloc[:,-1])\n",
    "    test_acc= np.mean(neighbour.predict(test.iloc[:,:9])==test.iloc[:,-1])\n",
    "    accuracy.append([train_acc,test_acc])\n",
    "print(accuracy)\n",
    "print(len(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "acc9cfc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(3,50,2),[i[0] for i in accuracy],\"bo-\")\n",
    "plt.plot(np.arange(3,50,2),[i[1] for i in accuracy],\"ro-\")\n",
    "plt.legend([\"train\",\"test\"])\n",
    "plt.xticks(np.arange(3,60,2))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "194bb42a",
   "metadata": {},
   "source": [
    "#  Selecting the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "403eb5d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model for k=3\n",
    "\n",
    "neighbour=KNC(n_neighbors=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ae49a6a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=3 :  0.8245614035087719 0.7674418604651163\n"
     ]
    }
   ],
   "source": [
    "neighbour.fit(train.iloc[:,:9],train.iloc[:,-1])\n",
    "train_acc= np.mean(neighbour.predict(train.iloc[:,:9])==train.iloc[:,-1])\n",
    "test_acc= np.mean(neighbour.predict(test.iloc[:,:9])==test.iloc[:,-1])\n",
    "print(\"K=3 : \",train_acc,test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d1b54e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model for k=4\n",
    "\n",
    "neighbour=KNC(n_neighbors=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "23f3524a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=4 :  0.7660818713450293 0.6744186046511628\n"
     ]
    }
   ],
   "source": [
    "neighbour.fit(train.iloc[:,:9],train.iloc[:,-1])\n",
    "train_acc= np.mean(neighbour.predict(train.iloc[:,:9])==train.iloc[:,-1])\n",
    "test_acc= np.mean(neighbour.predict(test.iloc[:,:9])==test.iloc[:,-1])\n",
    "print(\"K=4 : \",train_acc,test_acc)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fc47949a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model for K=8\n",
    "neighbour=KNC(n_neighbors=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ea629ac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=8 :  0.695906432748538 0.627906976744186\n"
     ]
    }
   ],
   "source": [
    "neighbour.fit(train.iloc[:,:9],train.iloc[:,-1])\n",
    "train_acc= np.mean(neighbour.predict(train.iloc[:,:9])==train.iloc[:,-1])\n",
    "test_acc= np.mean(neighbour.predict(test.iloc[:,:9])==test.iloc[:,-1])\n",
    "print(\"K=8 : \",train_acc,test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d82b2850",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model for K=15\n",
    "neighbour=KNC(n_neighbors=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3a873030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=15 :  0.6608187134502924 0.627906976744186\n"
     ]
    }
   ],
   "source": [
    "neighbour.fit(train.iloc[:,:9],train.iloc[:,-1])\n",
    "train_acc= np.mean(neighbour.predict(train.iloc[:,:9])==train.iloc[:,-1])\n",
    "test_acc= np.mean(neighbour.predict(test.iloc[:,:9])==test.iloc[:,-1])\n",
    "print(\"K=15 : \",train_acc,test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1dbe7fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model for K=18\n",
    "neighbour=KNC(n_neighbors=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "173661b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=18 :  0.6549707602339181 0.627906976744186\n"
     ]
    }
   ],
   "source": [
    "neighbour.fit(train.iloc[:,:9],train.iloc[:,-1])\n",
    "train_acc= np.mean(neighbour.predict(train.iloc[:,:9])==train.iloc[:,-1])\n",
    "test_acc= np.mean(neighbour.predict(test.iloc[:,:9])==test.iloc[:,-1])\n",
    "print(\"K=18 : \",train_acc,test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83875073",
   "metadata": {},
   "source": [
    "#  From all the abobe results we can see that, For K = 3 the model gives best accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "352b5d07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96f388a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}