K-Means.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "data = datasets.load_wine(as_frame = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.data\n",
    "y = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#as we are doing K-Means which is usepvised learning \n",
    "#which means exploring the target values on our own is main goal of it\n",
    "# we dont use \"y\" in our algorithm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(x, columns=data.feature_names)\n",
    "df['wine_class']=data.target # creating new column ans storing target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>wine_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  wine_class  \n",
       "0                            3.92   1065.0           0  \n",
       "1                            3.40   1050.0           0  \n",
       "2                            3.17   1185.0           0  \n",
       "3                            3.45   1480.0           0  \n",
       "4                            2.93    735.0           0  \n",
       "..                            ...      ...         ...  \n",
       "173                          1.74    740.0           2  \n",
       "174                          1.56    750.0           2  \n",
       "175                          1.56    835.0           2  \n",
       "176                          1.62    840.0           2  \n",
       "177                          1.60    560.0           2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol                         0\n",
       "malic_acid                      0\n",
       "ash                             0\n",
       "alcalinity_of_ash               0\n",
       "magnesium                       0\n",
       "total_phenols                   0\n",
       "flavanoids                      0\n",
       "nonflavanoid_phenols            0\n",
       "proanthocyanins                 0\n",
       "color_intensity                 0\n",
       "hue                             0\n",
       "od280/od315_of_diluted_wines    0\n",
       "proline                         0\n",
       "wine_class                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # to check null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>wine_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "      <td>0.938202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "      <td>0.775035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
       "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
       "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
       "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
       "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
       "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
       "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
       "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
       "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
       "\n",
       "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
       "count     178.000000  178.000000            178.000000       178.000000   \n",
       "mean        2.295112    2.029270              0.361854         1.590899   \n",
       "std         0.625851    0.998859              0.124453         0.572359   \n",
       "min         0.980000    0.340000              0.130000         0.410000   \n",
       "25%         1.742500    1.205000              0.270000         1.250000   \n",
       "50%         2.355000    2.135000              0.340000         1.555000   \n",
       "75%         2.800000    2.875000              0.437500         1.950000   \n",
       "max         3.880000    5.080000              0.660000         3.580000   \n",
       "\n",
       "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
       "count       178.000000  178.000000                    178.000000   178.000000   \n",
       "mean          5.058090    0.957449                      2.611685   746.893258   \n",
       "std           2.318286    0.228572                      0.709990   314.907474   \n",
       "min           1.280000    0.480000                      1.270000   278.000000   \n",
       "25%           3.220000    0.782500                      1.937500   500.500000   \n",
       "50%           4.690000    0.965000                      2.780000   673.500000   \n",
       "75%           6.200000    1.120000                      3.170000   985.000000   \n",
       "max          13.000000    1.710000                      4.000000  1680.000000   \n",
       "\n",
       "       wine_class  \n",
       "count  178.000000  \n",
       "mean     0.938202  \n",
       "std      0.775035  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      2.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as mean is varying across different features \n",
    "#scalling can be done to bring the whole data in same range\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sc.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now move to algorith\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wss=[]\n",
    "for i in range(1,11):\n",
    "    kmeans= KMeans(n_clusters=i, init='k-means++',random_state=0)\n",
    "    kmeans.fit(x)\n",
    "    wss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WSS')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "plt.plot(range(1,11), wss)\n",
    "plt.title(\"Elbow method\")\n",
    "plt.xlabel(\"number of clusters\")\n",
    "plt.ylabel(\"WSS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 1 1 1 1 1 1 1 1 1 1 1 0\n",
      " 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 2 1 1 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "N=3 #from the graph\n",
    "k_means = KMeans(n_clusters=3,init=\"k-means++\", max_iter=400)\n",
    "k_means.fit(x)\n",
    "lables = k_means.labels_\n",
    "print(lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9662921348314607"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(lables, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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