Assignment-PCA Data Mining (Wine).ipynb

{
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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ed40c58",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</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</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>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0       1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1       1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2       1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3       1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4       1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..    ...      ...    ...   ...         ...        ...      ...         ...   \n",
       "173     3    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174     3    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175     3    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176     3    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177     3    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \n",
       "0             0.28             2.29   5.64  1.04      3.92     1065  \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050  \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185  \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480  \n",
       "4             0.39             1.82   4.32  1.04      2.93      735  \n",
       "..             ...              ...    ...   ...       ...      ...  \n",
       "173           0.52             1.06   7.70  0.64      1.74      740  \n",
       "174           0.43             1.41   7.30  0.70      1.56      750  \n",
       "175           0.43             1.35  10.20  0.59      1.56      835  \n",
       "176           0.53             1.46   9.30  0.60      1.62      840  \n",
       "177           0.56             1.35   9.20  0.61      1.60      560  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "wine=pd.read_csv('wine.csv')\n",
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904adc6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    71\n",
       "1    59\n",
       "3    48\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfb6bfd1",
   "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</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</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</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</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</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</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</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</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</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</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</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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</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</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</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</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</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</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</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</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</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0      14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1      13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2      13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3      14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4      13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..       ...    ...   ...         ...        ...      ...         ...   \n",
       "173    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \n",
       "0             0.28             2.29   5.64  1.04      3.92     1065  \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050  \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185  \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480  \n",
       "4             0.39             1.82   4.32  1.04      2.93      735  \n",
       "..             ...              ...    ...   ...       ...      ...  \n",
       "173           0.52             1.06   7.70  0.64      1.74      740  \n",
       "174           0.43             1.41   7.30  0.70      1.56      750  \n",
       "175           0.43             1.35  10.20  0.59      1.56      835  \n",
       "176           0.53             1.46   9.30  0.60      1.62      840  \n",
       "177           0.56             1.35   9.20  0.61      1.60      560  \n",
       "\n",
       "[178 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine2=wine.iloc[:,1:]\n",
    "wine2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8017edae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f324c23e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Alcohol          178 non-null    float64\n",
      " 1   Malic            178 non-null    float64\n",
      " 2   Ash              178 non-null    float64\n",
      " 3   Alcalinity       178 non-null    float64\n",
      " 4   Magnesium        178 non-null    int64  \n",
      " 5   Phenols          178 non-null    float64\n",
      " 6   Flavanoids       178 non-null    float64\n",
      " 7   Nonflavanoids    178 non-null    float64\n",
      " 8   Proanthocyanins  178 non-null    float64\n",
      " 9   Color            178 non-null    float64\n",
      " 10  Hue              178 non-null    float64\n",
      " 11  Dilution         178 non-null    float64\n",
      " 12  Proline          178 non-null    int64  \n",
      "dtypes: float64(11), int64(2)\n",
      "memory usage: 18.2 KB\n"
     ]
    }
   ],
   "source": [
    "wine2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2cefa95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       ...,\n",
       "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "        8.350e+02],\n",
       "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "        8.400e+02],\n",
       "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "        5.600e+02]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting data to numpy array\n",
    "wine_ary=wine2.values\n",
    "wine_ary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a75fe61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.51861254, -0.5622498 ,  0.23205254, ...,  0.36217728,\n",
       "         1.84791957,  1.01300893],\n",
       "       [ 0.24628963, -0.49941338, -0.82799632, ...,  0.40605066,\n",
       "         1.1134493 ,  0.96524152],\n",
       "       [ 0.19687903,  0.02123125,  1.10933436, ...,  0.31830389,\n",
       "         0.78858745,  1.39514818],\n",
       "       ...,\n",
       "       [ 0.33275817,  1.74474449, -0.38935541, ..., -1.61212515,\n",
       "        -1.48544548,  0.28057537],\n",
       "       [ 0.20923168,  0.22769377,  0.01273209, ..., -1.56825176,\n",
       "        -1.40069891,  0.29649784],\n",
       "       [ 1.39508604,  1.58316512,  1.36520822, ..., -1.52437837,\n",
       "        -1.42894777, -0.59516041]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing the numerical data \n",
    "wine_norm=scale(wine_ary)\n",
    "wine_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f243d1b3",
   "metadata": {},
   "source": [
    "# PCA Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8b2ce54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.31675081e+00, -1.44346263e+00, -1.65739045e-01, ...,\n",
       "        -4.51563395e-01,  5.40810414e-01, -6.62386309e-02],\n",
       "       [ 2.20946492e+00,  3.33392887e-01, -2.02645737e+00, ...,\n",
       "        -1.42657306e-01,  3.88237741e-01,  3.63650247e-03],\n",
       "       [ 2.51674015e+00, -1.03115130e+00,  9.82818670e-01, ...,\n",
       "        -2.86672847e-01,  5.83573183e-04,  2.17165104e-02],\n",
       "       ...,\n",
       "       [-2.67783946e+00, -2.76089913e+00, -9.40941877e-01, ...,\n",
       "         5.12492025e-01,  6.98766451e-01,  7.20776948e-02],\n",
       "       [-2.38701709e+00, -2.29734668e+00, -5.50696197e-01, ...,\n",
       "         2.99821968e-01,  3.39820654e-01, -2.18657605e-02],\n",
       "       [-3.20875816e+00, -2.76891957e+00,  1.01391366e+00, ...,\n",
       "        -2.29964331e-01, -1.88787963e-01, -3.23964720e-01]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying PCA Fit Transform to dataset\n",
    "pca=PCA(n_components=13)\n",
    "\n",
    "wine_pca=pca.fit_transform(wine_norm)\n",
    "wine_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e023ff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1443294 , -0.24518758, -0.00205106, -0.23932041,  0.14199204,\n",
       "         0.39466085,  0.4229343 , -0.2985331 ,  0.31342949, -0.0886167 ,\n",
       "         0.29671456,  0.37616741,  0.28675223],\n",
       "       [-0.48365155, -0.22493093, -0.31606881,  0.0105905 , -0.299634  ,\n",
       "        -0.06503951,  0.00335981, -0.02877949, -0.03930172, -0.52999567,\n",
       "         0.27923515,  0.16449619, -0.36490283],\n",
       "       [-0.20738262,  0.08901289,  0.6262239 ,  0.61208035,  0.13075693,\n",
       "         0.14617896,  0.1506819 ,  0.17036816,  0.14945431, -0.13730621,\n",
       "         0.08522192,  0.16600459, -0.12674592],\n",
       "       [-0.0178563 ,  0.53689028, -0.21417556,  0.06085941, -0.35179658,\n",
       "         0.19806835,  0.15229479, -0.20330102,  0.39905653,  0.06592568,\n",
       "        -0.42777141,  0.18412074, -0.23207086],\n",
       "       [-0.26566365,  0.03521363, -0.14302547,  0.06610294,  0.72704851,\n",
       "        -0.14931841, -0.10902584, -0.50070298,  0.13685982, -0.07643678,\n",
       "        -0.17361452, -0.10116099, -0.1578688 ],\n",
       "       [-0.21353865, -0.53681385, -0.15447466,  0.10082451, -0.03814394,\n",
       "         0.0841223 ,  0.01892002,  0.25859401,  0.53379539,  0.41864414,\n",
       "        -0.10598274, -0.26585107, -0.11972557],\n",
       "       [-0.05639636,  0.42052391, -0.14917061, -0.28696914,  0.3228833 ,\n",
       "        -0.02792498, -0.06068521,  0.59544729,  0.37213935, -0.22771214,\n",
       "         0.23207564, -0.0447637 ,  0.0768045 ],\n",
       "       [-0.39613926, -0.06582674,  0.17026002, -0.42797018,  0.15636143,\n",
       "         0.40593409,  0.18724536,  0.23328465, -0.36822675,  0.03379692,\n",
       "        -0.43662362,  0.07810789, -0.12002267],\n",
       "       [ 0.50861912, -0.07528304, -0.30769445,  0.20044931,  0.27140257,\n",
       "         0.28603452,  0.04957849,  0.19550132, -0.20914487,  0.05621752,\n",
       "         0.08582839,  0.1372269 , -0.57578611],\n",
       "       [ 0.21160473, -0.30907994, -0.02712539,  0.05279942,  0.06787022,\n",
       "        -0.32013135, -0.16315051,  0.21553507,  0.1341839 , -0.29077518,\n",
       "        -0.52239889,  0.52370587,  0.162116  ],\n",
       "       [-0.22591696,  0.07648554, -0.49869142,  0.47931378,  0.07128891,\n",
       "         0.30434119, -0.02569409,  0.11689586, -0.23736257,  0.0318388 ,\n",
       "        -0.04821201,  0.0464233 ,  0.53926983],\n",
       "       [-0.26628645,  0.12169604, -0.04962237, -0.05574287,  0.06222011,\n",
       "        -0.30388245, -0.04289883,  0.04235219, -0.09555303,  0.60422163,\n",
       "         0.259214  ,  0.60095872, -0.07940162],\n",
       "       [ 0.01496997,  0.02596375, -0.14121803,  0.09168285,  0.05677422,\n",
       "        -0.46390791,  0.83225706,  0.11403985, -0.11691707, -0.0119928 ,\n",
       "        -0.08988884, -0.15671813,  0.01444734]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PCA Components matrix or covariance Matrix\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a57e9580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.36198848, 0.1920749 , 0.11123631, 0.0706903 , 0.06563294,\n",
       "       0.04935823, 0.04238679, 0.02680749, 0.02222153, 0.01930019,\n",
       "       0.01736836, 0.01298233, 0.00795215])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The amount of variance that each PCA has\n",
    "var=pca.explained_variance_ratio_\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a2e1949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 36.2 ,  55.41,  66.53,  73.6 ,  80.16,  85.1 ,  89.34,  92.02,\n",
       "        94.24,  96.17,  97.91,  99.21, 100.01])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cummulative variance of each PCA\n",
    "var1=np.cumsum(np.round(var,4)*100)\n",
    "var1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "853f1af5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x6c3b430>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variance plot for PCA components obtained \n",
    "plt.plot(var1,color='magenta')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0a9eea69",
   "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>Type</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.316751</td>\n",
       "      <td>-1.443463</td>\n",
       "      <td>-0.165739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.209465</td>\n",
       "      <td>0.333393</td>\n",
       "      <td>-2.026457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2.516740</td>\n",
       "      <td>-1.031151</td>\n",
       "      <td>0.982819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.757066</td>\n",
       "      <td>-2.756372</td>\n",
       "      <td>-0.176192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.008908</td>\n",
       "      <td>-0.869831</td>\n",
       "      <td>2.026688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>-3.370524</td>\n",
       "      <td>-2.216289</td>\n",
       "      <td>-0.342570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.601956</td>\n",
       "      <td>-1.757229</td>\n",
       "      <td>0.207581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.677839</td>\n",
       "      <td>-2.760899</td>\n",
       "      <td>-0.940942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.387017</td>\n",
       "      <td>-2.297347</td>\n",
       "      <td>-0.550696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>-3.208758</td>\n",
       "      <td>-2.768920</td>\n",
       "      <td>1.013914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type       PC1       PC2       PC3\n",
       "0       1  3.316751 -1.443463 -0.165739\n",
       "1       1  2.209465  0.333393 -2.026457\n",
       "2       1  2.516740 -1.031151  0.982819\n",
       "3       1  3.757066 -2.756372 -0.176192\n",
       "4       1  1.008908 -0.869831  2.026688\n",
       "..    ...       ...       ...       ...\n",
       "173     3 -3.370524 -2.216289 -0.342570\n",
       "174     3 -2.601956 -1.757229  0.207581\n",
       "175     3 -2.677839 -2.760899 -0.940942\n",
       "176     3 -2.387017 -2.297347 -0.550696\n",
       "177     3 -3.208758 -2.768920  1.013914\n",
       "\n",
       "[178 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Dataframe\n",
    "final_df=pd.concat([wine['Type'],pd.DataFrame(wine_pca[:,0:3],columns=['PC1','PC2','PC3'])],axis=1)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d502500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of PCAs\n",
    "fig=plt.figure(figsize=(16,12))\n",
    "sns.scatterplot(data=final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb1104e",
   "metadata": {},
   "source": [
    "# Checking with other Clustering Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391846f9",
   "metadata": {},
   "source": [
    "# 1. Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b366d200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1eb9cf6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n1UOho1C33tPX7VyX+nA6AID2REkURiyLPa7T+zkAIC2URAEAAEQgiAIAAIhAEAUAABCBIAoAACACQRQAAEAEgigAAIAIdHEAAC2u3NiJlcZNZMxEYOQoiQKAFpeMnRgaUyjsM3bi3r6+sgNVAyiPkigAaAMjGTuRMROB6lASBQAAEIGSKACpK9dmp54qtf+pJ9oSAe2HkigAqSvXZqeeyrX/qSfaEgHtiZIoAJkwkjY7jVKPkrC9fX3RJV2UYgH5REkUgLZXa0lYLSVdlGIB+UVJFAAovZIw3ogD8osgCgAA5N7q25Zr7Yo7Bs17fON6SdLiSxcOmn/0zNM0/fRZNe+T6jwAAJB7a1fcoe0bNwyaN6FrqiZ0TR00b/vGDfsEW7EoiQIAAC2hs2uK5l1y+ZDLlJZK1YKSKAAAgAgEUQAAABGozkPqevp71Lu+N2rdvp3utfQFyxdE7/+MqWdo7lFzo9cHALQnSqKQut71vVq3c13UuoWOggod8T1Rr9u5LjqAAwC0N0qikAndHd1aNGtR0/dbSwkWgHwq9yr8UCq9Jl9JvV6fR/YRRAEAmurBuzarf+W2sp/t2LRbknTDFfeV/fyoEw7VsadMrGn/yavwnV1TRrR86SvyQ0lesSeIag8EUQCApupfuU07Nu3R+Elj9/ls/KSDK663Y9MeSao5iJJG9ip8jHq+Po/sI4gCADTd+EljddaFx1e1TqXSKSAtNCwHAACIQBAFAAAQgeo8AAAyIHxrsNwbgbz1lz2tEUStWiStWVr8e+tq9/+iM4vzpp0tzeB1dgBANoVvDZYbNFfirb+saY0gas1Saesa6bBp7u/Dpg/+fOsa9z9BFAAgwyq9Nchbf9mUXhBVWnqUKFeKlBiqNOmwadKCm8p/Vm5bANBATyxeol3Llg273N4+N3TRo/PPHdF2x82erUPmnVNT2gDUR80Ny40xLzDG/NoYM/zTIpSUHpU6bPq+JUmSW7Zc0AUAGbRr2bKBAGkoYwoFjSmMbOiivX19IwrMADRHPUqi/kbSWknjql5zqNKjUpQmAciZMYWCjrju2rptb6SlVQCao6YgyhgzSdKZkr4o6W/rkiIgQk9/T9RAwn07XUlBtWPonTH1DM09am7V+wMAtI5aq/O+KukiSc/XnhQgXu/6Xq3bua7q9QodBRU6RlaVkli3c11UwAYAaC3RJVHGmNmSHrfW3muMOW2I5c6TdJ4kTZ48OXZ3mRBb2iHFl3gkKPkYXndHtxbNWtTw/cSeQwBAa6mlJGqmpD83xmyU9CNJbzTGfK90IWvt1dbaGdbaGZ2dnTXsLn2xpR1SXIlHgpIPAACyJ7okylr7aUmfliRfEvVJa+376pOs7GpWaUeIkg8AALKHsfMAAAAi1KWzTWvtHZLuqMe2AADIqnB8u3LKjXlXKq0x8IZL+1BGclxDadVx/yiJAgBghJLx7SqZ0DV1n3HvQts3bogOZGo1XNqHMtxxDSXNY2601hg7D1WJecuQ/pQAwKk0vt1IpD0GXi1pj5X2MTcSQVQbSt4y7O7oHvE6MW8WJm8yEkRlV6Xx3YYbz43x2wCAIKptNeMtQ94qzL5kfLfSsduGGsstCbAIooD2VG3bqtj2VHloR0UQBbS5asd3Y/w2oL0lbas6u6aMaPmYtlRJ2y2CKAAA0FIa3bYqL+2oshtErVokrVla/Hvravf/ojPd/9POlmZQXQRkXaV2V6Hh2mAl2q0tVmnelcundssTIEuyG0StWSptXSMdNs39fdj04mdb17j/CaKAzKvU7io01GeJdmyLVZp3pfnUjnkCVKNc+61ybbRi219lN4iSXAC14KZ95yelUQByodp2V+W0a1usofKuXfMEGKly7bdK22jV0v4q20EUAABADYZrv1VL+yt6LAcAAIhAEAUAABCBIAoAACACQRQAAEAEGpajrsLBjUsHLc7DYMQjGZx5JIMx5+FYAQC1IYhCXYWDG4eDFudlMOKRDM483GDMeTlWAEhbpXH4hhpvL0tj6hFEoe7KDW6cp8GIax2cOU/HCgBpqjQOX6Xx9rI2ph5BFHJjqKo2qtgAIJ+qGYcva2PqEURlUGmwkMe2RY0wVFVbnqvYkvPNeQZa2+rblg+qpspStRTiEERlUGmwkMe2RY0SW9WW5Sq25HxznoHWtnbFHTIy6uyakrlqKcQhiMqoSsFCloMBxCs935xnNMsTi5do17JlkooDGidj8o2bPTs3gxsnDZSTkp7Vty3PZICSVF1lrVoKcQiiAsO93k67G2RFT3/PoOuR6w6xdi1bpr19fRpTKGhMoVgamgRUeQmikgbKE7qmavvGDVq74o5MBlFoLQRRgeFeb89zuxu0lt71vTIy6u7obsp1V6m0Ik8lFahsTKGgI667dtC8pDQqTyjlQbMRRJWo5fV2qmDQTMm12ozrrlxpRSNKKsJgrVRpVVM5BHUAmokgCsCIlJZWNKKkIgzWyu1/KHmrfgKGE3ZEyVt92UQQBSBTylUtjUQeq5+AoYQdUSadTw73Vl+lHsCloXsBl7LVE3heEEQBQKRaqh+pesRIlHZEOVx7r0o9gEuVewGXstcTeF4QRAFApNjqR6oe0UjV9ACeoDF+HIIoAKhBTPUjVY9AayCIqlIt47fRlw+ALMtqx5sP3rVZ/Su3acem3ZKkG664T0edcKiOPWViKukBEvulnYC8SfqSKqfQUajYl9S6neuG7MgTANKWVE9K2qc7i0ptv5rBBVB7NH7SwRo/6WDt2LRH/Su3pZYeIEFJVISYvqToQwpAHmS1483xk8bqrAuPl+RKooAsaN0gatUiac1SN711tft/0Znu/2lnSzNaN6hh+BoAyI+8jPuHfbVuELVmqbR1jXTYNOmw6cX5W9e4/1s4iGL4GmB4WW3/g/bDuH/51bpBlOQCqAU3DZ6XlEa1OIavAYbWKgPvojUw7l8+tXYQBWAfzSiBycuAxVlt/zMUStCA7ODtPKDNNOMNrHL7SPsNr1aR1TfogHZESRTQhppRAtPIAYvLDbdSbpiVVi2ZyWMJGtCKKIkCkDthaUyiXNsmSmYANBIlUUCGJN1TJN1Q9PT38JZkBcMNt0LJDIBGI4hqU7HD19B/VGMl3VMUOgoDvdyT30Dtkr6YJA30xxS+CXf0zNPoVgBVozqvTcUMX8PQNc2RdE9RqZ8vANVL+mKSpAldUzWha+rAZ0nfTEC1KIlqY9X2JUX/UQDyLOmLqRR9MyFWvoKoZCiXZBiXVYtauudxAADSFFaDJspVh0r5qxItHW4nOZ5qjiNfQdTAUC7T3f9rlhJEAQAapjSIaLf2VEk1aGfXlIF5YVVoIqkqzVM+hMPtJKo9jnwFUVJxKJc2Gb4FAJCe0iCiNIDIY/BQrUrVoKG8VomWHlu1x5G/IApASxlqGBOpdTvMRH4MFUTkNXhAfRBEAUhVpYGAJQYDBhqtXLugVq6erDeCKACpq9RxJh1mopEIIPZtF9QO1ZP1RBAFoO1VqlKkKrG1EUA4YXUl1ZPVaX4QVdpNwaIzpWln85YdgNSUq1KkKrE9EEC0pmaVMjY/iAq7KZDctNSSQVQ4tErpUCoMn9J45D+qUVqlSFVi/Tx412b1r9w28PeOTbslSTdccd/AvKNOOFTHnjKx6WlDa2pWKWM61XlJNwVSS3dVkAyt0t3RPWgYlWS4Fb7EG4v8B+IN9dZktdWc/Su3acemPRo/aawkafykgwd9vmPTHkkiiBpCFttvDTUeYdppk5pTykibKBVLLJLSip7+nrp9wZYbWoXhU5qH/EdW1DMoaYZKb03GVnOOnzRWZ114fNnPwhKpPEhjMOMstt8K+9CqpcPK4VTK7ywEagRRKpZYFDoKA4PsUkoBoJ7qHZQ0Q7m3JqnmrBw8SI0NbrLYfqtcH1r1Tlu5/M5CECkRRA1ISizyXErRyBK1WvT09wxqk0R7JLQrgpLWwWDGzVVrz+KN0l5B1KpFLf1WYFZL1HrX98rIqLuju6r2SGHDcInG4Rhe3qrMgHbTamMR7pd2AppqzVJJZvAAxi0mKVHr7uhOOymDxKQrCQoThY7CQAPxJEgEQkmVmaR9uitIgisA6Umq5hITuqbu054qDLKyrr1KoqTcDmBcWlXXLqUx5RqGSzQOR2VUmQHZ1kpjEbZfEJVTYVVdIu+v6me1DVe1hqt2TLR6wIt0JFWYSQncE4uX6JB559ALO9AErVmdl7R92rralTit2rckI4+SUpnkX9aq7KoVBoZGJrfVc0NVOyaofkSjhG/9JX+H86Vi1SbVmkB9tWZJ1EDbp2kt3SN6K2iFtyKlytWOibwfH7ItqcIsrbakF3bUYvVtyzPXL1PWtGYQJeW27RMAANVoVM/ha1fcISOjzq4pmemXKWtaszoPqFJPf48WLF+gvp196tvZpwXLF2jB8gXq6e9JO2kAMKTwjbfwbbd6vOmWNALv7JpSazJbUrolUZX6bcpYf05DDWQr0WC4FbRiw30A7aMZPYdjX9FBlDHmZZKulXSYpOclXW2t/VpVG6nUdiljbZoqDWQr8UXbSkrbNdGOCXB40w8or5aSqD9JutBae58x5mBJ9xpjbrXWPlTVViq1XcpYmyb6K0KMcv17UXKJvAUl5cb9y/KYf0CzRAdR1totkrb46d3GmLWSJkqqLogCMqaeHZuWVhNScplvYfAjxQ8tk8egpNXf9ONNNMSoS5soY0yXpFdLuqce20N2DNUerFVLVOrdPiosxcxayWXeSkQqdSzZLKV9MiX/S9UHQa0elORNq7yJVulNPYLCxqg5iDLGjJV0vaSPW2t3lfn8PEnnSdLkyZMlHVrrLtFEldqDtXqJSru0j4opESkNZJo5wG+Y3qTjyGYHe+WGlZEIgqr14F2b1b9ymyRpx6bdkqQbrrhPknTUCYfq2FMmNj1NSePsPDfITt7U6+yaMugtPSmfQWHW1RREGWNGywVQ37fW/me5Zay1V0u6WpJmzJhha9kf0lGuPVhaQQVtjOqv2hKR0tIYqblVUZU6lkS+9K/cph2b9mj8pLEaP+nggfk7Nu2RpFSCqFZR+qZenoPCrKvl7Twj6T8krbXWfqV+SQIqo41RNlAVhXoYP2mszrrw+EHzktKoSh68a/Ogkqu0Sq0AqbaSqJmS5ktaY4y538/7jLWWAcIyqlXaN8W2MaIUC8g/VwVoNH7SWEqtWkweG/fX8nbeLyWZOqYlV/L4hVyP9k15PO4EpVhAa0hKsIYrtUK+5LFxf+uOnddgef1CrrV901DH3dPfk/ngql5vylUq1cviMaetXEP0rL79ByBe8mZgUpq0+rblmn76rKreGGxE4/5GlnARRNWgEa+u5zkQ6V3fKyOj7o7u3ASVscqV6rX6MccqbYie9f6QgGrRrYCTvBk4oWvqwLh900+fVdc3BisFasOlq1ElXAxAnDFJIFLoKGjdznUDpR15kQRY3R3daSel4ZJjTf5l4ZifWLxEe/v6tLevT4/OP1dPLF6SdpIkFRuiH3HdtYPe6gNaQbkBgOsx+G8eVRqwOJmf/Isd0DgM1IzMiPO4UQMpUxLVJNVU/yRfzq3aN1GelLYB6+nvyXRJU9JxZtKPkkSJTyNV6qw0kaVqyyTAlqhSbQS6FWieLPXnRUlUkyTVP5JU6CjktqQpD3r6e7Rg+QL17exT384+9fT3RG8rbANmZHJxvpJSH0p8Gi9s6xV2WCppoDPQrCgNsLOUNiCvKIlqonbpBTttYeCTBKq1lB5RMlhZ2sOwNEK1x5SnHszpqBSoL0qi0JLaqW1WmkobjbdC6UYrHhOAxiCIAlCTVqw+bMVjAlB/VOcByJxWrCYEWkVMNwOtipIo5FbSp1bfzj4tWL6gpgbkSEelLhmoUtvXE4uX6NH55w7kV1a6r0D7ie1moBURRCG38t6nFoZ+Y6ydqtRG0r8XgSWypFH9LuUNQRRyjQbk+ddOwVIlI+1+gLxCNVbftlyLL12oxzeu1+Mb12vxpQu1+NKFWn3b8rST1jIIopA5pf08UVWHdkCAhHoLq93C4Vbaufqt3mhYjszJ6+DOgESj+KxJoxF0Iwe8rRY9qTcWJVHIpHBcOqrqUItmjyfY6m2XShu4Z2mMxnLSaASdDHjbzmPoDSUJMpMqxjxXLxJEAWhpaQx30spVc2GQmJchZNJoBE3D68paKcgkiBoCbXOA1tDKQU0akvwkTxEra0FmaSP8kZaOEUQNIWybU8/X6GsdILeeA+wCiNPsakKgFrFBQruIrfYliBpGI9rmhMGZkak6MKt1/RAdVtaOoLY9pVFNCMSig8zhxZSOEUSlpNb+jerVPxIdVtaunkEt4qVRMkQ1IfIka1VorYAgqk0MVeJEh5W1Iw/TR8lQvj1412bdcMV92rFpt3Zs2q0brrhPD961Oe1kAUMiiKqDPFSJUeKEdkDJUH71r9ymHZv2aPykgzV+0sHasWmP+lduSztZg9CuCKUIouogLwEKpSUA0lZa4hSWNo2fNFZnXXi8zrrweI2fNDbFVJZHuyKUIoiqEwIUDCfrJZZ560QR+RSWOEkmc6VNw6FdEUIEUSiLN87qL+sllnnsRBH5lJQ4ZbG0CagGQRTK4o2zxsh6iSWdKALAyBFEoaKsf+EDAJAmgigAAIAIBFEAAAARCKIAAAAiEEQBAABEIIgCAACIQBAFAAAQgSAKAAAgAkEUAABABIIoAACACARRAAAAEQiiAAAAIhBEAQAARCCIAgAAiEAQBQAAEIEgCgAAIAJBFAAAQASCKAAAgAgEUQAAABEIogAAACIQRAEAAEQgiAIAAIhAEAUAABCBIAoAACACQRQAAEAEgigAAIAIBFEAAAARCKIAAAAiEEQBAABEIIgCAACIQBAFAAAQgSAKAAAgAkEUAABABIIoAACACARRAAAAEQiiAAAAIhBEAQAARCCIAgAAiEAQBQAAEKGmIMoYM8sYs84Y84gxZmG9EgUAAJB10UGUMeYFkv5N0tskHSPp3caYY+qVMAAAgCyrpSTqBEmPWGvXW2uflfQjSXPqkywAAIBsM9bauBWNOVvSLGvth/3f8yWdaK39aMly50k6z//ZLWldfHIBAACa5ghrbWelD0fVsGFTZt4+EZm19mpJV9ewHwAAgMyppTpvk6SXBX9PkvS72pIDAACQD7UEUb+SdKQxZooxZn9J75L0k/okCwAAINuiq/OstX8yxnxU0s2SXiDp29baB+uWMgAAgAyLblgOAADQzuixHAAAIAJBFAAAQASCKAAAgAi19BOVW8aYF0uStfbJdFNSHWPMW621N5eZ/1lJeySNlfSMtfaf/fwPSJrgF9tmrf3uCPbxYbne6CXpRmvtTTWk9ypJN0q62Vr7XOx2ymz3eLlr9x2S1lprrxtm+fly/Zq9SNLT1tpvVVju3XJddVwp6SRr7a1lljnDT54o6Slr7Vdij2OkqrlejTHjJO2WO9bnrLW7Y7c1XFr89D778MuNtdbuqbCNl0jaaYMGmTHpKt1OpX0meWKttckyw6R9YLvhuiXLlE3vUNsd5ljK7meE6+6Tn37+oPwYIn+GPd4h9v1iqTnPUmPMOX5ypqQV1tolDdxXxeu30UZyjVaxrfDcDhxTpWsmbeWuRVV4lvnlm3J/DpnmZuehMWaupMMlHSD3xfYWSf8jaa+kN0paLneT7JHrMuEYuS+s2/3y75J0laTrJb1frhf0F/n110u6RtJJkvaX9Lxfv0vSDyWdKulVfl0j9yW8yq/zYUkr/TITJfX6dd8p6euSrrfW7vBftMl2Z0q6xf/fJenfk+WC471E0j1+u91++mm5MQfvUPGB8E8l+TPGL/chST/w6f2YpM/79P+dpGf8cb9eLpj4mDGmR9LSJH3W2tP9di/yyyb5foJPi/HL/tT/f4q19o1+QOl5kr7p9/chuc5UZ8oFML9I0i7pueA8/EnSH/z0M3LjK75F0hRJS3w+HOXz+mlJs4JtSdIvfZqettZ+3af9Wp/2F8ldExMkjbXWfsEY80ufj3slnSLpv/35/HdJD/j5b5D0K2vtQmPMTX7fyXUyMciT1/tjvkjSWyXd6tcvSPqeT/s0n1fflnSD3LV7jaTL/PG+SNJPrLXfD66BudbaHj/9H5Ie077X3DGSHvfrHyTpfr/vN0v6rk/fWT6vjM+7HSpei2f687lW0nxJP5M0Ve4+usUvc6qk6/z675Z0tz8nb5AbtslIeo+kg/2/xySd4/N3iaRLJS3zy73PH/tUuXsoSeP3VbwnnvDTr5T0rKQtctfKGEkX+jzolTRbro85I3ddf8fPT/qdM37eAz5NZ8pd42Pkrqtn/b5mSnqhz4clkr7m82Ot3LOixy9zkFwXLVMlHSt3f/XKPWeS9H1Y7tpeK+lISV8M0rLI7780H78o6UG/3QP9OVwiaaGk3/hzNlvSan8cE+XO9/N+P2/x5+GVkub6/fRKmiz3A+mVctfsqjL5EObnvCA/Vkg6JPg7PCfT5H7klB5vmI7Rkv65zDmYKHdtJeft5ZK+6qevlLs2npZ0ut/u0z6vf6x9nwGvVfFZb/xnP5e0Ve6eOlXSNn9u18g9uw6Vu/6T+/t/fV5uknSvT/txctfCNXLn/loVn+N/r+I1fpakR/z6L5R7BpXmVbitZT7feyW9V8Xn8KmSbpMz8PySJGPMYkn9ctfD933+9kr68yAd75F7ppReo6Mk/V7F598r/Lwlkr4l9yy6XsWaJSP3nXWL3Lmd7z+fKfd8/qmff5akO1V8Fn5Y0l0+Xaeq+B30Nrnnzsl++8kzeuC7eIjvvOP8+VgiaYak5Ee5kbtffqahr8XwWXagpD6fVwvk7ru1ko601v6N3/dcue+i5Lqc69cvzdP9/frJ905yjb7TH99eSQdaaz+jYTSlOs8Y02uMuch/kb9PUoekr/h/Cv7eLZf5t8tl1gS5L6+xwfJb5S6kd8uN1fdxuYfVeElfkHSu3EPgFcH6r5ALKD4jl5E/l8vYscE6c4JlXhqsO7A/Y8znS7b7B5/en8ldyMlyD/vj/Tu5GyPZ7gslPe9vrj3BuqeV5M/oYLl7rbX/ZK39kt/njf7YX+Pz7V98vt3qh9jZz6fvOklb/XY/KXfBhPn+kmD6ZcE6T/gHzH/J3YRJXidB1+1+uXsk3e6DvxeXnIdkere19h5r7RckbQ7y4cDg+MI8/HWQpguCPDkx2OYEuS+dDXIeCfJhTHA+Xxoc7+8kJTfDEyXXSZgnT/lfav8g6Y/BZ1OCtP+vn3+ZpI1yQe25cg+CgTQaY34apP8bwfVwkspfc+OC9U1wTKNVvF7HBWntUPFavE4ukDhA7iE+Su4L49cqXsvX+fxJtrWftfZzPj0XyQWzs+QedLslPZCcn2C5o4P1xwT7ODdYfz9/Tn7uz+3x/vj2yn3xTZQb+mmDpC/7vH1rsN2Nwfw7g+1OCNL0gmA7kgvykn3+LEjvYUGebA6WuS9I+8PB/t4dbHdvsK6R+7JI0jK5Qj4eFWzXBPOPVfF6fy44jl2Sdgb76Qrya22Qrs8F8/9QIR/C/HxZcqz+/gzzJzwnB5Qc7/5l0vG2Cudgl9x9nOTDO4PljgiOd/9guqDyz4BDVXymHiD3rPqJpL+S9Bm//Fhr7eV+v0/KfZEm98gBkv5G7t45MUj7firea5J/dvrn+G5Ja4JA5+N+/d9VyKv9VLzXnw7yOnwOHyjpCvn7s+R7b1pwPTwXrD8zyNM+lb9GD9bg598zwbb2V/EZHd4v3cG5/W2w3d8E8/fX4Gfh8iBdszX4u+puuWd/+IzeGuTpw8Gxht95LwjS+jYNvo9OUeVrsdyzLLx3ngqWmRM8X9+nwdfl0yp+v4R5ujuYf2dwrP9Pxef+iOKjppREGWMulrsATpC7CZ6Vi6o/JVdysELuRN4pd5KTX253yz3AzpX7VbZX0tlyJ/5muV8wr5f7MhktV9p0otxJP9Jv40BJH5H0UbmL5HQ/z8hdkH/0aXmV3K+kP5O7AB+SK3nZXy6geIfcg83IXdQvkXtY/Mrv+02S/tNv6zS5X2LflPs1vMLvc45P95VyX+p75R6ir5K7GGbKPexHyz3cT5a0MinVMMZ8zq9zpVwJz2q/3a3W2mv8MvP8sX9K0pestf8YnIdk/TFyN///SPq03K+ER32aLvNBT3LeHpb0OrkL+HFJn5V7mNwp9+vmIr/MTLlfV9+Ue2gfVCZdo33+Wmvtx/38M+VKEE/y+T3OJ/ctkhYH+Xmv3K/dfh9QJsd0stz1dKSKv3peJxewflfuprnE+io/H1De6c/n0/58PuX3G6b3Yv/ZfLlf/mv9NvcG+fMeuRv/m35fd8tdg2+Tu2FHyZ3zF1lr/9qv82Vr7Sf99A0qnvsun28vl/sF9x25L4rdKt7MT8rdQ5+TdLnPt0Mk/aOkf5UbXmmy3DVxsNy1ttCfV+unH/HHtcNae6dPx/wgfzokHWSt/a3/+73B9Xe2XAAruWDyFX4fU6y1a4L1R8k9IB+Re+D3GWNe6f9+s/98o7X218E6p/ntPmqtvdfPP8Ja+6ifnhbsY6bcg26UXND0lDFmgt/n/1pr7/fLvV/uYTpZLnje7vP2XknH+7QfGaRjTJC+9X6dZN09QVqm+vNmJG231t7l558r98V7s6RXB/Ov9dfDWEljrLWf9fNnyN3/f/L7mSDpIZ9fb7PW/tQv9ypJz1pr1xpjTrfW3lYmHzYkx+0/myBXqnx9+HfJOTle7voNj/cISZ1BOsL9hedghl/n9T4fHrHWrvaf3Sl3fd4s6T9U/AFQkHSDtfZ2Y8zV/rM5cs88I/d8eFbS3dba+3yp/xslfULSJ5L7zu/jc3IlQFv8Muf67e2QdJ3Pq2/44x0r90xY7qefkbTYWvuY39Y3/XJj/Gff9dfJw0Fe/b1cVb8kbbHWXhKkY6/cs2G6XMmSkfS3cl/yR/l1fm+t/bRfZ4G1dpGfnhyk48/knsPhNXqLXKBym0/XKEkdwTW7SK6U7UQVA3oZYw6X+0E0yuf/Af783yR3jY+W9E9ytRu3SPqgXND9sF//o/54JPecPFruHv2RXBBykN/nFr/MC32ak7y+3e/zpdbaj/htvlVSX3AfnSJ3/wx1LR7ot3mLpK7g+pvj0zFZ0pfkSrVulvsBsUvF6/I1cs/QUX5b9/rlviH3XH/ap32D3LP1BJ/uIyR9zI6gOUuz2kQ9L5eoy+Qe6E/LPTwOlTupO+VO2E1yD5uXy104ndZVUb1W7iJ6i1zm/t5v52RJ/22t/aAx5la5L6JkHw/KZdJb5L4Ad/j5r5UrmpRfv89X89wqd0Iuk7vZPiwXIJwk9wD7jL+Rnpb0Er/OzXIXwUJjzGxJx/jps+QCnYt8mh4vM/8UuS/bi+QCgB/7fPihXCS/R+5L864gH/cL1n+Z3M280BefJqbIXVxflftlpwrrv1iuaPyr/jNjrT3QH2PiObkvheTX8mq5XyoHSjrdWvtSv/yZckHyfLlz91CFdK3xx3Sc/8ViJF0gFwAk08kvw64gP98uFxAf4PMndIE/XiPp7XIB0k7/9xustVPLHNNbS87n4WXS+5xP7+ggfy4u2dYr5H6xXCT3xZPk7f5yN/5Yv5/VwfG+yxjzuJ/u9ulbKPcAeVLSX8tdC8n1bqy1n5ckY8x35IKEg3w6JssFgBPkfuElVXYHyxXJf0bSdGvtfsaYT8tdy0lV4Pv9ffVHSaf7h+4f5c7t08aYF/rljjXG/L5ku6fIVY/9t9yX1xZjzI/9MsfK/WgYqOI2xuyw1j5gjPmRXFBzvaQPGWPerGLV/X/JPajfa4xZ4o/lncaYr/vlX2KMmRWk4yZ/PJN9fibVmp83xvyrX+dQuSqHmXLPnyv9/DfLBZWfknR2sPybg+28T+7HRVLFtNbnV1KNf6v/f5Qx5iQ/bX2eLJD0WmPMS/3yh0qa6p9lt/hrIalqSx7iSbXWaB88dBljbHi8xrXtm2VcW8Pr5b5kk3U/YIwprTZ7jTFmkj+mZLlzJa3weT9L7n6ZKemXvtTqMWPM9yS9whjzJX98xyfnyRhTrtrternSiLPkqoYeUfEZfbiK9/Eav/0Z/tz9Nrk/5J5VRu6L+nXGmF/L/eB9VsV7KrSfXJX9RT5vX+Dvo2ettWv9MmODfP9pMH1JErh4YyRNDj57XNJSY8y3JfUZY/5c0p9Za9/u93GiMeZTPr3HSdrs173NWru/v9dGSzrAWntekq5gf68zxkz2efX3xpjf+unz5UqNrpH7ofpbuR9mG+WCkg/6fT7h03SN30+X3PfWj4N0nSBXXSq5Ksdk+i9tsZnE2/15+rTcc+bhII0zrbXv9mk/1Vr7Fn9cJ8r9aEqONzm+15bk9cv8dG+Qpqft4Da9H5C7Hq6RK3XcVHLs18i1TU1+CHzCGJMUZJwgd/0ZuUAuud6MXIzQ4dd/u1yNyMeMMb1yz9iP+GvjV3LP2FPlqlEvk9Rri81ZulWsfqyoWUHUjZL+Qi6RS+Qe/Mnf35L74i2d/rmkB42rovqNtfYeSfcYY94YTH9cLjiSXB11Z7CPMRXWuVSuyDV5CCTVPMtUrKbpta5h3z/IBVRJ1dEquZPwP/7vX8j94iqdXhms/+1gH+H8QjD9Bbno+Sl/c1/uo/QnS272+yts94FkAWttpXVL10/2+aTcQ/b3wTGWbuspa+2/JNsts/wvrLW/kCRjzO/k6vwrpkvSV4LlHw9+lYXT4XauStJa5pi+GWzrp3IlV09VOiYNrgosPZ+V0ltpW78Z4nyG+/meLbZ5C4/xaR8MXS9XjH2s3PX3X8H1+tMgAHuNim0uVsk9XJP76Dn5InRjzM99Xn3GGPM9v4+lcqWnz1trv25cA/nnrbXfMK40MJn+nN9WstwdFbb7IxXbMo4PlvmRXDH5Mz6dSRV3p4rVNEn18L/65cbJ3bvfk7uuk6qdmcHyc+Wus9J0XOzXf7LMOicG+1Aw/y/kSo1Llw/nh+n4sFzAnOzjuODYt8k9xJ+Re54an29zgvWnq1jd/nCSP9barxljLgzyOlznOxXyfXqQ3rdLuqXkfJaes4t9OkvP+9f99Z0cxyxjjPx1VpD7Anqm5PjGleRJeD7Pkgt6LpL78ZtcvzP9cpK0JLgPOlT+/pgu6RBr7S+MMUk15QNy91QofJb9KLiPrjLGjFKxrecXjDHny7VHus1Pb/DPip+XWe4vjDFJCfXJkv7STx8X7OMIuR9PxudFcm7XBPfaaLlAuFy6TvLbvchfG+cNMx3m535yJcpf8J+NVvF7a1OQrltV/J4Lp9ca9wPJSDoq3G5JnkwN0h4+p/ZKeqTkeK/318cjJXl9np+fpOnikn2E+fAquSCy9Ng/Yox5eZnzcWuw3WuGyJ+XyjWnON+n5Yoy6xwrd/1eJml5cEwjio/arsdyY8yRks7wfyb1oEAmGWNeZa39jZ8+31p7VYXl5shVOd1uXEnCIj99sbX2i36ZY60fmskY81fW2ivLTJ8pV9WWLPd5ueBln+3KBRwnyZWI3VVm/guttf/mt/NWuVLb+4K/H/PL/UEuiBotV9q4x08faq1d6Jf/h2Afpen4llyQVbpOuI8wLfOC/YXLz6uQjmflvhjnBn//zG93c7CPUdbaq/223itX1TparnTk34JzdXSw7qYgr8N1uiX1lDneb1hrP+qXPy/YX3gOw3N2saQfVzjvYVX6/tbab/r558uVUJUeX3ieBqrd/DqftNZ+2U9fkNZztYr7pexyJfM/J/fjTQq+K2K+Q0q2G1bpf80WG0VXmh6Un8aY1wc/HC+okK6XqPjDL5w+zBarFQflz0jzrhqleSXpzirz4bIgveH5CI/p+Yj8qdt3fzt2cfB5uaonyUW2BFHIsoXGVW0kv8QqPdjeJenXxlWVnCZptZ8+IVjms8G2LjDGHFxm+mRJfwyW+6BcNU7pdv9KrhGmkSuV+GOZ+RcYYw6qsN2wGvfzwfRHg+n3GWN2ltlH6fG9UsUq3XCdQVXFQVouqLCPCyqk4zS5dh7JPi6UaxNSmvYLjHuFunRbp8m1YZQx5ofBduYMkSezJa0vc7zvMMY8Vm5/wTkMz9kJkl5Z6byXpH3cMOcmnD5NxWq3ZP39gnOd1nN1pPdLpeXC+eereBzhMcV8h4TbDav032mM2TzMdOk+/o8x5sQyn4Xp+mAwP5yeb4x5osxxD5UntSjNq5OrzIcwveH5CI8pJn/qd41aa9vqn6TXB9PT004P//g31L+RXq8lyy0ot84QywxafoTLVTudt+3Wcx+VzkEe0h6z3dSeq5H3y0jukYrnMCJd0ddPZNpr2m49z0ejrsWI/KnbNdp21XkAAAD1MKJ+EAAAADAYQRQAAEAEgigAAIAIBFEAAAARCKIAAAAi/H/ZAbscezeMkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As we already have normalized data, create Dendrograms\n",
    "plt.figure(figsize=(10,8))\n",
    "dendrogram=sch.dendrogram(sch.linkage(wine_norm,'complete'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "225475f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Clusters (y)\n",
    "hclusters=AgglomerativeClustering(n_clusters=3,affinity='euclidean',linkage='ward')\n",
    "hclusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "41f19970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    64\n",
       "0    58\n",
       "1    56\n",
       "Name: clustersid, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.DataFrame(hclusters.fit_predict(wine_norm),columns=['clustersid'])\n",
    "y['clustersid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dce2a41c",
   "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>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "      <th>clustersid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</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</td>\n",
       "      <td>2</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0       1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1       1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2       1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3       1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4       1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..    ...      ...    ...   ...         ...        ...      ...         ...   \n",
       "173     3    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174     3    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175     3    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176     3    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177     3    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \\\n",
       "0             0.28             2.29   5.64  1.04      3.92     1065   \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050   \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185   \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480   \n",
       "4             0.39             1.82   4.32  1.04      2.93      735   \n",
       "..             ...              ...    ...   ...       ...      ...   \n",
       "173           0.52             1.06   7.70  0.64      1.74      740   \n",
       "174           0.43             1.41   7.30  0.70      1.56      750   \n",
       "175           0.43             1.35  10.20  0.59      1.56      835   \n",
       "176           0.53             1.46   9.30  0.60      1.62      840   \n",
       "177           0.56             1.35   9.20  0.61      1.60      560   \n",
       "\n",
       "     clustersid  \n",
       "0             2  \n",
       "1             2  \n",
       "2             2  \n",
       "3             2  \n",
       "4             2  \n",
       "..          ...  \n",
       "173           1  \n",
       "174           1  \n",
       "175           1  \n",
       "176           1  \n",
       "177           1  \n",
       "\n",
       "[178 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "wine3=wine.copy()\n",
    "wine3['clustersid']=hclusters.labels_\n",
    "wine3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6096c06",
   "metadata": {},
   "source": [
    "# 2. K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "13ee3e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "from sklearn.cluster import KMeans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "62ec5c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we already have normalized data\n",
    "# Use Elbow Graph to find optimum number of  clusters (K value) from K values range\n",
    "# The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS \n",
    "# random state can be anything from 0 to 42, but the same number to be used everytime,so that the results don't change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07c8f4f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# within-cluster sum-of-squares criterion \n",
    "wcss=[]\n",
    "for i in range (1,6):\n",
    "    kmeans=KMeans(n_clusters=i,random_state=2)\n",
    "    kmeans.fit(wine_norm)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "21670c5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)\n",
    "plt.plot(range(1,6),wcss)\n",
    "plt.title('Elbow Graph')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f970c99b",
   "metadata": {},
   "source": [
    "# Build Cluster algorithm using K=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0cbcce31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=30)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster algorithm using K=3\n",
    "clusters3=KMeans(3,random_state=30).fit(wine_norm)\n",
    "clusters3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6585c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 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,\n",
       "       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])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters3.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "593407cb",
   "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>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "      <th>clusters3id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</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</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0       1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1       1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2       1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3       1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4       1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..    ...      ...    ...   ...         ...        ...      ...         ...   \n",
       "173     3    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174     3    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175     3    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176     3    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177     3    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \\\n",
       "0             0.28             2.29   5.64  1.04      3.92     1065   \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050   \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185   \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480   \n",
       "4             0.39             1.82   4.32  1.04      2.93      735   \n",
       "..             ...              ...    ...   ...       ...      ...   \n",
       "173           0.52             1.06   7.70  0.64      1.74      740   \n",
       "174           0.43             1.41   7.30  0.70      1.56      750   \n",
       "175           0.43             1.35  10.20  0.59      1.56      835   \n",
       "176           0.53             1.46   9.30  0.60      1.62      840   \n",
       "177           0.56             1.35   9.20  0.61      1.60      560   \n",
       "\n",
       "     clusters3id  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  \n",
       "..           ...  \n",
       "173            0  \n",
       "174            0  \n",
       "175            0  \n",
       "176            0  \n",
       "177            0  \n",
       "\n",
       "[178 rows x 15 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign clusters to the data set\n",
    "wine4=wine.copy()\n",
    "wine4['clusters3id']=clusters3.labels_\n",
    "wine4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "35094a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    65\n",
       "1    62\n",
       "0    51\n",
       "Name: clusters3id, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine4['clusters3id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6fd82d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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