Assignment-08-PCA Data Mining (Wine).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment-08-PCA Data Mining (Wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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,
   "metadata": {
    "scrolled": true
   },
   "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",
       "    </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('Database/wine.csv')\n",
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    71\n",
       "1    59\n",
       "3    48\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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",
       "      <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</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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine2=wine.iloc[:,1:]\n",
    "wine2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting data to numpy array\n",
    "wine_ary=wine2.values\n",
    "wine_ary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing the numerical data \n",
    "wine_norm=scale(wine_ary)\n",
    "wine_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "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": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PCA Components matrix or covariance Matrix\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "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": 41,
   "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": 41,
     "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20ea5bec7f0>]"
      ]
     },
     "execution_count": 42,
     "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 45,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ea5a796a0>"
      ]
     },
     "execution_count": 54,
     "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",
   "metadata": {},
   "source": [
    "# Checking with other Clustering Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=3)"
      ]
     },
     "execution_count": 57,
     "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    64\n",
       "0    58\n",
       "1    56\n",
       "Name: clustersid, dtype: int64"
      ]
     },
     "execution_count": 58,
     "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": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "wine3=wine.copy()\n",
    "wine3['clustersid']=hclusters.labels_\n",
    "wine3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": 81,
   "metadata": {},
   "outputs": [],
   "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": 82,
   "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",
   "metadata": {},
   "source": [
    "## Build Cluster algorithm using K=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=30)"
      ]
     },
     "execution_count": 83,
     "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": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 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,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters3.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>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>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              2  \n",
       "1              2  \n",
       "2              2  \n",
       "3              2  \n",
       "4              2  \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": 85,
     "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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    65\n",
       "2    62\n",
       "0    51\n",
       "Name: clusters3id, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine4['clusters3id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}