lab8_kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_wine()\n",
    "data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, 1.560e+01, 1.270e+02, 2.800e+00,\n",
       "        3.060e+00, 2.800e-01, 2.290e+00, 5.640e+00, 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, 1.120e+01, 1.000e+02, 2.650e+00,\n",
       "        2.760e+00, 2.600e-01, 1.280e+00, 4.380e+00, 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, 1.860e+01, 1.010e+02, 2.800e+00,\n",
       "        3.240e+00, 3.000e-01, 2.810e+00, 5.680e+00, 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       [1.437e+01, 1.950e+00, 2.500e+00, 1.680e+01, 1.130e+02, 3.850e+00,\n",
       "        3.490e+00, 2.400e-01, 2.180e+00, 7.800e+00, 8.600e-01, 3.450e+00,\n",
       "        1.480e+03],\n",
       "       [1.324e+01, 2.590e+00, 2.870e+00, 2.100e+01, 1.180e+02, 2.800e+00,\n",
       "        2.690e+00, 3.900e-01, 1.820e+00, 4.320e+00, 1.040e+00, 2.930e+00,\n",
       "        7.350e+02]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  \n",
       "3                          3.45   1480.0  \n",
       "4                          2.93    735.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(X, columns=data.feature_names)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  target  \n",
       "0                          3.92   1065.0       0  \n",
       "1                          3.40   1050.0       0  \n",
       "2                          3.17   1185.0       0  \n",
       "3                          3.45   1480.0       0  \n",
       "4                          2.93    735.0       0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = y\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n",
       "       'total_phenols', 'flavanoids', 'nonflavanoid_phenols',\n",
       "       'proanthocyanins', 'color_intensity', 'hue',\n",
       "       'od280/od315_of_diluted_wines', 'proline', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 14)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "      <td>0.938202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "      <td>0.775035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
       "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
       "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
       "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
       "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
       "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
       "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
       "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
       "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
       "\n",
       "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
       "count     178.000000  178.000000            178.000000       178.000000   \n",
       "mean        2.295112    2.029270              0.361854         1.590899   \n",
       "std         0.625851    0.998859              0.124453         0.572359   \n",
       "min         0.980000    0.340000              0.130000         0.410000   \n",
       "25%         1.742500    1.205000              0.270000         1.250000   \n",
       "50%         2.355000    2.135000              0.340000         1.555000   \n",
       "75%         2.800000    2.875000              0.437500         1.950000   \n",
       "max         3.880000    5.080000              0.660000         3.580000   \n",
       "\n",
       "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
       "count       178.000000  178.000000                    178.000000   178.000000   \n",
       "mean          5.058090    0.957449                      2.611685   746.893258   \n",
       "std           2.318286    0.228572                      0.709990   314.907474   \n",
       "min           1.280000    0.480000                      1.270000   278.000000   \n",
       "25%           3.220000    0.782500                      1.937500   500.500000   \n",
       "50%           4.690000    0.965000                      2.780000   673.500000   \n",
       "75%           6.200000    1.120000                      3.170000   985.000000   \n",
       "max          13.000000    1.710000                      4.000000  1680.000000   \n",
       "\n",
       "           target  \n",
       "count  178.000000  \n",
       "mean     0.938202  \n",
       "std      0.775035  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      2.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol                         0\n",
       "malic_acid                      0\n",
       "ash                             0\n",
       "alcalinity_of_ash               0\n",
       "magnesium                       0\n",
       "total_phenols                   0\n",
       "flavanoids                      0\n",
       "nonflavanoid_phenols            0\n",
       "proanthocyanins                 0\n",
       "color_intensity                 0\n",
       "hue                             0\n",
       "od280/od315_of_diluted_wines    0\n",
       "proline                         0\n",
       "target                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2, random_state=57)\n",
    "X_pca = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.18562979e+02,  2.14921307e+01],\n",
       "       [ 3.03097420e+02, -5.36471768e+00],\n",
       "       [ 4.38061133e+02, -6.53730945e+00],\n",
       "       [ 7.33240139e+02,  1.92729032e-01],\n",
       "       [-1.15714285e+01,  1.84899946e+01]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 2)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_pca = scaler.fit_transform(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.56171362, 0.56775687],\n",
       "       [0.55068395, 0.2565456 ],\n",
       "       [0.64693687, 0.24295786],\n",
       "       [0.85745156, 0.32094407],\n",
       "       [0.32626957, 0.53296877]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27035726bb0>"
      ]
     },
     "execution_count": 107,
     "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": [
    "plt.scatter(X_pca[:, 0], X_pca[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elbow Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "see = []\n",
    "rng = np.arange(1, 10)\n",
    "\n",
    "for k in rng:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(X_pca)\n",
    "    see.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13.031363776244792,\n",
       " 6.389448206458044,\n",
       " 4.002668576365032,\n",
       " 3.2423435838224504,\n",
       " 2.6170304820200916]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "see[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27035783610>]"
      ]
     },
     "execution_count": 110,
     "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": [
    "plt.plot(see, rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4, random_state=57)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4, random_state=57)\n",
    "kmeans.fit(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = kmeans.predict(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 1, 1, 2])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  target  predict  \n",
       "0                          3.92   1065.0       0        2  \n",
       "1                          3.40   1050.0       0        1  \n",
       "2                          3.17   1185.0       0        1  \n",
       "3                          3.45   1480.0       0        1  \n",
       "4                          2.93    735.0       0        2  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['predict'] = y_pred\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = X_pca[df.predict == 0]\n",
    "df2 = X_pca[df.predict == 1]\n",
    "df3 = X_pca[df.predict == 2]\n",
    "df4 = X_pca[df.predict == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 2)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44, 2)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 2)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 2)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x270357ebbe0>"
      ]
     },
     "execution_count": 120,
     "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": [
    "plt.scatter(df1[:, 0], df1[:, 1], c='red')\n",
    "plt.scatter(df2[:, 0], df2[:, 1], c='blue')\n",
    "plt.scatter(df3[:, 0], df3[:, 1], c='green')\n",
    "plt.scatter(df4[:, 0], df4[:, 1], c='yellow')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], marker='*', color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}