63 K Means Clustering Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Means Clustering Project\n",
    "Usually, when dealing with an unsupervised learning problem, it's difficult to get a good measurement of how well the model performed. For this project, we will use data from the UCI archive based off of red and white wines (this is a very commonly used dataset in ML)\n",
    "\n",
    "We will then add a label to the combined dataset. We'll bring this label back later to see how well we can cluster the wine into groups\n",
    "\n",
    "### Get the Data\n",
    "__Download the two data csv files from the UCI repo or just use the downloaded csv files__\n",
    "\n",
    "__Use read.csv to open both datasets and set them as df1 and df2.__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 <- read.csv(\"winequality-red.csv\", sep = \";\")\n",
    "df2 <- read.csv(\"winequality-white.csv\", sep = \";\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Now add a label column to both df1 and df2 indicating a label 'red' or 'white'__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1$label <- \"red\"\n",
    "df2$label <- \"white\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Check the head of df1 and df2__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>fixed.acidity</th><th scope=col>volatile.acidity</th><th scope=col>citric.acid</th><th scope=col>residual.sugar</th><th scope=col>chlorides</th><th scope=col>free.sulfur.dioxide</th><th scope=col>total.sulfur.dioxide</th><th scope=col>density</th><th scope=col>pH</th><th scope=col>sulphates</th><th scope=col>alcohol</th><th scope=col>quality</th><th scope=col>label</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 7.4</td><td>0.70</td><td>0.00</td><td>1.9</td><td>0.076</td><td>11</td><td>34</td><td>0.9978</td><td>3.51</td><td>0.56</td><td>9.4</td><td>5</td><td>red</td></tr>\n",
       "\t<tr><th scope=row>2</th><td> 7.8</td><td>0.88</td><td>0.00</td><td>2.6</td><td>0.098</td><td>25</td><td>67</td><td>0.9968</td><td>3.20</td><td>0.68</td><td>9.8</td><td>5</td><td>red</td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 7.8</td><td>0.76</td><td>0.04</td><td>2.3</td><td>0.092</td><td>15</td><td>54</td><td>0.9970</td><td>3.26</td><td>0.65</td><td>9.8</td><td>5</td><td>red</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>11.2</td><td>0.28</td><td>0.56</td><td>1.9</td><td>0.075</td><td>17</td><td>60</td><td>0.9980</td><td>3.16</td><td>0.58</td><td>9.8</td><td>6</td><td>red</td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 7.4</td><td>0.70</td><td>0.00</td><td>1.9</td><td>0.076</td><td>11</td><td>34</td><td>0.9978</td><td>3.51</td><td>0.56</td><td>9.4</td><td>5</td><td>red</td></tr>\n",
       "\t<tr><th scope=row>6</th><td> 7.4</td><td>0.66</td><td>0.00</td><td>1.8</td><td>0.075</td><td>13</td><td>40</td><td>0.9978</td><td>3.51</td><td>0.56</td><td>9.4</td><td>5</td><td>red</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & fixed.acidity & volatile.acidity & citric.acid & residual.sugar & chlorides & free.sulfur.dioxide & total.sulfur.dioxide & density & pH & sulphates & alcohol & quality & label\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 &  7.4 & 0.70 & 0.00 & 1.9 & 0.076 & 11 & 34 & 0.9978 & 3.51 & 0.56 & 9.4 & 5 & red\\\\\n",
       "\t2 &  7.8 & 0.88 & 0.00 & 2.6 & 0.098 & 25 & 67 & 0.9968 & 3.20 & 0.68 & 9.8 & 5 & red\\\\\n",
       "\t3 &  7.8 & 0.76 & 0.04 & 2.3 & 0.092 & 15 & 54 & 0.9970 & 3.26 & 0.65 & 9.8 & 5 & red\\\\\n",
       "\t4 & 11.2 & 0.28 & 0.56 & 1.9 & 0.075 & 17 & 60 & 0.9980 & 3.16 & 0.58 & 9.8 & 6 & red\\\\\n",
       "\t5 &  7.4 & 0.70 & 0.00 & 1.9 & 0.076 & 11 & 34 & 0.9978 & 3.51 & 0.56 & 9.4 & 5 & red\\\\\n",
       "\t6 &  7.4 & 0.66 & 0.00 & 1.8 & 0.075 & 13 & 40 & 0.9978 & 3.51 & 0.56 & 9.4 & 5 & red\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | fixed.acidity &lt;dbl&gt; | volatile.acidity &lt;dbl&gt; | citric.acid &lt;dbl&gt; | residual.sugar &lt;dbl&gt; | chlorides &lt;dbl&gt; | free.sulfur.dioxide &lt;dbl&gt; | total.sulfur.dioxide &lt;dbl&gt; | density &lt;dbl&gt; | pH &lt;dbl&gt; | sulphates &lt;dbl&gt; | alcohol &lt;dbl&gt; | quality &lt;int&gt; | label &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 |  7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 | red |\n",
       "| 2 |  7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25 | 67 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 | red |\n",
       "| 3 |  7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 | red |\n",
       "| 4 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17 | 60 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 | red |\n",
       "| 5 |  7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 | red |\n",
       "| 6 |  7.4 | 0.66 | 0.00 | 1.8 | 0.075 | 13 | 40 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 | red |\n",
       "\n"
      ],
      "text/plain": [
       "  fixed.acidity volatile.acidity citric.acid residual.sugar chlorides\n",
       "1  7.4          0.70             0.00        1.9            0.076    \n",
       "2  7.8          0.88             0.00        2.6            0.098    \n",
       "3  7.8          0.76             0.04        2.3            0.092    \n",
       "4 11.2          0.28             0.56        1.9            0.075    \n",
       "5  7.4          0.70             0.00        1.9            0.076    \n",
       "6  7.4          0.66             0.00        1.8            0.075    \n",
       "  free.sulfur.dioxide total.sulfur.dioxide density pH   sulphates alcohol\n",
       "1 11                  34                   0.9978  3.51 0.56      9.4    \n",
       "2 25                  67                   0.9968  3.20 0.68      9.8    \n",
       "3 15                  54                   0.9970  3.26 0.65      9.8    \n",
       "4 17                  60                   0.9980  3.16 0.58      9.8    \n",
       "5 11                  34                   0.9978  3.51 0.56      9.4    \n",
       "6 13                  40                   0.9978  3.51 0.56      9.4    \n",
       "  quality label\n",
       "1 5       red  \n",
       "2 5       red  \n",
       "3 5       red  \n",
       "4 6       red  \n",
       "5 5       red  \n",
       "6 5       red  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>fixed.acidity</th><th scope=col>volatile.acidity</th><th scope=col>citric.acid</th><th scope=col>residual.sugar</th><th scope=col>chlorides</th><th scope=col>free.sulfur.dioxide</th><th scope=col>total.sulfur.dioxide</th><th scope=col>density</th><th scope=col>pH</th><th scope=col>sulphates</th><th scope=col>alcohol</th><th scope=col>quality</th><th scope=col>label</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>7.0</td><td>0.27</td><td>0.36</td><td>20.7</td><td>0.045</td><td>45</td><td>170</td><td>1.0010</td><td>3.00</td><td>0.45</td><td> 8.8</td><td>6</td><td>white</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>6.3</td><td>0.30</td><td>0.34</td><td> 1.6</td><td>0.049</td><td>14</td><td>132</td><td>0.9940</td><td>3.30</td><td>0.49</td><td> 9.5</td><td>6</td><td>white</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>8.1</td><td>0.28</td><td>0.40</td><td> 6.9</td><td>0.050</td><td>30</td><td> 97</td><td>0.9951</td><td>3.26</td><td>0.44</td><td>10.1</td><td>6</td><td>white</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>7.2</td><td>0.23</td><td>0.32</td><td> 8.5</td><td>0.058</td><td>47</td><td>186</td><td>0.9956</td><td>3.19</td><td>0.40</td><td> 9.9</td><td>6</td><td>white</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>7.2</td><td>0.23</td><td>0.32</td><td> 8.5</td><td>0.058</td><td>47</td><td>186</td><td>0.9956</td><td>3.19</td><td>0.40</td><td> 9.9</td><td>6</td><td>white</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>8.1</td><td>0.28</td><td>0.40</td><td> 6.9</td><td>0.050</td><td>30</td><td> 97</td><td>0.9951</td><td>3.26</td><td>0.44</td><td>10.1</td><td>6</td><td>white</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & fixed.acidity & volatile.acidity & citric.acid & residual.sugar & chlorides & free.sulfur.dioxide & total.sulfur.dioxide & density & pH & sulphates & alcohol & quality & label\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 7.0 & 0.27 & 0.36 & 20.7 & 0.045 & 45 & 170 & 1.0010 & 3.00 & 0.45 &  8.8 & 6 & white\\\\\n",
       "\t2 & 6.3 & 0.30 & 0.34 &  1.6 & 0.049 & 14 & 132 & 0.9940 & 3.30 & 0.49 &  9.5 & 6 & white\\\\\n",
       "\t3 & 8.1 & 0.28 & 0.40 &  6.9 & 0.050 & 30 &  97 & 0.9951 & 3.26 & 0.44 & 10.1 & 6 & white\\\\\n",
       "\t4 & 7.2 & 0.23 & 0.32 &  8.5 & 0.058 & 47 & 186 & 0.9956 & 3.19 & 0.40 &  9.9 & 6 & white\\\\\n",
       "\t5 & 7.2 & 0.23 & 0.32 &  8.5 & 0.058 & 47 & 186 & 0.9956 & 3.19 & 0.40 &  9.9 & 6 & white\\\\\n",
       "\t6 & 8.1 & 0.28 & 0.40 &  6.9 & 0.050 & 30 &  97 & 0.9951 & 3.26 & 0.44 & 10.1 & 6 & white\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | fixed.acidity &lt;dbl&gt; | volatile.acidity &lt;dbl&gt; | citric.acid &lt;dbl&gt; | residual.sugar &lt;dbl&gt; | chlorides &lt;dbl&gt; | free.sulfur.dioxide &lt;dbl&gt; | total.sulfur.dioxide &lt;dbl&gt; | density &lt;dbl&gt; | pH &lt;dbl&gt; | sulphates &lt;dbl&gt; | alcohol &lt;dbl&gt; | quality &lt;int&gt; | label &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 7.0 | 0.27 | 0.36 | 20.7 | 0.045 | 45 | 170 | 1.0010 | 3.00 | 0.45 |  8.8 | 6 | white |\n",
       "| 2 | 6.3 | 0.30 | 0.34 |  1.6 | 0.049 | 14 | 132 | 0.9940 | 3.30 | 0.49 |  9.5 | 6 | white |\n",
       "| 3 | 8.1 | 0.28 | 0.40 |  6.9 | 0.050 | 30 |  97 | 0.9951 | 3.26 | 0.44 | 10.1 | 6 | white |\n",
       "| 4 | 7.2 | 0.23 | 0.32 |  8.5 | 0.058 | 47 | 186 | 0.9956 | 3.19 | 0.40 |  9.9 | 6 | white |\n",
       "| 5 | 7.2 | 0.23 | 0.32 |  8.5 | 0.058 | 47 | 186 | 0.9956 | 3.19 | 0.40 |  9.9 | 6 | white |\n",
       "| 6 | 8.1 | 0.28 | 0.40 |  6.9 | 0.050 | 30 |  97 | 0.9951 | 3.26 | 0.44 | 10.1 | 6 | white |\n",
       "\n"
      ],
      "text/plain": [
       "  fixed.acidity volatile.acidity citric.acid residual.sugar chlorides\n",
       "1 7.0           0.27             0.36        20.7           0.045    \n",
       "2 6.3           0.30             0.34         1.6           0.049    \n",
       "3 8.1           0.28             0.40         6.9           0.050    \n",
       "4 7.2           0.23             0.32         8.5           0.058    \n",
       "5 7.2           0.23             0.32         8.5           0.058    \n",
       "6 8.1           0.28             0.40         6.9           0.050    \n",
       "  free.sulfur.dioxide total.sulfur.dioxide density pH   sulphates alcohol\n",
       "1 45                  170                  1.0010  3.00 0.45       8.8   \n",
       "2 14                  132                  0.9940  3.30 0.49       9.5   \n",
       "3 30                   97                  0.9951  3.26 0.44      10.1   \n",
       "4 47                  186                  0.9956  3.19 0.40       9.9   \n",
       "5 47                  186                  0.9956  3.19 0.40       9.9   \n",
       "6 30                   97                  0.9951  3.26 0.44      10.1   \n",
       "  quality label\n",
       "1 6       white\n",
       "2 6       white\n",
       "3 6       white\n",
       "4 6       white\n",
       "5 6       white\n",
       "6 6       white"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t1599 obs. of  13 variables:\n",
      " $ fixed.acidity       : num  7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...\n",
      " $ volatile.acidity    : num  0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...\n",
      " $ citric.acid         : num  0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...\n",
      " $ residual.sugar      : num  1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...\n",
      " $ chlorides           : num  0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...\n",
      " $ free.sulfur.dioxide : num  11 25 15 17 11 13 15 15 9 17 ...\n",
      " $ total.sulfur.dioxide: num  34 67 54 60 34 40 59 21 18 102 ...\n",
      " $ density             : num  0.998 0.997 0.997 0.998 0.998 ...\n",
      " $ pH                  : num  3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...\n",
      " $ sulphates           : num  0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...\n",
      " $ alcohol             : num  9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...\n",
      " $ quality             : int  5 5 5 6 5 5 5 7 7 5 ...\n",
      " $ label               : chr  \"red\" \"red\" \"red\" \"red\" ...\n",
      "'data.frame':\t4898 obs. of  13 variables:\n",
      " $ fixed.acidity       : num  7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ...\n",
      " $ volatile.acidity    : num  0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...\n",
      " $ citric.acid         : num  0.36 0.34 0.4 0.32 0.32 0.4 0.16 0.36 0.34 0.43 ...\n",
      " $ residual.sugar      : num  20.7 1.6 6.9 8.5 8.5 6.9 7 20.7 1.6 1.5 ...\n",
      " $ chlorides           : num  0.045 0.049 0.05 0.058 0.058 0.05 0.045 0.045 0.049 0.044 ...\n",
      " $ free.sulfur.dioxide : num  45 14 30 47 47 30 30 45 14 28 ...\n",
      " $ total.sulfur.dioxide: num  170 132 97 186 186 97 136 170 132 129 ...\n",
      " $ density             : num  1.001 0.994 0.995 0.996 0.996 ...\n",
      " $ pH                  : num  3 3.3 3.26 3.19 3.19 3.26 3.18 3 3.3 3.22 ...\n",
      " $ sulphates           : num  0.45 0.49 0.44 0.4 0.4 0.44 0.47 0.45 0.49 0.45 ...\n",
      " $ alcohol             : num  8.8 9.5 10.1 9.9 9.9 10.1 9.6 8.8 9.5 11 ...\n",
      " $ quality             : int  6 6 6 6 6 6 6 6 6 6 ...\n",
      " $ label               : chr  \"white\" \"white\" \"white\" \"white\" ...\n"
     ]
    }
   ],
   "source": [
    "head(df1)\n",
    "head(df2)\n",
    "str(df1)\n",
    "str(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Combine df1 and df2 int a single dataframe called wine.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t6497 obs. of  13 variables:\n",
      " $ fixed.acidity       : num  7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...\n",
      " $ volatile.acidity    : num  0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...\n",
      " $ citric.acid         : num  0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...\n",
      " $ residual.sugar      : num  1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...\n",
      " $ chlorides           : num  0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...\n",
      " $ free.sulfur.dioxide : num  11 25 15 17 11 13 15 15 9 17 ...\n",
      " $ total.sulfur.dioxide: num  34 67 54 60 34 40 59 21 18 102 ...\n",
      " $ density             : num  0.998 0.997 0.997 0.998 0.998 ...\n",
      " $ pH                  : num  3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...\n",
      " $ sulphates           : num  0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...\n",
      " $ alcohol             : num  9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...\n",
      " $ quality             : int  5 5 5 6 5 5 5 7 7 5 ...\n",
      " $ label               : Factor w/ 2 levels \"red\",\"white\": 1 1 1 1 1 1 1 1 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "wine  <- rbind(df1,df2)\n",
    "wine$label <- sapply(wine$label,factor)\n",
    "\n",
    "str(wine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "__Create a Histogram of residual sugar from the wine data. Color by red and white wines.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h8/v2cLIlUIpnAtkbbP01KTEKlCMIWL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\nYAX+D+saoypZS18AAAABSURBVNZHiAoJAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "\n",
    "ggplot(data = wine , aes(x = residual.sugar)) + \n",
    "geom_histogram(aes(fill = label), \n",
    "               position = \"dodge\", \n",
    "               bins = 40, \n",
    "               color = 'black') +\n",
    "scale_fill_manual(values = c('red','white'))+\n",
    "theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Create a histogram of citric.acid from the wine data.__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vmzyemrAimZ2YqQ2vdN/HQ6kJKZBakV\nSMnMrh3S5s0Pm93m5TuBv3zf1fCzVCDFs0MyWwHS5vjj4fLyf2+MXTN9VSAlM7t2SAc1Jz0g\ngbTS2bqQ4j+BBCmeHZLZApBeP7q79Ix09fRVgZTMLEitQEpmdvWQXl5++5siv0f6r9e2Zodk\ntiokn/5uvbY1OySz64d0SyDFs0MyC1IrkJKZBakVSMnMgtQKpGRmQWoFUjKzILUCKZnZmZB8\nE/3mKki1ZqdBGhhI8eyQzILUCqRkZkFqBVIysyC1AimZWZBagZTMLEitQEpmFqRWICUzC1Ir\nkJKZBakVSMnMgtQKpGRmQWoFUjKzILUCKZlZkFqBlMwsSK1ASmYWpFYgJTMLUiuQkpkFqRVI\nycyC1AqkZGZBagVSMrMgtQIpmVmQWoGUzCxIrUBKZhakViAlMwtSK5CSmQWpFUjJzILUCqRk\nZkFqBVIysyC1AimZWZBagZTMLEitQEpmFqRWICUzC1IrkJKZBakVSMnMgtQKpGRmQWoFUjKz\nILUCKZlZkFqBlMwsSK1ASmYWpFYgJTMLUiuQkpkFqRVIycyC1AqkZGZBagVSMrMgtQIpmVmQ\nWoGUzCxIrUBKZhakViAlMwtSK5CSmQWp1ZefE3s10rq5+Xnxpj5Y1SFl3sgzktlxqx/kGSnz\nRiCZHbcKUmcgVZsFKQ6kZGZBagVSMrMgtQIpmVmQWoGUzCxIrUBKZhakViAlMwtSK5CSmQWp\nFUjJzILUCqRkZkFqBVIysyC1AimZWZBagZTMLEitQEpmFqRWICUzC1IrkJKZBakVSMnMgtQK\npGRmQWoFUjKzILUCKZlZkFqBlMwsSK1ASmYWpFYgJTMLUiuQkpkFqRVIycyC1AqkZGZBagVS\nMrMgtQIpmVmQWoGUzCxIrUBKZhakViAlMwtSK5CSmQWpFUjJzILU6p0hbV46rIJUaxakuPeG\n9OYCUrVZkOJASmYWpFYgJTMLUiuQkpkFqRVIycyC1AqkZGZBavUxIb39LPudKnXiQUoHUtTP\nK972ikqdeJDSgRQF0rBZkOJASlbqxIOUDqQokIbNghQHUrJSJx6kdCBFgTRsFqQ4kJKVOvEg\npQMpCqRhsyDFgZSs1IkHKR1IUSANmwUpDqRkpU48SOlAigJp2CxIcSAlK3XiQUoHUhRIw2ZB\nigMpWakTD1I6kKJAGjYLUhxIyUqdeJDSgRQF0rBZkOJASlbqxIOUDqQokIbNghQHUrJSJx6k\ndCBFgTRsFqQ4kJKVOvEgpQMpCqRhsyDFVYW0Cb8HJEjDZkGKKwtp+do3gTRsFqQ4kJKVOvEg\npQNpeT0F0rBZkOJASlbqxIOUDqTl9RRIw2ZBigMpWakTD1I6kJbXUyANmwUpDqRkpU48SOlA\nWl5PgTRsFqQ4kJKVOvEgpQNpeT0F0rBZkOJASlbqxIOUDqTl9RRIw2ZBigMpWakTD1K6/BnZ\n/u543b65fQikZKVOPEjp0mdke/xhe3b7EEjJSp14kNKBtLyeAmnY7KeGtG97sgPSbZU68SCl\nux7S8bdIu+MPX/b9fNeOJ3xxq31zk7lr8z+jClWA9BaQZ6TbKvXU4Rkp3dWQji+AdFulTjxI\n6a45I9u3L4F0W6VOPEjprjgj29OPIN1cqRMPUror/kD2dFl+smEfSMlKnXiQ0uX/HOn8Kxp8\nZcNNlTrxIKW71xkBKVmpEw9SOpCW11MgDZsFKQ6kZKVOPEjpQFpeT4E0bBakOJCSlTrxIKUD\naXk9BdKwWZDiQEpW6sSDlA6k5fUUSMNmQYoDKVmpEw9SOpCW11MgDZsFKQ6kZKVOPEjpQFpe\nT4E0bBakOJCSlTrxIKUDaXk9BdKwWZDiQEpW6sSDlA6k5fUUSMNmQYoDKVmpEw9SOpCW11Mg\nDZsFKa758DfH7vQfA6n2LEhxbUj/dwikYicepHQgLa+nQBo2C1IcSMlKnXiQ0oG0vJ4Cadgs\nSHEgJSt14kFKB9LyegqkYbMgxYGUrNSJBykdSMvrKZCGzYIUB1KyUicepHQgLa+nQBo2C1Ic\nSMlKnXiQ0oG0vJ4CadgsSHEgJSt14kFKB9LyegqkYbMgxYGUrNSJBykdSMvrKZCGzYIUB1Ky\nUicepHQgLa+nQBo2C1IcSMlKnXiQ0oG0vJ4CadgsSHEgJSt14kFKB9LyegqkYbMgxYGUrNSJ\nByldFUhn3xkPpMqzIMUNh7S4gFR6FqQ4kJKVOvEgpQNpeT0F0rBZkOI+CKQ336UcpGGzIMV9\nFEinmyANmwUpDqRkpU48SOlAim6CNGwWpDiQkpU68SClAym6CdKwWZDiQEpW6sSDlA6k6CZI\nw2ZBigMpWakTD1I6kKKbIA2bBSkOpGSlTjxI6UCKboI0bBakOJCSlTrxIKUDKboJ0rBZkOJA\nSlbqxIOUDqToJkjDZkGKAylZqRMPUjqQopsgDZsFKQ6kZKVOPEjpQIpugjRsFqQ4kJKVOvEg\npQMpugnSsFmQ4kBKVurEg5QOpOgmSMNmQYoDKVmpEw9SOpCimyANmwUpDqRkpU48SOmKQ4r/\nsZc/boK0mlmQ4iZBWu6CVGMWpDiQkpU68SClA+nyzcXHjCBVWAUp03tDWgyDVGEVpEwgfaBZ\nkOJASlbqxIOU7m6QfjZ6hdR6o3bHI3128wzS4ifPbp7ftfW255Buf9x676pDav5HPCO9Vuqp\nwzNSOpAu3wRp4CxIcSAlK3XiQUq3AkhnX+cTTCwuIJWeBSmuB1Lm6QqkDzQLUhxIyUqdeJDS\ngXT5JkgDZ0GKWyWki18wB9L0WZDiVgnpogaQps+CFAdSslInHqR01SCdfbgGUsVZkOLeC9Jy\nCaSSsyDFgZSs1IkHKR1IV9z1HpU68SClA+mKu96jUicepHQzIJ1/Ey2QPtksSHFXQTo7/iB9\nslmQ4tYBqfVUB9JqZkGKWwek1k2QVjMLUhxIyUqdeJDSgXT5JkgDZ0GKAylZqRMPUjqQLt8E\naeAsSHEgJSt14kFK94kgXfx7fiBNmAUprgCks7suLiC95yxIcaMgXf4LSCCVngUpbhiki3cF\nqfQsSHEgJSt14kFK93khnX1dXubL9O5RqRMPUrr1Qcr8pugekK6563W/Fs1KnXiQ0q0QUuL4\ng1R6FqQ4kJKVOvEgpQMpc9frfi2alTrxIKUDKXPX634tmpU68SClAylz1+t+LZqVOvEgpQMp\nc9frfi2alTrxIKUDKXPX634tmpU68SClAylz1+t+LZqVOvEgpQMpc9frfi2alTrxIKUDKXPX\n634tmpU68SClAylz1+NrM//++n9U6sSDlG69kF5aFaTlzZsqdeJBSrdeSK27glR6FqQ4kJKV\nOvEgpQMpc9fd4nrLr9nrb69KnXiQ0oGUuetucb0J0vFS6sSDlA6kzF13iytIq1wFCaT+Ss2C\nFAdSO5CGr4IEUn+lZkGKA6kdSMNXQQKpv1KzIMWB1A6k4asggdRfqVmQ4kBqB9LwVZBA6q/U\nLEhxd4C0/IsTq4J08d8ou+qvJ4E0fBWk9T8jnd11t7iCtI5VkEDqr9QsSHEgtQNp+CpIIPVX\nahakOJDagTR8FSSQ+is1C1JcDtKlT3B/FEibs85+CY6XUicepHSekW64625xPX/tcun8bUud\neJDSgXTDXXeLawDp7F93Pl5KnXiQ0oF0w10vfqFDcNfd2RuVOvEgpQOp/0GcPfeANGf180LK\nfO6hAqTmXXeLK0jjVj8vpF3iJki5Ss2CFAcSSJNXQQKpv1KzIMWBBNLkVZBA6q/ULEhxIIE0\neRUkkPorNQtSHEggTV4FCaT+Ss2CFAcSSJNXQfrQkM6/wLXUiQcpHUhjH8T52148RMFfBrwm\nkEAC6ezjv6vqN9gKpGy3vwO2vzvdAil314vnvgvS7XdNBFK2m98B29cf9oF0y12Pdzm7XhNI\nZ7MgXbz5USFd/mbil3/1mh+8gXQ2C9LFmx8V0svNy9/YoflX2XetN7pfd/gESNitkP7jIVWH\n9GXfz0bn36lK6ql11n7WhbQv9fALfqBgtsxq+WekfSCZnbwKUmelzpDZYasgdVbqDJkdtgpS\nZ6XOkNlhq0UhXfOVDccKvlvMllmtCmkZSGYnr4LUWakzZHbYKkidlTpDZoetgtRZqTNkdtgq\nSJ2VOkNmh62C1FmpM2R22CpInZU6Q2aHrYLUWakzZHbYKkidlTpDZoetgtRZqTNkdtgqSJ2V\nOkNmh62C1FmpM2R22CpInZU6Q2aHrYLUWakzZHbYKkidlTpDZoetgtRZqTNkdtgqSJ2VOkNm\nh62C1FmpM2R22CpInZU6Q2aHrYLUWakzZHbYKkidlTpDZoetgtRZqTNkdtgqSJ2VOkNmh62C\n1FmpM2R22CpInZU6Q2aHrYLUWakzZHbYKkidlTpDZoetgtRZqTNkdtjqB4EkraM7negrG/Sv\nYV9u0v/G2yr1YGs92lIPNhlIUaUebK1HW+rBJgMpqtSDrfVoSz3YZO8KSfqogSTdIZCkOwSS\ndIdAku4QSNIdehdI299denmVnT/YdT/a34/x9FK1B7v2R3tN7wFp+/rD8uVVtniAa36gL71R\n//rDantDZ90P9PpAOqsYpO2uEKTtDqSeykJa8+N8rRCk8wf7kQLprMWDrfBxfFFIFX5prwmk\ns0o92OeKQlrc+gCBdNYfD3DND/a5Umdz27hVO5DOAmlkIPVUFtLqH+xzNSFVeLBXBdJZ5w92\nzY91X6mzuXiwK3+s1/WuX9mwffPyaiv1YHfH41jk0ZZ6sFfla+2kOwSSdIdAku4QSNIdAkm6\nQyBJdwgk6Q6BJN0hkKQ7BNI7t/n9K/73dnEzdZ8r3lwT8o55554pvOEA0gfJO2ZCt3MAaa15\nx7xTT98228fdnsJmc3ha+nf7cJBx/LlDP75tXm6+uc/vGw+bbyCtNu+Y9+nX9pnPtwWkh833\nvYzXn9v3z2bf4/I+hxvfQFpr3jHv0+NvND+Ovz/ac9hb2b/4+nP7vm7+t9v9u3j94cbD7tcD\nSGvNO+Z9+rr5dXjhDaSnl5uvP/fS0z9/PSxef7jx+82fQFpr3jHv06uAN5AWN089HD62u3Qf\nv0dabd4x71Me0vfN17//eQKpWN4x79OlD+2ON5cf2u1f/8uHdsXyjnmfHjePh88gXID0+nO7\nw2t+vHxWYXGfvzYPv3Y+2bDavGPep6f9p7K/HiFt30J6/bkXVsffIy3u49Pf68475p369/ez\nyfenl6+1W0J6/bnDre+bzcOPxeuPf2rrD2TXm3eMdIdAku4QSNIdAkm6QyBJdwgk6Q6BJN0h\nkKQ7BJJ0h0CS7hBI0h0CSbpD/w8d5cQupJkcgwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = wine , aes(x = citric.acid)) + \n",
    "geom_histogram(aes(fill = label), \n",
    "               position = \"dodge\", \n",
    "               bins = 40, \n",
    "               color = 'black') +\n",
    "scale_fill_manual(values = c('red','white'))+\n",
    "theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Create a histogram of alcohol from wine data. Coor by red and white wines.__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0i/PrprPSKVp1OBBNJYmyBFgRROdmlj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\nHQJJ6hBIUodAkjoEktQhkKQO/T+qU4a6OpGCcAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ggplot(data = wine , aes(x = alcohol)) + \n",
    "geom_histogram(aes(fill = label), \n",
    "               position = \"dodge\", \n",
    "               bins = 30, \n",
    "               color = 'black') +\n",
    "scale_fill_manual(values = c('red','white'))+\n",
    "theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Create a scatterplot of residual.sugar versus citric.acid.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0y1sRYDB3CLKNOq2L1791pf3nPPPaX7LOv/Nd9qpqenx8fH5bi/v/+ee+657bbb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BKQYmoaBIMgyyujCwUAIBkGGAY4Dt+C45CW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\nCAQ7AAAAgBKBYAcAAABQIv4fNslEKHUNRmsAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(wine,aes(x=citric.acid,y=residual.sugar)) + \n",
    "geom_point(aes(color=label),alpha=0.2) + \n",
    "scale_color_manual(values = c('red','white')) +\n",
    "theme_dark()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Create a scatterplot of volatile.acidity verses residual.sugar__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I+fsYZK8ZAXgVQRQ+0bSCDlqoguQRFIJfkZa6gUD3npDhKtkUryM9ZQKR7y0h4k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snoHA50Equq8MS4KHljWIhtKwIwcgZVmK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\nBxFIJBIHEUgkEgcRSCQSBxFIJBIH/R/HHgq527rFngAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = wine , aes(x = volatile.acidity, y = residual.sugar)) + \n",
    "geom_point(aes(color = label), alpha= 0.3) +\n",
    "scale_color_manual(values = c('red','white')) +\n",
    "theme_dark()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the Model\n",
    "__Grab the wine data without the label and call it clus.data__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "clus.data <- select(wine, -label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Check the head of clus.data.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>fixed.acidity</th><th scope=col>volatile.acidity</th><th scope=col>citric.acid</th><th scope=col>residual.sugar</th><th scope=col>chlorides</th><th scope=col>free.sulfur.dioxide</th><th scope=col>total.sulfur.dioxide</th><th scope=col>density</th><th scope=col>pH</th><th scope=col>sulphates</th><th scope=col>alcohol</th><th scope=col>quality</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 7.4</td><td>0.70</td><td>0.00</td><td>1.9</td><td>0.076</td><td>11</td><td>34</td><td>0.9978</td><td>3.51</td><td>0.56</td><td>9.4</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>2</th><td> 7.8</td><td>0.88</td><td>0.00</td><td>2.6</td><td>0.098</td><td>25</td><td>67</td><td>0.9968</td><td>3.20</td><td>0.68</td><td>9.8</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 7.8</td><td>0.76</td><td>0.04</td><td>2.3</td><td>0.092</td><td>15</td><td>54</td><td>0.9970</td><td>3.26</td><td>0.65</td><td>9.8</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>11.2</td><td>0.28</td><td>0.56</td><td>1.9</td><td>0.075</td><td>17</td><td>60</td><td>0.9980</td><td>3.16</td><td>0.58</td><td>9.8</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 7.4</td><td>0.70</td><td>0.00</td><td>1.9</td><td>0.076</td><td>11</td><td>34</td><td>0.9978</td><td>3.51</td><td>0.56</td><td>9.4</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>6</th><td> 7.4</td><td>0.66</td><td>0.00</td><td>1.8</td><td>0.075</td><td>13</td><td>40</td><td>0.9978</td><td>3.51</td><td>0.56</td><td>9.4</td><td>5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 12\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & fixed.acidity & volatile.acidity & citric.acid & residual.sugar & chlorides & free.sulfur.dioxide & total.sulfur.dioxide & density & pH & sulphates & alcohol & quality\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t1 &  7.4 & 0.70 & 0.00 & 1.9 & 0.076 & 11 & 34 & 0.9978 & 3.51 & 0.56 & 9.4 & 5\\\\\n",
       "\t2 &  7.8 & 0.88 & 0.00 & 2.6 & 0.098 & 25 & 67 & 0.9968 & 3.20 & 0.68 & 9.8 & 5\\\\\n",
       "\t3 &  7.8 & 0.76 & 0.04 & 2.3 & 0.092 & 15 & 54 & 0.9970 & 3.26 & 0.65 & 9.8 & 5\\\\\n",
       "\t4 & 11.2 & 0.28 & 0.56 & 1.9 & 0.075 & 17 & 60 & 0.9980 & 3.16 & 0.58 & 9.8 & 6\\\\\n",
       "\t5 &  7.4 & 0.70 & 0.00 & 1.9 & 0.076 & 11 & 34 & 0.9978 & 3.51 & 0.56 & 9.4 & 5\\\\\n",
       "\t6 &  7.4 & 0.66 & 0.00 & 1.8 & 0.075 & 13 & 40 & 0.9978 & 3.51 & 0.56 & 9.4 & 5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 12\n",
       "\n",
       "| <!--/--> | fixed.acidity &lt;dbl&gt; | volatile.acidity &lt;dbl&gt; | citric.acid &lt;dbl&gt; | residual.sugar &lt;dbl&gt; | chlorides &lt;dbl&gt; | free.sulfur.dioxide &lt;dbl&gt; | total.sulfur.dioxide &lt;dbl&gt; | density &lt;dbl&gt; | pH &lt;dbl&gt; | sulphates &lt;dbl&gt; | alcohol &lt;dbl&gt; | quality &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 |  7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |\n",
       "| 2 |  7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25 | 67 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 |\n",
       "| 3 |  7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 |\n",
       "| 4 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17 | 60 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 |\n",
       "| 5 |  7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |\n",
       "| 6 |  7.4 | 0.66 | 0.00 | 1.8 | 0.075 | 13 | 40 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |\n",
       "\n"
      ],
      "text/plain": [
       "  fixed.acidity volatile.acidity citric.acid residual.sugar chlorides\n",
       "1  7.4          0.70             0.00        1.9            0.076    \n",
       "2  7.8          0.88             0.00        2.6            0.098    \n",
       "3  7.8          0.76             0.04        2.3            0.092    \n",
       "4 11.2          0.28             0.56        1.9            0.075    \n",
       "5  7.4          0.70             0.00        1.9            0.076    \n",
       "6  7.4          0.66             0.00        1.8            0.075    \n",
       "  free.sulfur.dioxide total.sulfur.dioxide density pH   sulphates alcohol\n",
       "1 11                  34                   0.9978  3.51 0.56      9.4    \n",
       "2 25                  67                   0.9968  3.20 0.68      9.8    \n",
       "3 15                  54                   0.9970  3.26 0.65      9.8    \n",
       "4 17                  60                   0.9980  3.16 0.58      9.8    \n",
       "5 11                  34                   0.9978  3.51 0.56      9.4    \n",
       "6 13                  40                   0.9978  3.51 0.56      9.4    \n",
       "  quality\n",
       "1 5      \n",
       "2 5      \n",
       "3 5      \n",
       "4 6      \n",
       "5 5      \n",
       "6 5      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(clus.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Call the kmeans function on clus.data and assign the results to wine.cluster__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine.cluster <- kmeans(clus.data, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Print out the wine.cluster Cluster Means and explore the information__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  fixed.acidity volatile.acidity citric.acid residual.sugar chlorides\n",
      "1      7.619044        0.4079451   0.2911080       3.082690 0.0656846\n",
      "2      6.904698        0.2871364   0.3398094       7.259286 0.0486092\n",
      "  free.sulfur.dioxide total.sulfur.dioxide   density       pH sulphates\n",
      "1            18.43735             63.54832 0.9945680 3.255147 0.5718655\n",
      "2            39.82503            155.90101 0.9947956 3.190308 0.5000354\n",
      "   alcohol  quality\n",
      "1 10.79529 5.809204\n",
      "2 10.25832 5.825436\n"
     ]
    }
   ],
   "source": [
    "print(wine.cluster$centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Clusters\n",
    "Again, you normally won't have the luxury of having labelled data when using k means, but let's go ahead and see how we did!\n",
    "\n",
    "__Use the table() function to compare your cluster results to the real results. Which is easier to correctly group? Red, white or both wines?__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   \n",
       "     red white\n",
       "  1 1515  1310\n",
       "  2   84  3588"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(wine.cluster$cluster, wine$label)"
   ]
  }
 ],
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