P29. Unsupervised ML - Hierarchical Clustering (Univ.).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised ML - Hierarchical Clustering (Univ.)"
   ]
  },
  {
   "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 scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Univ</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown</td>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CalTech</td>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMU</td>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Columbia</td>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cornell</td>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dartmouth</td>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duke</td>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harvard</td>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JohnsHopkins</td>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MIT</td>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwestern</td>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NotreDame</td>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PennState</td>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Princeton</td>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Purdue</td>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stanford</td>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TexasA&amp;M</td>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UCBerkeley</td>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UChicago</td>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UMichigan</td>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>UPenn</td>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UVA</td>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UWisconsin</td>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yale</td>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "            Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "0          Brown  1310     89      22       13     22704        94\n",
       "1        CalTech  1415    100      25        6     63575        81\n",
       "2            CMU  1260     62      59        9     25026        72\n",
       "3       Columbia  1310     76      24       12     31510        88\n",
       "4        Cornell  1280     83      33       13     21864        90\n",
       "5      Dartmouth  1340     89      23       10     32162        95\n",
       "6           Duke  1315     90      30       12     31585        95\n",
       "7     Georgetown  1255     74      24       12     20126        92\n",
       "8        Harvard  1400     91      14       11     39525        97\n",
       "9   JohnsHopkins  1305     75      44        7     58691        87\n",
       "10           MIT  1380     94      30       10     34870        91\n",
       "11  Northwestern  1260     85      39       11     28052        89\n",
       "12     NotreDame  1255     81      42       13     15122        94\n",
       "13     PennState  1081     38      54       18     10185        80\n",
       "14     Princeton  1375     91      14        8     30220        95\n",
       "15        Purdue  1005     28      90       19      9066        69\n",
       "16      Stanford  1360     90      20       12     36450        93\n",
       "17      TexasA&M  1075     49      67       25      8704        67\n",
       "18    UCBerkeley  1240     95      40       17     15140        78\n",
       "19      UChicago  1290     75      50       13     38380        87\n",
       "20     UMichigan  1180     65      68       16     15470        85\n",
       "21         UPenn  1285     80      36       11     27553        90\n",
       "22           UVA  1225     77      44       14     13349        92\n",
       "23    UWisconsin  1085     40      69       15     11857        71\n",
       "24          Yale  1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import dataset\n",
    "univ=pd.read_csv('Database/Universities.csv')\n",
    "univ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "0   1310     89      22       13     22704        94\n",
       "1   1415    100      25        6     63575        81\n",
       "2   1260     62      59        9     25026        72\n",
       "3   1310     76      24       12     31510        88\n",
       "4   1280     83      33       13     21864        90\n",
       "5   1340     89      23       10     32162        95\n",
       "6   1315     90      30       12     31585        95\n",
       "7   1255     74      24       12     20126        92\n",
       "8   1400     91      14       11     39525        97\n",
       "9   1305     75      44        7     58691        87\n",
       "10  1380     94      30       10     34870        91\n",
       "11  1260     85      39       11     28052        89\n",
       "12  1255     81      42       13     15122        94\n",
       "13  1081     38      54       18     10185        80\n",
       "14  1375     91      14        8     30220        95\n",
       "15  1005     28      90       19      9066        69\n",
       "16  1360     90      20       12     36450        93\n",
       "17  1075     49      67       25      8704        67\n",
       "18  1240     95      40       17     15140        78\n",
       "19  1290     75      50       13     38380        87\n",
       "20  1180     65      68       16     15470        85\n",
       "21  1285     80      36       11     27553        90\n",
       "22  1225     77      44       14     13349        92\n",
       "23  1085     40      69       15     11857        71\n",
       "24  1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ2=univ.iloc[:,1:]\n",
    "univ2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize heterogenous numerical data using z-score (x-mean/std) or custom defined function \n",
    "# Normalization function - here custom defined\n",
    "def norm_func(i):\n",
    "    x = (i-i.min())/(i.max()-i.min())\n",
    "    return (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalized data frame (considering the numerical part of data)\n",
    "univ2_norm = norm_func(univ2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# Create dendrograms\n",
    "dendrogram=sch.dendrogram(sch.linkage(univ2_norm,method='average'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Clusters\n",
    "hc=AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    clusters\n",
       "0          3\n",
       "1          0\n",
       "2          2\n",
       "3          3\n",
       "4          3\n",
       "5          4\n",
       "6          4\n",
       "7          3\n",
       "8          4\n",
       "9          0\n",
       "10         4\n",
       "11         3\n",
       "12         3\n",
       "13         1\n",
       "14         4\n",
       "15         1\n",
       "16         4\n",
       "17         1\n",
       "18         2\n",
       "19         0\n",
       "20         2\n",
       "21         3\n",
       "22         3\n",
       "23         1\n",
       "24         4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.DataFrame(hc.fit_predict(univ2_norm),columns=['clusters'])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Univ</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "      <th>clusterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown</td>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CalTech</td>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMU</td>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Columbia</td>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cornell</td>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dartmouth</td>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duke</td>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harvard</td>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JohnsHopkins</td>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MIT</td>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwestern</td>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NotreDame</td>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PennState</td>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Princeton</td>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Purdue</td>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stanford</td>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TexasA&amp;M</td>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UCBerkeley</td>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UChicago</td>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UMichigan</td>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>UPenn</td>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UVA</td>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UWisconsin</td>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yale</td>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate  clusterid\n",
       "0          Brown  1310     89      22       13     22704        94          3\n",
       "1        CalTech  1415    100      25        6     63575        81          0\n",
       "2            CMU  1260     62      59        9     25026        72          2\n",
       "3       Columbia  1310     76      24       12     31510        88          3\n",
       "4        Cornell  1280     83      33       13     21864        90          3\n",
       "5      Dartmouth  1340     89      23       10     32162        95          4\n",
       "6           Duke  1315     90      30       12     31585        95          4\n",
       "7     Georgetown  1255     74      24       12     20126        92          3\n",
       "8        Harvard  1400     91      14       11     39525        97          4\n",
       "9   JohnsHopkins  1305     75      44        7     58691        87          0\n",
       "10           MIT  1380     94      30       10     34870        91          4\n",
       "11  Northwestern  1260     85      39       11     28052        89          3\n",
       "12     NotreDame  1255     81      42       13     15122        94          3\n",
       "13     PennState  1081     38      54       18     10185        80          1\n",
       "14     Princeton  1375     91      14        8     30220        95          4\n",
       "15        Purdue  1005     28      90       19      9066        69          1\n",
       "16      Stanford  1360     90      20       12     36450        93          4\n",
       "17      TexasA&M  1075     49      67       25      8704        67          1\n",
       "18    UCBerkeley  1240     95      40       17     15140        78          2\n",
       "19      UChicago  1290     75      50       13     38380        87          0\n",
       "20     UMichigan  1180     65      68       16     15470        85          2\n",
       "21         UPenn  1285     80      36       11     27553        90          3\n",
       "22           UVA  1225     77      44       14     13349        92          3\n",
       "23    UWisconsin  1085     40      69       15     11857        71          1\n",
       "24          Yale  1375     95      19       11     43514        96          4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "univ['clusterid']=hc.labels_\n",
    "univ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusterid</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1336.666667</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>39.666667</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>53548.666667</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1061.500000</td>\n",
       "      <td>38.750000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>9953.000000</td>\n",
       "      <td>71.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1226.666667</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18545.333333</td>\n",
       "      <td>78.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1272.500000</td>\n",
       "      <td>80.625000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>12.375000</td>\n",
       "      <td>22535.000000</td>\n",
       "      <td>91.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1363.571429</td>\n",
       "      <td>91.428571</td>\n",
       "      <td>21.428571</td>\n",
       "      <td>10.571429</td>\n",
       "      <td>35475.142857</td>\n",
       "      <td>94.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusterid          SAT      Top10     Accept    SFRatio      Expenses  \\\n",
       "                    mean       mean       mean       mean          mean   \n",
       "0         0  1336.666667  83.333333  39.666667   8.666667  53548.666667   \n",
       "1         1  1061.500000  38.750000  70.000000  19.250000   9953.000000   \n",
       "2         2  1226.666667  74.000000  55.666667  14.000000  18545.333333   \n",
       "3         3  1272.500000  80.625000  33.000000  12.375000  22535.000000   \n",
       "4         4  1363.571429  91.428571  21.428571  10.571429  35475.142857   \n",
       "\n",
       "    GradRate  \n",
       "        mean  \n",
       "0  85.000000  \n",
       "1  71.750000  \n",
       "2  78.333333  \n",
       "3  91.125000  \n",
       "4  94.571429  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data by Clusters (K=5)\n",
    "univ.groupby('clusterid').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20e3d5482b0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gMAIXAABAYAQuAACAwAhcAAAAgRG4AAAAAiNwAQAABEbgApAqpUpFm3bt0u5CIe5WUGPFSlHbertUKLP2qD3uNA8gNX6x5il99de/VKFcVtkretf04/Std56l5rq6uFtDQO6unz9/h5a+cI9MJpfrnUe9Qwvbz+PDq1Ez/KQBSIVHOjfp8gfv085Cr3pKfSqWy7r/T8/qc/fcHXdrCOzerfdr6Qv3qFgpqlApqFgp6v6tD+quLUvjbg0pQuACkArXdixXT6m0X61QLuvXz63X9r17Y+oKtXDXlqUqVor71YqVopZu+feYOkIaEbgApMLzu3dXrddls+ra83KNu0Etdfd1V63vKe9VxSs17gZpReACkAqnTW5X1uyAernimjp2XAwdoVbam9ur1ic2Hs05XKgZftIApMIlp71FzXV1yuiV0NWUy+nTb5mnJk6aT7RjmqoHrkmNk2rcCdKMqxQBpEL76DFasuh8fX/577S8c5NaW1p00Zy5OnvGCXG3hsAe2bG8av2xnY/XuBOkGYELQGocO3asvnfm2XG3gRoreblqvaKKSpWSchl+FSI8DikCABItc5BfdSYjbKFmCFwAgET7DxPmVa3nx82ucSdIMwIXACDRPj7tozptXH6/2qljZumTx10UU0dII/alAgASLZPJ6FMzLlapUtKuvl0aUzeGQ4moOX7iAACpkMvkNKFhQtxtIKU4pAgAABAYgQsAACAwAhcAAEBgBC4AqbKrt1fLNm3Uuh0vxt0KgBThpHkAqXHV8mW6tmO56rNZ9VUqmjF+gq4/5wNqbW6OuzUACcceLgCpcO+zz+iHKx5VoVxWd7Go3lJJT2/v0ifvXhJ3awBSgMAFIBV+9NgK9ZRK+9VKlYpWbn1BW7q7Y+oKQFoQuACkwks9PVXruUxWOwu9Ne4GQNoQuACkwjumTldd5sB/8rJmOn7c+Bg6ApAmBC4AqbB4zmka39SkhmxWkmSSGnM5feVvzlBdVAOAULhKEUAqTGhu1tIPX6ibnnhcD23coEmjjtDH/nKOTj16YtytAUgBc/e4e3hV+XzeOzo64m4DAABgSGa2wt3zg+scUgQAAAiMwAUAABAYgQsAACAwAhcAAEBgXKUIAEi8PaU9+k3Xw9qwd4OmNE/R21vfplF1o+JuCylC4AIAJFpXoUtfXv11FStFFStFdez4g+7cfLeunHm5jm46Ou72kBIcUgQAJNpPnrtFe0p7VKwUJUl93qeeco9u3HBzzJ0hTQhcAIBEW7XrKbn2v+eky7Wme60O93tRIjkIXACARMtZ9bNnssZHOqF2CFwAgER7a+vpB4SunOU0b8JcmVlMXSFtCFwAgET70JSFmtYyTQ2ZejVkGtSQadCU5nZ95Nj/FHdrSBGuUgQAJFpDtkH/Y+YXtP7lDXq+53lNbJqo6S3T2LuFmiJwAQBSYdqoqZo2amrcbSClOKQIAAAQGIELAAAgMAIXAABAYAQuAACAwAhcAAAAgRG4AAAAAiNwAQAABEbgAgAACIzABQAAEBiBCwAAIDACFwAAQGAELgAAgMAIXAAAAIENGbjM7AYz22ZmqwbUvm1ma8zsSTP7uZmNHTB2mZmtM7O1ZnbmgPocM1sZjV1lZjby0wEAADj8DGcP142SzhpUu0/Sye4+S9IfJV0mSWY2U9IiSSdFr7nGzLLRa66VtFjSjOhr8HsCAAAk0pCBy90fkrRjUO1edy9FTx+R1B49XiDpNncvuPt6SeskzTWziZJGu/syd3dJN0k6d6QmAQAAcDgbiXO4PiZpafR4sqRNA8Y6o9rk6PHgelVmttjMOsyso6urawRaBAAAiM/rClxmdrmkkqSf7itV2cxfpV6Vu1/n7nl3z7e1tb2eFgEAAGKXO9QXmtmFkt4n6YzoMKHUv+dqyoDN2iVtjurtVeoAAACJd0h7uMzsLEmXSjrH3fcOGFoiaZGZNZjZNPWfHP+ou2+R1G1m86KrEy+QdMfr7B0AAOANYcg9XGZ2q6T5klrNrFPSleq/KrFB0n3R3R0ecfeL3H21md0u6Sn1H2q8xN3L0VtdrP4rHpvUf87XUgEAAKSAvXI08PCUz+e9o6Mj7jYAAACGZGYr3D0/uM6d5gEAAAIjcAEAAARG4AIAAAiMwAUAABAYgQsAACAwAhcAAEBgBC4AAIDADvmjfQDgjaanr08/X/OUfrNxgyaNGq0PzzpF08eNj7stAClA4AKQCrsLBZ37s59o68t71FPqU85Mt65+Uv/rPX+rd0ybHnd7ABKOQ4oAUuH6xzq0ubtbPaU+SVLJXb2lkv7+vn9XuVKJuTsASUfgApAKS9f9UcVy+YB6sVzSupd2xNARgDQhcAFIhZa6+qr1UsXVUldX424ApA2BC0AqXHjKbDXl9g9WWTMdP3682kePiakrAGlB4AKQCgtOfJMWzjxJDdmsRtXVq6WuTpOPGK1/fu+CuFsDkAJcpQggFcxMX55/hj4x5zQ9vmWL2lpalJ80WRmzuFsDkAIELgCpMvmI0Zp8xOi42wCQMhxSBAAACIzABQAAEBiBCwAAIDACFwAAQGAELgAAgMAIXAAAAIERuAAAAAIjcAEAAARG4AIAAAiMwAUAABAYgQsAACAwAhcAAEBgBC4AAIDACFwAAACBEbgAAAACI3ABAAAERuACAAAIjMAFAAAQGIELAAAgMAIXAABAYAQuAACAwAhcAAAAgRG4AAAAAiNwAQAABEbgAgAACIzABQAAEBiBCwAAIDACFwAAQGAELgAAgMAIXAAAAIERuAAAAAIjcAEAAARG4AIAAAiMwAUAABAYgQsAACAwAhcAAEBgBC4AAIDACFwAAACBEbgAAAACI3ABAAAERuACAAAIjMAFAAAQGIELAAAgMAIXAABAYAQuAACAwAhcAAAAgRG4AAAAAiNwAQAABEbgAgAACIzABQAAEBiBCwAAIDACFwAAQGAELgAAgMAIXAAAAIERuAAAAAIjcAEAAARG4AIAAAiMwAUAABAYgQsAACCwIQOXmd1gZtvMbNWA2kIzW21mFTPLD9r+MjNbZ2ZrzezMAfU5ZrYyGrvKzGxkpwIAAHB4Gs4erhslnTWotkrSByQ9NLBoZjMlLZJ0UvSaa8wsGw1fK2mxpBnR1+D3BAAASKQhA5e7PyRpx6Da0+6+tsrmCyTd5u4Fd18vaZ2kuWY2UdJod1/m7i7pJknnvv72AQAADn8jfQ7XZEmbBjzvjGqTo8eD61WZ2WIz6zCzjq6urhFuEQAAoLZGOnBVOy/LX6Velbtf5+55d8+3tbWNWHMAAABxGOnA1SlpyoDn7ZI2R/X2KnUAAIDEG+nAtUTSIjNrMLNp6j85/lF33yKp28zmRVcnXiDpjhH+3gAAAIel3FAbmNmtkuZLajWzTklXqv8k+qsltUm6y8wed/cz3X21md0u6SlJJUmXuHs5equL1X/FY5OkpdEXAABA4ln/RYOHr3w+7x0dHXG3AQAAMCQzW+Hu+cF17jQPAAAQGIELAAAgMAIXAABAYEOeNA8k0bPPdWnZH/6kxoY6zT/9BLWOGxV3SwCABCNwIVXcXVf/n1/qjvueVKlcUS6b0bU3P6QvffpszT/9hLjbAwAkFIcUkSpPPNWpJfevVKFYUrlcUaFYUqFY0teuult79hbibg8AkFAELqTKvb95WoVi3wH1bDaj5Y9vqH1DAIBUIHAhVdxd1W495xXX4X5POgDAGxeBC6ky95SpVeu9hb6DjgEA8HoRuJAqf9q4XWYH1nO5jNY911X7hgAAqUDgQqp0vvBS1UOKuVxWL3Ttrn1DAIBUIHAhVU75i3Y1NtQdUK9UXCdOPyqGjgAAaUDgQqqc+faZam7aP3BlzDT7zVM0/ZjWmLoCACQdgQup0lvoU2+htH/RpK4XX+YqRQBAMAQupMr/u3+lyuXyfrVKxfX8Czu1au3mmLoCACQdgQupsn7TdhWK5apjnS/srHE3AIC0IHAhVWbOmKjGhgM/QrTiruOPbYuhIwBAGhC4kCrvmX+ympvqlcm8cjOu+rqs3nziZM2YdmSMnQEAkozAhVQZ1dKgH/3T+Zo/7wQ1N9ZpzBFNWvjeOfrWZe+PuzUAQIIdeGwFSLgjJxyhr/63v427DQBAirCHCwAAIDACFwAAQGAELgAAgMAIXAAAAIERuAAAAAIjcAEAAARG4AIAAAiMwAUAABAYgQsAACAwAhcAAEBgBC4AAIDACFwAAACBEbgAAAACI3ABAAAERuACAAAIjMAFAAAQGIELAAAgMAIXAABAYAQuAACAwAhcAAAAgRG4AAAAAiNwAQAABEbgAgAACIzABQAAEBiBCwAAIDACFwAAQGAELgAAgMAIXAAAAIERuAAAAAIjcAEAAARG4AIAAAiMwAUAABAYgQsAACAwAhcAAEBgBC4AAIDACFwAAACBEbgAAAACI3ABAAAERuACAAAIjMAFAAAQGIELAAAgMAIXAABAYAQuAACAwAhcAAAAgRG4AAAAAiNwAQAABEbgAgAACIzABQAAEBiBCwAAIDACFwAAQGAELgAAgMBycTcA1Frnlpf03R89oBVPPqe6uqze/dcz9akL56u5qT7u1gAACUXgQqrs7u7R4i/8VN17CnJ3lQslLf3Vav1p43Zd+42/k5nF3SIAIIE4pIhUufPBVSoUS3L3P9f6+spat6FLa559IcbOAABJRuBCqjyzfqsKxdIBdTNpQ+eOGDoCAKQBgQupMmPaUWqoP/BIurs0tX18DB0BANJgyMBlZjeY2TYzWzWgNt7M7jOzZ6I/xw0Yu8zM1pnZWjM7c0B9jpmtjMauMk6WQQze+46T1VCf2+9crbq6rI47tlVvOu7oGDsDACTZcPZw3SjprEG1L0h6wN1nSHogei4zmylpkaSTotdcY2bZ6DXXSlosaUb0Nfg9geDGHNGk6/7xw8rPOkaZjKmhPqf3vH2mvnfFQk6YBwAEM+RViu7+kJlNHVReIGl+9PjHkn4l6dKofpu7FyStN7N1kuaa2QZJo919mSSZ2U2SzpW09HXPAHiN2ieO0/euWCh3J2QBAGriUM/hOsrdt0hS9OeRUX2ypE0DtuuMapOjx4PrVZnZYjPrMLOOrq6uQ2wReHWELQBArYz0SfPVfoP5q9Srcvfr3D3v7vm2trYRaw4AACAOhxq4tprZREmK/twW1TslTRmwXbukzVG9vUodAAAg8Q41cC2RdGH0+EJJdwyoLzKzBjObpv6T4x+NDjt2m9m86OrECwa8BgAAINGGPGnezG5V/wnyrWbWKelKSf8o6XYz+7ikjZIWSpK7rzaz2yU9Jakk6RJ3L0dvdbH6r3hsUv/J8pwwDwAAUsEGfsTJ4Sifz3tHR0fcbQAAAAzJzFa4e35wnTvNAwAABEbgAgAACIzABQAAEBiBCwAAIDACFwAAQGAELgAAgMAIXAAAAIEd9vfhMrMuSc8F/jatkrYH/h6HqzTPXUr3/NM8dynd80/z3KV0z5+5h3esux/wQdCHfeCqBTPrqHaTsjRI89yldM8/zXOX0j3/NM9dSvf8mXt8c+eQIgAAQGAELgAAgMAIXP2ui7uBGKV57lK655/muUvpnn+a5y6le/7MPSacwwUAABAYe7gAAAACI3ABAAAElqrAZWZnmdlaM1tnZl+oMm5mdlU0/qSZzY6jzxCGMff5ZrbLzB6Pvq6Io88QzOwGM9tmZqsOMp7kdR9q7kle9ylm9ksze9rMVpvZZ6psk+S1H878E7n+ZtZoZo+a2RPR3L9SZZskr/1w5p/Itd/HzLJm9piZ3VllLJ61d/dUfEnKSnpW0nRJ9ZKekDRz0DZnS1oqySTNk7Q87r5rOPf5ku6Mu9dA8/9rSbMlrTrIeCLXfZhzT/K6T5Q0O3p8hKQ/puXv/GuYfyLXP1rPUdHjOknLJc1L0doPZ/6JXPsB8/ucpFuqzTGutU/THq65kta5+5/cvSjpNkkLBm2zQNJN3u8RSWPNbGKtGw1gOHNPLHd/SNKOV9kkqes+nLknlrtvcfc/RI+7JT0tafKgzZK89sOZfyJF6/ly9LQu+hp8hViS1344808sM2uX9F5JPzrIJrGsfZoC12RJmwY879SB//gMZ5s3ouHO6/RoF/RSMzupNq0dFh7XsfgAAAJJSURBVJK67sOV+HU3s6mS/lL9/9MfKBVr/yrzlxK6/tEhpcclbZN0n7unau2HMX8poWsv6fuSPi+pcpDxWNY+TYHLqtQGJ/7hbPNGNJx5/UH9n/90iqSrJf0ieFeHj6Su+3Akft3NbJSkf5X0WXffPXi4yksStfZDzD+x6+/uZXc/VVK7pLlmdvKgTRK99sOYfyLX3szeJ2mbu694tc2q1IKvfZoCV6ekKQOet0vafAjbvBENOS93371vF7S73y2pzsxaa9dirJK67kNK+rqbWZ36w8ZP3f3fqmyS6LUfav5JX39Jcvedkn4l6axBQ4le+30ONv8Er/1bJZ1jZhvUf/rMO8zsJ4O2iWXt0xS4fi9phplNM7N6SYskLRm0zRJJF0RXMMyTtMvdt9S60QCGnLuZHW1mFj2eq/6fjRdr3mk8krruQ0ryukfzul7S0+7+3YNslti1H878k7r+ZtZmZmOjx02S3ilpzaDNkrz2Q84/qWvv7pe5e7u7T1X/77oH3f0jgzaLZe1zob/B4cLdS2b2KUn3qP+qvRvcfbWZXRSN/7Oku9V/9cI6SXslfTSufkfSMOf+QUkXm1lJUo+kRR5dzvFGZ2a3qv+KnFYz65R0pfpPIk30ukvDmnti1139/9M9X9LK6FwWSfqipGOk5K+9hjf/pK7/REk/NrOs+oPE7e5+Zxr+vY8MZ/5JXfuqDoe156N9AAAAAkvTIUUAAIBYELgAAAACI3ABAAAERuACAAAIjMAFAAAQGIELAAAgMAIXAABAYP8fjhOfNmfTS/4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Clusters\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(univ['clusterid'],univ['SAT'], c=hc.labels_) "
   ]
  },
  {
   "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
}