P29. Unsupervised ML - Hierarchical Clustering (Univ.)-ver-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised ML - Hierarchical Clustering (Univ.)\n",
    "# Using sklearn normalize function"
   ]
  },
  {
   "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\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "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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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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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       "  </thead>\n",
       "  <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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.057602</td>\n",
       "      <td>0.003913</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>0.998323</td>\n",
       "      <td>0.004133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.022252</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.999750</td>\n",
       "      <td>0.001274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.050283</td>\n",
       "      <td>0.002474</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.998725</td>\n",
       "      <td>0.002873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.041538</td>\n",
       "      <td>0.002410</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.999130</td>\n",
       "      <td>0.002790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.058443</td>\n",
       "      <td>0.003790</td>\n",
       "      <td>0.001507</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.998274</td>\n",
       "      <td>0.004109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.041628</td>\n",
       "      <td>0.002765</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>0.002951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.041597</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>0.003005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.062235</td>\n",
       "      <td>0.003670</td>\n",
       "      <td>0.001190</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.998043</td>\n",
       "      <td>0.004562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.035398</td>\n",
       "      <td>0.002301</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.999368</td>\n",
       "      <td>0.002453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.022230</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.000750</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.999751</td>\n",
       "      <td>0.001482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.039544</td>\n",
       "      <td>0.002694</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>0.002608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.044871</td>\n",
       "      <td>0.003027</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.998982</td>\n",
       "      <td>0.003169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.082704</td>\n",
       "      <td>0.005338</td>\n",
       "      <td>0.002768</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.996536</td>\n",
       "      <td>0.006195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.105538</td>\n",
       "      <td>0.003710</td>\n",
       "      <td>0.005272</td>\n",
       "      <td>0.001757</td>\n",
       "      <td>0.994362</td>\n",
       "      <td>0.007810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.045452</td>\n",
       "      <td>0.003008</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.998957</td>\n",
       "      <td>0.003140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.110170</td>\n",
       "      <td>0.003069</td>\n",
       "      <td>0.009866</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>0.993828</td>\n",
       "      <td>0.007564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.037285</td>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.999298</td>\n",
       "      <td>0.002550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.122566</td>\n",
       "      <td>0.005587</td>\n",
       "      <td>0.007639</td>\n",
       "      <td>0.002850</td>\n",
       "      <td>0.992382</td>\n",
       "      <td>0.007639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.081626</td>\n",
       "      <td>0.006254</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.001119</td>\n",
       "      <td>0.996626</td>\n",
       "      <td>0.005135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.033592</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.001302</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.999430</td>\n",
       "      <td>0.002266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.076053</td>\n",
       "      <td>0.004189</td>\n",
       "      <td>0.004383</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>0.997070</td>\n",
       "      <td>0.005478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.046586</td>\n",
       "      <td>0.002900</td>\n",
       "      <td>0.001305</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.998904</td>\n",
       "      <td>0.003263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.091379</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.003282</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>0.995770</td>\n",
       "      <td>0.006863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.091123</td>\n",
       "      <td>0.003359</td>\n",
       "      <td>0.005795</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.995798</td>\n",
       "      <td>0.005963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.031583</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.999496</td>\n",
       "      <td>0.002205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SAT     Top10    Accept   SFRatio  Expenses  GradRate\n",
       "0   0.057602  0.003913  0.000967  0.000572  0.998323  0.004133\n",
       "1   0.022252  0.001573  0.000393  0.000094  0.999750  0.001274\n",
       "2   0.050283  0.002474  0.002355  0.000359  0.998725  0.002873\n",
       "3   0.041538  0.002410  0.000761  0.000381  0.999130  0.002790\n",
       "4   0.058443  0.003790  0.001507  0.000594  0.998274  0.004109\n",
       "5   0.041628  0.002765  0.000715  0.000311  0.999125  0.002951\n",
       "6   0.041597  0.002847  0.000949  0.000380  0.999125  0.003005\n",
       "7   0.062235  0.003670  0.001190  0.000595  0.998043  0.004562\n",
       "8   0.035398  0.002301  0.000354  0.000278  0.999368  0.002453\n",
       "9   0.022230  0.001278  0.000750  0.000119  0.999751  0.001482\n",
       "10  0.039544  0.002694  0.000860  0.000287  0.999210  0.002608\n",
       "11  0.044871  0.003027  0.001389  0.000392  0.998982  0.003169\n",
       "12  0.082704  0.005338  0.002768  0.000857  0.996536  0.006195\n",
       "13  0.105538  0.003710  0.005272  0.001757  0.994362  0.007810\n",
       "14  0.045452  0.003008  0.000463  0.000264  0.998957  0.003140\n",
       "15  0.110170  0.003069  0.009866  0.002083  0.993828  0.007564\n",
       "16  0.037285  0.002467  0.000548  0.000329  0.999298  0.002550\n",
       "17  0.122566  0.005587  0.007639  0.002850  0.992382  0.007639\n",
       "18  0.081626  0.006254  0.002633  0.001119  0.996626  0.005135\n",
       "19  0.033592  0.001953  0.001302  0.000339  0.999430  0.002266\n",
       "20  0.076053  0.004189  0.004383  0.001031  0.997070  0.005478\n",
       "21  0.046586  0.002900  0.001305  0.000399  0.998904  0.003263\n",
       "22  0.091379  0.005744  0.003282  0.001044  0.995770  0.006863\n",
       "23  0.091123  0.003359  0.005795  0.001260  0.995798  0.005963\n",
       "24  0.031583  0.002182  0.000436  0.000253  0.999496  0.002205"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Normalized data frame (considering only the numerical part of data)\n",
    "univ2_norm=pd.DataFrame(normalize(univ2),columns=univ2.columns)\n",
    "univ2_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Clusters\n",
    "hc=AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    clusters\n",
       "0          4\n",
       "1          0\n",
       "2          3\n",
       "3          3\n",
       "4          4\n",
       "5          3\n",
       "6          3\n",
       "7          4\n",
       "8          0\n",
       "9          0\n",
       "10         3\n",
       "11         3\n",
       "12         1\n",
       "13         2\n",
       "14         3\n",
       "15         2\n",
       "16         0\n",
       "17         2\n",
       "18         1\n",
       "19         0\n",
       "20         1\n",
       "21         3\n",
       "22         1\n",
       "23         1\n",
       "24         0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.DataFrame(hc.fit_predict(univ2_norm),columns=['clusters'])\n",
    "y"
   ]
  },
  {
   "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",
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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>4</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>3</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>4</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>3</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>3</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>4</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>0</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>3</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>1</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>2</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>3</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>2</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>0</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>2</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>1</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>1</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>1</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>0</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          4\n",
       "1        CalTech  1415    100      25        6     63575        81          0\n",
       "2            CMU  1260     62      59        9     25026        72          3\n",
       "3       Columbia  1310     76      24       12     31510        88          3\n",
       "4        Cornell  1280     83      33       13     21864        90          4\n",
       "5      Dartmouth  1340     89      23       10     32162        95          3\n",
       "6           Duke  1315     90      30       12     31585        95          3\n",
       "7     Georgetown  1255     74      24       12     20126        92          4\n",
       "8        Harvard  1400     91      14       11     39525        97          0\n",
       "9   JohnsHopkins  1305     75      44        7     58691        87          0\n",
       "10           MIT  1380     94      30       10     34870        91          3\n",
       "11  Northwestern  1260     85      39       11     28052        89          3\n",
       "12     NotreDame  1255     81      42       13     15122        94          1\n",
       "13     PennState  1081     38      54       18     10185        80          2\n",
       "14     Princeton  1375     91      14        8     30220        95          3\n",
       "15        Purdue  1005     28      90       19      9066        69          2\n",
       "16      Stanford  1360     90      20       12     36450        93          0\n",
       "17      TexasA&M  1075     49      67       25      8704        67          2\n",
       "18    UCBerkeley  1240     95      40       17     15140        78          1\n",
       "19      UChicago  1290     75      50       13     38380        87          0\n",
       "20     UMichigan  1180     65      68       16     15470        85          1\n",
       "21         UPenn  1285     80      36       11     27553        90          3\n",
       "22           UVA  1225     77      44       14     13349        92          1\n",
       "23    UWisconsin  1085     40      69       15     11857        71          1\n",
       "24          Yale  1375     95      19       11     43514        96          0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "univ['clusterid']=hc.labels_\n",
    "univ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>1357.500000</td>\n",
       "      <td>87.666667</td>\n",
       "      <td>28.666667</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>46689.166667</td>\n",
       "      <td>90.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>71.600000</td>\n",
       "      <td>52.600000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14187.600000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1053.666667</td>\n",
       "      <td>38.333333</td>\n",
       "      <td>70.333333</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>9318.333333</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1315.625000</td>\n",
       "      <td>83.375000</td>\n",
       "      <td>31.875000</td>\n",
       "      <td>10.375000</td>\n",
       "      <td>30122.250000</td>\n",
       "      <td>89.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1281.666667</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>21564.666667</td>\n",
       "      <td>92.000000</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  1357.500000  87.666667  28.666667  10.000000  46689.166667   \n",
       "1         1  1197.000000  71.600000  52.600000  15.000000  14187.600000   \n",
       "2         2  1053.666667  38.333333  70.333333  20.666667   9318.333333   \n",
       "3         3  1315.625000  83.375000  31.875000  10.375000  30122.250000   \n",
       "4         4  1281.666667  82.000000  26.333333  12.666667  21564.666667   \n",
       "\n",
       "    GradRate  \n",
       "        mean  \n",
       "0  90.166667  \n",
       "1  84.000000  \n",
       "2  72.000000  \n",
       "3  89.375000  \n",
       "4  92.000000  "
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c2632f2df0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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
}