Copy of University Classification - KMeans Clustering.ipynb

copy-of-university-classification-kmeans-clustering.ipynb

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    "colab": {
      "name": "Copy of University Classification - KMeans Clustering.ipynb",
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      "toc_visible": true,
      "mount_file_id": "1d6_Z9VtxVdwmRfQzH3YpUFYt3RUbKYOt",
      "authorship_tag": "ABX9TyNpEDwUZdUqFZcrarvKR21O",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/luisgdelafuente/2d1ea4d8da92cf2d007f8681146be7d0/copy-of-university-classification-kmeans-clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TJBwi08gHBv"
      },
      "source": [
        "# Import Libraries and Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knEiecaTgNn1"
      },
      "source": [
        "# LIBRARIES FOR DATA ANALYSIS\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wIm2UfqQgO8W",
        "outputId": "574558b0-971d-44ef-d7bd-64b6d066a5dd"
      },
      "source": [
        "# GET THE DATA \n",
        "# Read in the College_Data file using read_csv. \n",
        "# Figure out how to set the first column as the index and read the first 20 points. \n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/datasets/College_Data', index_col=0)\n",
        "\n",
        "df.head(20)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Private</th>\n",
              "      <th>Apps</th>\n",
              "      <th>Accept</th>\n",
              "      <th>Enroll</th>\n",
              "      <th>Top10perc</th>\n",
              "      <th>Top25perc</th>\n",
              "      <th>F.Undergrad</th>\n",
              "      <th>P.Undergrad</th>\n",
              "      <th>Outstate</th>\n",
              "      <th>Room.Board</th>\n",
              "      <th>Books</th>\n",
              "      <th>Personal</th>\n",
              "      <th>PhD</th>\n",
              "      <th>Terminal</th>\n",
              "      <th>S.F.Ratio</th>\n",
              "      <th>perc.alumni</th>\n",
              "      <th>Expend</th>\n",
              "      <th>Grad.Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Abilene Christian University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1660</td>\n",
              "      <td>1232</td>\n",
              "      <td>721</td>\n",
              "      <td>23</td>\n",
              "      <td>52</td>\n",
              "      <td>2885</td>\n",
              "      <td>537</td>\n",
              "      <td>7440</td>\n",
              "      <td>3300</td>\n",
              "      <td>450</td>\n",
              "      <td>2200</td>\n",
              "      <td>70</td>\n",
              "      <td>78</td>\n",
              "      <td>18.1</td>\n",
              "      <td>12</td>\n",
              "      <td>7041</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adelphi University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2186</td>\n",
              "      <td>1924</td>\n",
              "      <td>512</td>\n",
              "      <td>16</td>\n",
              "      <td>29</td>\n",
              "      <td>2683</td>\n",
              "      <td>1227</td>\n",
              "      <td>12280</td>\n",
              "      <td>6450</td>\n",
              "      <td>750</td>\n",
              "      <td>1500</td>\n",
              "      <td>29</td>\n",
              "      <td>30</td>\n",
              "      <td>12.2</td>\n",
              "      <td>16</td>\n",
              "      <td>10527</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adrian College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1428</td>\n",
              "      <td>1097</td>\n",
              "      <td>336</td>\n",
              "      <td>22</td>\n",
              "      <td>50</td>\n",
              "      <td>1036</td>\n",
              "      <td>99</td>\n",
              "      <td>11250</td>\n",
              "      <td>3750</td>\n",
              "      <td>400</td>\n",
              "      <td>1165</td>\n",
              "      <td>53</td>\n",
              "      <td>66</td>\n",
              "      <td>12.9</td>\n",
              "      <td>30</td>\n",
              "      <td>8735</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agnes Scott College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>417</td>\n",
              "      <td>349</td>\n",
              "      <td>137</td>\n",
              "      <td>60</td>\n",
              "      <td>89</td>\n",
              "      <td>510</td>\n",
              "      <td>63</td>\n",
              "      <td>12960</td>\n",
              "      <td>5450</td>\n",
              "      <td>450</td>\n",
              "      <td>875</td>\n",
              "      <td>92</td>\n",
              "      <td>97</td>\n",
              "      <td>7.7</td>\n",
              "      <td>37</td>\n",
              "      <td>19016</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alaska Pacific University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>193</td>\n",
              "      <td>146</td>\n",
              "      <td>55</td>\n",
              "      <td>16</td>\n",
              "      <td>44</td>\n",
              "      <td>249</td>\n",
              "      <td>869</td>\n",
              "      <td>7560</td>\n",
              "      <td>4120</td>\n",
              "      <td>800</td>\n",
              "      <td>1500</td>\n",
              "      <td>76</td>\n",
              "      <td>72</td>\n",
              "      <td>11.9</td>\n",
              "      <td>2</td>\n",
              "      <td>10922</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albertson College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>587</td>\n",
              "      <td>479</td>\n",
              "      <td>158</td>\n",
              "      <td>38</td>\n",
              "      <td>62</td>\n",
              "      <td>678</td>\n",
              "      <td>41</td>\n",
              "      <td>13500</td>\n",
              "      <td>3335</td>\n",
              "      <td>500</td>\n",
              "      <td>675</td>\n",
              "      <td>67</td>\n",
              "      <td>73</td>\n",
              "      <td>9.4</td>\n",
              "      <td>11</td>\n",
              "      <td>9727</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albertus Magnus College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>353</td>\n",
              "      <td>340</td>\n",
              "      <td>103</td>\n",
              "      <td>17</td>\n",
              "      <td>45</td>\n",
              "      <td>416</td>\n",
              "      <td>230</td>\n",
              "      <td>13290</td>\n",
              "      <td>5720</td>\n",
              "      <td>500</td>\n",
              "      <td>1500</td>\n",
              "      <td>90</td>\n",
              "      <td>93</td>\n",
              "      <td>11.5</td>\n",
              "      <td>26</td>\n",
              "      <td>8861</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albion College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1899</td>\n",
              "      <td>1720</td>\n",
              "      <td>489</td>\n",
              "      <td>37</td>\n",
              "      <td>68</td>\n",
              "      <td>1594</td>\n",
              "      <td>32</td>\n",
              "      <td>13868</td>\n",
              "      <td>4826</td>\n",
              "      <td>450</td>\n",
              "      <td>850</td>\n",
              "      <td>89</td>\n",
              "      <td>100</td>\n",
              "      <td>13.7</td>\n",
              "      <td>37</td>\n",
              "      <td>11487</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albright College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1038</td>\n",
              "      <td>839</td>\n",
              "      <td>227</td>\n",
              "      <td>30</td>\n",
              "      <td>63</td>\n",
              "      <td>973</td>\n",
              "      <td>306</td>\n",
              "      <td>15595</td>\n",
              "      <td>4400</td>\n",
              "      <td>300</td>\n",
              "      <td>500</td>\n",
              "      <td>79</td>\n",
              "      <td>84</td>\n",
              "      <td>11.3</td>\n",
              "      <td>23</td>\n",
              "      <td>11644</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alderson-Broaddus College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>582</td>\n",
              "      <td>498</td>\n",
              "      <td>172</td>\n",
              "      <td>21</td>\n",
              "      <td>44</td>\n",
              "      <td>799</td>\n",
              "      <td>78</td>\n",
              "      <td>10468</td>\n",
              "      <td>3380</td>\n",
              "      <td>660</td>\n",
              "      <td>1800</td>\n",
              "      <td>40</td>\n",
              "      <td>41</td>\n",
              "      <td>11.5</td>\n",
              "      <td>15</td>\n",
              "      <td>8991</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alfred University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1732</td>\n",
              "      <td>1425</td>\n",
              "      <td>472</td>\n",
              "      <td>37</td>\n",
              "      <td>75</td>\n",
              "      <td>1830</td>\n",
              "      <td>110</td>\n",
              "      <td>16548</td>\n",
              "      <td>5406</td>\n",
              "      <td>500</td>\n",
              "      <td>600</td>\n",
              "      <td>82</td>\n",
              "      <td>88</td>\n",
              "      <td>11.3</td>\n",
              "      <td>31</td>\n",
              "      <td>10932</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allegheny College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2652</td>\n",
              "      <td>1900</td>\n",
              "      <td>484</td>\n",
              "      <td>44</td>\n",
              "      <td>77</td>\n",
              "      <td>1707</td>\n",
              "      <td>44</td>\n",
              "      <td>17080</td>\n",
              "      <td>4440</td>\n",
              "      <td>400</td>\n",
              "      <td>600</td>\n",
              "      <td>73</td>\n",
              "      <td>91</td>\n",
              "      <td>9.9</td>\n",
              "      <td>41</td>\n",
              "      <td>11711</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allentown Coll. of St. Francis de Sales</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1179</td>\n",
              "      <td>780</td>\n",
              "      <td>290</td>\n",
              "      <td>38</td>\n",
              "      <td>64</td>\n",
              "      <td>1130</td>\n",
              "      <td>638</td>\n",
              "      <td>9690</td>\n",
              "      <td>4785</td>\n",
              "      <td>600</td>\n",
              "      <td>1000</td>\n",
              "      <td>60</td>\n",
              "      <td>84</td>\n",
              "      <td>13.3</td>\n",
              "      <td>21</td>\n",
              "      <td>7940</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alma College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1267</td>\n",
              "      <td>1080</td>\n",
              "      <td>385</td>\n",
              "      <td>44</td>\n",
              "      <td>73</td>\n",
              "      <td>1306</td>\n",
              "      <td>28</td>\n",
              "      <td>12572</td>\n",
              "      <td>4552</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>79</td>\n",
              "      <td>87</td>\n",
              "      <td>15.3</td>\n",
              "      <td>32</td>\n",
              "      <td>9305</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alverno College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>494</td>\n",
              "      <td>313</td>\n",
              "      <td>157</td>\n",
              "      <td>23</td>\n",
              "      <td>46</td>\n",
              "      <td>1317</td>\n",
              "      <td>1235</td>\n",
              "      <td>8352</td>\n",
              "      <td>3640</td>\n",
              "      <td>650</td>\n",
              "      <td>2449</td>\n",
              "      <td>36</td>\n",
              "      <td>69</td>\n",
              "      <td>11.1</td>\n",
              "      <td>26</td>\n",
              "      <td>8127</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>American International College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1420</td>\n",
              "      <td>1093</td>\n",
              "      <td>220</td>\n",
              "      <td>9</td>\n",
              "      <td>22</td>\n",
              "      <td>1018</td>\n",
              "      <td>287</td>\n",
              "      <td>8700</td>\n",
              "      <td>4780</td>\n",
              "      <td>450</td>\n",
              "      <td>1400</td>\n",
              "      <td>78</td>\n",
              "      <td>84</td>\n",
              "      <td>14.7</td>\n",
              "      <td>19</td>\n",
              "      <td>7355</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Amherst College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4302</td>\n",
              "      <td>992</td>\n",
              "      <td>418</td>\n",
              "      <td>83</td>\n",
              "      <td>96</td>\n",
              "      <td>1593</td>\n",
              "      <td>5</td>\n",
              "      <td>19760</td>\n",
              "      <td>5300</td>\n",
              "      <td>660</td>\n",
              "      <td>1598</td>\n",
              "      <td>93</td>\n",
              "      <td>98</td>\n",
              "      <td>8.4</td>\n",
              "      <td>63</td>\n",
              "      <td>21424</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Anderson University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1216</td>\n",
              "      <td>908</td>\n",
              "      <td>423</td>\n",
              "      <td>19</td>\n",
              "      <td>40</td>\n",
              "      <td>1819</td>\n",
              "      <td>281</td>\n",
              "      <td>10100</td>\n",
              "      <td>3520</td>\n",
              "      <td>550</td>\n",
              "      <td>1100</td>\n",
              "      <td>48</td>\n",
              "      <td>61</td>\n",
              "      <td>12.1</td>\n",
              "      <td>14</td>\n",
              "      <td>7994</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andrews University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1130</td>\n",
              "      <td>704</td>\n",
              "      <td>322</td>\n",
              "      <td>14</td>\n",
              "      <td>23</td>\n",
              "      <td>1586</td>\n",
              "      <td>326</td>\n",
              "      <td>9996</td>\n",
              "      <td>3090</td>\n",
              "      <td>900</td>\n",
              "      <td>1320</td>\n",
              "      <td>62</td>\n",
              "      <td>66</td>\n",
              "      <td>11.5</td>\n",
              "      <td>18</td>\n",
              "      <td>10908</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angelo State University</th>\n",
              "      <td>No</td>\n",
              "      <td>3540</td>\n",
              "      <td>2001</td>\n",
              "      <td>1016</td>\n",
              "      <td>24</td>\n",
              "      <td>54</td>\n",
              "      <td>4190</td>\n",
              "      <td>1512</td>\n",
              "      <td>5130</td>\n",
              "      <td>3592</td>\n",
              "      <td>500</td>\n",
              "      <td>2000</td>\n",
              "      <td>60</td>\n",
              "      <td>62</td>\n",
              "      <td>23.1</td>\n",
              "      <td>5</td>\n",
              "      <td>4010</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Private  Apps  ...  Expend  Grad.Rate\n",
              "Abilene Christian University                Yes  1660  ...    7041         60\n",
              "Adelphi University                          Yes  2186  ...   10527         56\n",
              "Adrian College                              Yes  1428  ...    8735         54\n",
              "Agnes Scott College                         Yes   417  ...   19016         59\n",
              "Alaska Pacific University                   Yes   193  ...   10922         15\n",
              "Albertson College                           Yes   587  ...    9727         55\n",
              "Albertus Magnus College                     Yes   353  ...    8861         63\n",
              "Albion College                              Yes  1899  ...   11487         73\n",
              "Albright College                            Yes  1038  ...   11644         80\n",
              "Alderson-Broaddus College                   Yes   582  ...    8991         52\n",
              "Alfred University                           Yes  1732  ...   10932         73\n",
              "Allegheny College                           Yes  2652  ...   11711         76\n",
              "Allentown Coll. of St. Francis de Sales     Yes  1179  ...    7940         74\n",
              "Alma College                                Yes  1267  ...    9305         68\n",
              "Alverno College                             Yes   494  ...    8127         55\n",
              "American International College              Yes  1420  ...    7355         69\n",
              "Amherst College                             Yes  4302  ...   21424        100\n",
              "Anderson University                         Yes  1216  ...    7994         59\n",
              "Andrews University                          Yes  1130  ...   10908         46\n",
              "Angelo State University                      No  3540  ...    4010         34\n",
              "\n",
              "[20 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "VERZGZWsglZn",
        "outputId": "350cf96c-21f1-48ec-dba2-aabe664fd379"
      },
      "source": [
        "# CHECH THE STRUCTURE OF THE DATA \n",
        "\n",
        "df.info()\n",
        "df.describe()\n",
        "\n",
        "# So we have in here a collection of 777 records containing many features and 1 label: Private YES/NO. \n",
        "# We need to build a model to predict the label according to all or some of the features. This is to be researched in the EDA. "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 777 entries, Abilene Christian University to York College of Pennsylvania\n",
            "Data columns (total 18 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Private      777 non-null    object \n",
            " 1   Apps         777 non-null    int64  \n",
            " 2   Accept       777 non-null    int64  \n",
            " 3   Enroll       777 non-null    int64  \n",
            " 4   Top10perc    777 non-null    int64  \n",
            " 5   Top25perc    777 non-null    int64  \n",
            " 6   F.Undergrad  777 non-null    int64  \n",
            " 7   P.Undergrad  777 non-null    int64  \n",
            " 8   Outstate     777 non-null    int64  \n",
            " 9   Room.Board   777 non-null    int64  \n",
            " 10  Books        777 non-null    int64  \n",
            " 11  Personal     777 non-null    int64  \n",
            " 12  PhD          777 non-null    int64  \n",
            " 13  Terminal     777 non-null    int64  \n",
            " 14  S.F.Ratio    777 non-null    float64\n",
            " 15  perc.alumni  777 non-null    int64  \n",
            " 16  Expend       777 non-null    int64  \n",
            " 17  Grad.Rate    777 non-null    int64  \n",
            "dtypes: float64(1), int64(16), object(1)\n",
            "memory usage: 135.3+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Apps</th>\n",
              "      <th>Accept</th>\n",
              "      <th>Enroll</th>\n",
              "      <th>Top10perc</th>\n",
              "      <th>Top25perc</th>\n",
              "      <th>F.Undergrad</th>\n",
              "      <th>P.Undergrad</th>\n",
              "      <th>Outstate</th>\n",
              "      <th>Room.Board</th>\n",
              "      <th>Books</th>\n",
              "      <th>Personal</th>\n",
              "      <th>PhD</th>\n",
              "      <th>Terminal</th>\n",
              "      <th>S.F.Ratio</th>\n",
              "      <th>perc.alumni</th>\n",
              "      <th>Expend</th>\n",
              "      <th>Grad.Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>777.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3001.638353</td>\n",
              "      <td>2018.804376</td>\n",
              "      <td>779.972973</td>\n",
              "      <td>27.558559</td>\n",
              "      <td>55.796654</td>\n",
              "      <td>3699.907336</td>\n",
              "      <td>855.298584</td>\n",
              "      <td>10440.669241</td>\n",
              "      <td>4357.526384</td>\n",
              "      <td>549.380952</td>\n",
              "      <td>1340.642214</td>\n",
              "      <td>72.660232</td>\n",
              "      <td>79.702703</td>\n",
              "      <td>14.089704</td>\n",
              "      <td>22.743887</td>\n",
              "      <td>9660.171171</td>\n",
              "      <td>65.46332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3870.201484</td>\n",
              "      <td>2451.113971</td>\n",
              "      <td>929.176190</td>\n",
              "      <td>17.640364</td>\n",
              "      <td>19.804778</td>\n",
              "      <td>4850.420531</td>\n",
              "      <td>1522.431887</td>\n",
              "      <td>4023.016484</td>\n",
              "      <td>1096.696416</td>\n",
              "      <td>165.105360</td>\n",
              "      <td>677.071454</td>\n",
              "      <td>16.328155</td>\n",
              "      <td>14.722359</td>\n",
              "      <td>3.958349</td>\n",
              "      <td>12.391801</td>\n",
              "      <td>5221.768440</td>\n",
              "      <td>17.17771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>81.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2340.000000</td>\n",
              "      <td>1780.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3186.000000</td>\n",
              "      <td>10.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>776.000000</td>\n",
              "      <td>604.000000</td>\n",
              "      <td>242.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>992.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>7320.000000</td>\n",
              "      <td>3597.000000</td>\n",
              "      <td>470.000000</td>\n",
              "      <td>850.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>6751.000000</td>\n",
              "      <td>53.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1558.000000</td>\n",
              "      <td>1110.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>1707.000000</td>\n",
              "      <td>353.000000</td>\n",
              "      <td>9990.000000</td>\n",
              "      <td>4200.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>13.600000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>8377.000000</td>\n",
              "      <td>65.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3624.000000</td>\n",
              "      <td>2424.000000</td>\n",
              "      <td>902.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>4005.000000</td>\n",
              "      <td>967.000000</td>\n",
              "      <td>12925.000000</td>\n",
              "      <td>5050.000000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>1700.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>10830.000000</td>\n",
              "      <td>78.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>48094.000000</td>\n",
              "      <td>26330.000000</td>\n",
              "      <td>6392.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>31643.000000</td>\n",
              "      <td>21836.000000</td>\n",
              "      <td>21700.000000</td>\n",
              "      <td>8124.000000</td>\n",
              "      <td>2340.000000</td>\n",
              "      <td>6800.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>39.800000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>56233.000000</td>\n",
              "      <td>118.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Apps        Accept  ...        Expend  Grad.Rate\n",
              "count    777.000000    777.000000  ...    777.000000  777.00000\n",
              "mean    3001.638353   2018.804376  ...   9660.171171   65.46332\n",
              "std     3870.201484   2451.113971  ...   5221.768440   17.17771\n",
              "min       81.000000     72.000000  ...   3186.000000   10.00000\n",
              "25%      776.000000    604.000000  ...   6751.000000   53.00000\n",
              "50%     1558.000000   1110.000000  ...   8377.000000   65.00000\n",
              "75%     3624.000000   2424.000000  ...  10830.000000   78.00000\n",
              "max    48094.000000  26330.000000  ...  56233.000000  118.00000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWRHCRv7gybk"
      },
      "source": [
        "# EXPLORATORY DATA ANALYSIS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "chi486msg152",
        "outputId": "7e96d03a-2588-4ddb-b989-54e47d410cf8"
      },
      "source": [
        "# Check the room board vs Grad.rate\n",
        "sns.set_style('whitegrid')\n",
        "sns.scatterplot(x='Grad.Rate',y=('Room.Board'),data=df,hue='Private')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d6d023c50>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "UV5a33EGhvm2",
        "outputId": "8cefdf88-6041-46f6-81a4-4cb2e96713b8"
      },
      "source": [
        "# ** Create a scatterplot of Grad.Rate versus Room.Board where the points are colored by the Private column. **n\n",
        "\n",
        "sns.lmplot(x = 'Room.Board',y = 'Grad.Rate',data=df, hue='Private', height=6,aspect=1,fit_reg=False)\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f7d5c416fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 484x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "MIsPs1liiydD",
        "outputId": "53dabf94-d4ba-4834-d0f4-e35166050f17"
      },
      "source": [
        "# Create a stacked histogram showing Out of State Tuition based on the Private column. Try doing this using sns.FacetGrid.\n",
        "# If that is too tricky, see if you can do it just by using two instances of pandas.plot(kind='hist').\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
        "g = g.map(plt.hist,'Outstate',bins=20,alpha=0.7)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "1kFctl3TjDkh",
        "outputId": "756277f9-8caa-4a96-d974-772829485a7d"
      },
      "source": [
        "# Create a similar histogram for the Grad.Rate column.\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
        "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "dFSWtItpjR-p",
        "outputId": "08b1dbf5-1316-4b51-adaf-5fbcfceee8e3"
      },
      "source": [
        "# Notice how there seems to be a private school with a graduation rate of higher than 100%.What is the name of that school?\n",
        "\n",
        "df[df['Grad.Rate'] > 100]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Private</th>\n",
              "      <th>Apps</th>\n",
              "      <th>Accept</th>\n",
              "      <th>Enroll</th>\n",
              "      <th>Top10perc</th>\n",
              "      <th>Top25perc</th>\n",
              "      <th>F.Undergrad</th>\n",
              "      <th>P.Undergrad</th>\n",
              "      <th>Outstate</th>\n",
              "      <th>Room.Board</th>\n",
              "      <th>Books</th>\n",
              "      <th>Personal</th>\n",
              "      <th>PhD</th>\n",
              "      <th>Terminal</th>\n",
              "      <th>S.F.Ratio</th>\n",
              "      <th>perc.alumni</th>\n",
              "      <th>Expend</th>\n",
              "      <th>Grad.Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cazenovia College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>3847</td>\n",
              "      <td>3433</td>\n",
              "      <td>527</td>\n",
              "      <td>9</td>\n",
              "      <td>35</td>\n",
              "      <td>1010</td>\n",
              "      <td>12</td>\n",
              "      <td>9384</td>\n",
              "      <td>4840</td>\n",
              "      <td>600</td>\n",
              "      <td>500</td>\n",
              "      <td>22</td>\n",
              "      <td>47</td>\n",
              "      <td>14.3</td>\n",
              "      <td>20</td>\n",
              "      <td>7697</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Private  Apps  Accept  ...  perc.alumni  Expend  Grad.Rate\n",
              "Cazenovia College     Yes  3847    3433  ...           20    7697        118\n",
              "\n",
              "[1 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9QHpjYljZ6j"
      },
      "source": [
        "** Set that school's graduation rate to 100 so it makes sense. You may get a warning not an error) when doing this operation, so use dataframe operations or just re-do the histogram visualization to make sure it actually went through.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "G8sUpt7BjcKj",
        "outputId": "12c3bb88-1b91-4394-b855-de569215cbc6"
      },
      "source": [
        "df['Grad.Rate']['Cazenovia College'] = 100\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "g = sns.FacetGrid(df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
        "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ASykGmYji0N"
      },
      "source": [
        "# K MEANS MODEL CREATION "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJpdDpSOjoW5",
        "outputId": "390b64cb-ca11-4878-b6e5-cf567509641e"
      },
      "source": [
        "# IMPORT, INSTANTIATE AND FIT THE MODEL (except for the private label)\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters=2)\n",
        "kmeans.fit(df.drop('Private',axis=1))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=2)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhDE-HY5j2JZ",
        "outputId": "0bfe4a15-33c0-4a6d-cd7c-cde3cbfb066d"
      },
      "source": [
        "# ** What are the cluster center vectors?**\n",
        "kmeans.cluster_centers_"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.81323468e+03, 1.28716592e+03, 4.91044843e+02, 2.53094170e+01,\n",
              "        5.34708520e+01, 2.18854858e+03, 5.95458894e+02, 1.03957085e+04,\n",
              "        4.31136472e+03, 5.41982063e+02, 1.28033632e+03, 7.04424514e+01,\n",
              "        7.78251121e+01, 1.40997010e+01, 2.31748879e+01, 8.93204634e+03,\n",
              "        6.50926756e+01],\n",
              "       [1.03631389e+04, 6.55089815e+03, 2.56972222e+03, 4.14907407e+01,\n",
              "        7.02037037e+01, 1.30619352e+04, 2.46486111e+03, 1.07191759e+04,\n",
              "        4.64347222e+03, 5.95212963e+02, 1.71420370e+03, 8.63981481e+01,\n",
              "        9.13333333e+01, 1.40277778e+01, 2.00740741e+01, 1.41705000e+04,\n",
              "        6.75925926e+01]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZLE6HrRj-Mx"
      },
      "source": [
        "# MODEL EVALUATION \n",
        "\n",
        "There is no perfect way to evaluate clustering if you don't have the labels, however since this is just an exercise, we do have the labels, so we take advantage of this to evaluate our clusters, keep in mind, you usually won't have this luxury in the real world.\n",
        "\n",
        "** Create a new column for df called 'Cluster', which is a 1 for a Private school, and a 0 for a public school.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LirkAVaDkClS"
      },
      "source": [
        "def converter(cluster):\n",
        "    if cluster=='Yes':\n",
        "        return 1\n",
        "    else:\n",
        "        return 0"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "Ws8tBUxUkICQ",
        "outputId": "7ef90788-7d7b-47b5-b4a8-e383fd0bef78"
      },
      "source": [
        "df['Cluster'] = df['Private'].apply(converter)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Private</th>\n",
              "      <th>Apps</th>\n",
              "      <th>Accept</th>\n",
              "      <th>Enroll</th>\n",
              "      <th>Top10perc</th>\n",
              "      <th>Top25perc</th>\n",
              "      <th>F.Undergrad</th>\n",
              "      <th>P.Undergrad</th>\n",
              "      <th>Outstate</th>\n",
              "      <th>Room.Board</th>\n",
              "      <th>Books</th>\n",
              "      <th>Personal</th>\n",
              "      <th>PhD</th>\n",
              "      <th>Terminal</th>\n",
              "      <th>S.F.Ratio</th>\n",
              "      <th>perc.alumni</th>\n",
              "      <th>Expend</th>\n",
              "      <th>Grad.Rate</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Abilene Christian University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1660</td>\n",
              "      <td>1232</td>\n",
              "      <td>721</td>\n",
              "      <td>23</td>\n",
              "      <td>52</td>\n",
              "      <td>2885</td>\n",
              "      <td>537</td>\n",
              "      <td>7440</td>\n",
              "      <td>3300</td>\n",
              "      <td>450</td>\n",
              "      <td>2200</td>\n",
              "      <td>70</td>\n",
              "      <td>78</td>\n",
              "      <td>18.1</td>\n",
              "      <td>12</td>\n",
              "      <td>7041</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adelphi University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2186</td>\n",
              "      <td>1924</td>\n",
              "      <td>512</td>\n",
              "      <td>16</td>\n",
              "      <td>29</td>\n",
              "      <td>2683</td>\n",
              "      <td>1227</td>\n",
              "      <td>12280</td>\n",
              "      <td>6450</td>\n",
              "      <td>750</td>\n",
              "      <td>1500</td>\n",
              "      <td>29</td>\n",
              "      <td>30</td>\n",
              "      <td>12.2</td>\n",
              "      <td>16</td>\n",
              "      <td>10527</td>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adrian College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>1428</td>\n",
              "      <td>1097</td>\n",
              "      <td>336</td>\n",
              "      <td>22</td>\n",
              "      <td>50</td>\n",
              "      <td>1036</td>\n",
              "      <td>99</td>\n",
              "      <td>11250</td>\n",
              "      <td>3750</td>\n",
              "      <td>400</td>\n",
              "      <td>1165</td>\n",
              "      <td>53</td>\n",
              "      <td>66</td>\n",
              "      <td>12.9</td>\n",
              "      <td>30</td>\n",
              "      <td>8735</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agnes Scott College</th>\n",
              "      <td>Yes</td>\n",
              "      <td>417</td>\n",
              "      <td>349</td>\n",
              "      <td>137</td>\n",
              "      <td>60</td>\n",
              "      <td>89</td>\n",
              "      <td>510</td>\n",
              "      <td>63</td>\n",
              "      <td>12960</td>\n",
              "      <td>5450</td>\n",
              "      <td>450</td>\n",
              "      <td>875</td>\n",
              "      <td>92</td>\n",
              "      <td>97</td>\n",
              "      <td>7.7</td>\n",
              "      <td>37</td>\n",
              "      <td>19016</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alaska Pacific University</th>\n",
              "      <td>Yes</td>\n",
              "      <td>193</td>\n",
              "      <td>146</td>\n",
              "      <td>55</td>\n",
              "      <td>16</td>\n",
              "      <td>44</td>\n",
              "      <td>249</td>\n",
              "      <td>869</td>\n",
              "      <td>7560</td>\n",
              "      <td>4120</td>\n",
              "      <td>800</td>\n",
              "      <td>1500</td>\n",
              "      <td>76</td>\n",
              "      <td>72</td>\n",
              "      <td>11.9</td>\n",
              "      <td>2</td>\n",
              "      <td>10922</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Private  Apps  Accept  ...  Expend  Grad.Rate  Cluster\n",
              "Abilene Christian University     Yes  1660    1232  ...    7041         60        1\n",
              "Adelphi University               Yes  2186    1924  ...   10527         56        1\n",
              "Adrian College                   Yes  1428    1097  ...    8735         54        1\n",
              "Agnes Scott College              Yes   417     349  ...   19016         59        1\n",
              "Alaska Pacific University        Yes   193     146  ...   10922         15        1\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSLPRkyGkNjW",
        "outputId": "36604aaa-c239-45ed-f8ec-4c73ed30b999"
      },
      "source": [
        "# Create a confusion matrix and classification report to see how well the Kmeans clustering worked without being given any labels\n",
        "\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "print(confusion_matrix(df['Cluster'],kmeans.labels_))\n",
        "print(classification_report(df['Cluster'],kmeans.labels_))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[138  74]\n",
            " [531  34]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.21      0.65      0.31       212\n",
            "           1       0.31      0.06      0.10       565\n",
            "\n",
            "    accuracy                           0.22       777\n",
            "   macro avg       0.26      0.36      0.21       777\n",
            "weighted avg       0.29      0.22      0.16       777\n",
            "\n"
          ]
        }
      ]
    }
  ]
}