K means clusttering

university.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Means Clustering Project \n",
    "_______________\n",
    "For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public.\n",
    "\n",
    "___\n",
    "It is **very important to note, we actually have the labels for this data set, but we will NOT use them for the KMeans clustering algorithm, since that is an unsupervised learning algorithm.** \n",
    "\n",
    "When using the Kmeans algorithm under normal circumstances, it is because we don't have labels. In this case we will use the labels to try to get an idea of how well the algorithm performed, but you won't usually do this for Kmeans, so the classification report and confusion matrix at the end of this project, don't truly make sense in a real world setting!.\n",
    "___\n",
    "\n",
    "## The Data\n",
    "\n",
    "We will use a data frame with 777 observations on the following 18 variables.\n",
    "* Private A factor with levels No and Yes indicating private or public university\n",
    "* Apps: Number of applications received\n",
    "* Accept: Number of applications accepted\n",
    "* Enroll: Number of new students enrolled\n",
    "* Top10perc: Pct. new students from top 10% of H.S. class\n",
    "* Top25perc: Pct. new students from top 25% of H.S. class\n",
    "* F: Undergrad Number of fulltime undergraduates\n",
    "* P: Undergrad Number of parttime undergraduates\n",
    "* Outstate: Out-of-state tuition\n",
    "* Room: Board Room and board costs\n",
    "* Books: Estimated book costs\n",
    "* Personal: Estimated personal spending\n",
    "* PhD: Pct. of faculty with Ph.D.’s\n",
    "* Terminal: Pct. of faculty with terminal degree\n",
    "* S.F.Ratio: Student/faculty ratio\n",
    "* perc.alumni: Pct. alumni who donate\n",
    "* Expend: Instructional expenditure per student\n",
    "* Grad: Rate Graduation rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "\n",
    "** Import the libraries required for data analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the College_Data file using read_csv.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('College_Data', index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the head of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "Abilene Christian University       18.1           12    7041         60  \n",
       "Adelphi University                 12.2           16   10527         56  \n",
       "Adrian College                     12.9           30    8735         54  \n",
       "Agnes Scott College                 7.7           37   19016         59  \n",
       "Alaska Pacific University          11.9            2   10922         15  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() and describe() methods on the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "Private        777 non-null object\n",
      "Apps           777 non-null int64\n",
      "Accept         777 non-null int64\n",
      "Enroll         777 non-null int64\n",
      "Top10perc      777 non-null int64\n",
      "Top25perc      777 non-null int64\n",
      "F.Undergrad    777 non-null int64\n",
      "P.Undergrad    777 non-null int64\n",
      "Outstate       777 non-null int64\n",
      "Room.Board     777 non-null int64\n",
      "Books          777 non-null int64\n",
      "Personal       777 non-null int64\n",
      "PhD            777 non-null int64\n",
      "Terminal       777 non-null int64\n",
      "S.F.Ratio      777 non-null float64\n",
      "perc.alumni    777 non-null int64\n",
      "Expend         777 non-null int64\n",
      "Grad.Rate      777 non-null int64\n",
      "dtypes: float64(1), int64(16), object(1)\n",
      "memory usage: 115.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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       Enroll   Top10perc   Top25perc  \\\n",
       "count    777.000000    777.000000   777.000000  777.000000  777.000000   \n",
       "mean    3001.638353   2018.804376   779.972973   27.558559   55.796654   \n",
       "std     3870.201484   2451.113971   929.176190   17.640364   19.804778   \n",
       "min       81.000000     72.000000    35.000000    1.000000    9.000000   \n",
       "25%      776.000000    604.000000   242.000000   15.000000   41.000000   \n",
       "50%     1558.000000   1110.000000   434.000000   23.000000   54.000000   \n",
       "75%     3624.000000   2424.000000   902.000000   35.000000   69.000000   \n",
       "max    48094.000000  26330.000000  6392.000000   96.000000  100.000000   \n",
       "\n",
       "        F.Undergrad   P.Undergrad      Outstate   Room.Board        Books  \\\n",
       "count    777.000000    777.000000    777.000000   777.000000   777.000000   \n",
       "mean    3699.907336    855.298584  10440.669241  4357.526384   549.380952   \n",
       "std     4850.420531   1522.431887   4023.016484  1096.696416   165.105360   \n",
       "min      139.000000      1.000000   2340.000000  1780.000000    96.000000   \n",
       "25%      992.000000     95.000000   7320.000000  3597.000000   470.000000   \n",
       "50%     1707.000000    353.000000   9990.000000  4200.000000   500.000000   \n",
       "75%     4005.000000    967.000000  12925.000000  5050.000000   600.000000   \n",
       "max    31643.000000  21836.000000  21700.000000  8124.000000  2340.000000   \n",
       "\n",
       "          Personal         PhD    Terminal   S.F.Ratio  perc.alumni  \\\n",
       "count   777.000000  777.000000  777.000000  777.000000   777.000000   \n",
       "mean   1340.642214   72.660232   79.702703   14.089704    22.743887   \n",
       "std     677.071454   16.328155   14.722359    3.958349    12.391801   \n",
       "min     250.000000    8.000000   24.000000    2.500000     0.000000   \n",
       "25%     850.000000   62.000000   71.000000   11.500000    13.000000   \n",
       "50%    1200.000000   75.000000   82.000000   13.600000    21.000000   \n",
       "75%    1700.000000   85.000000   92.000000   16.500000    31.000000   \n",
       "max    6800.000000  103.000000  100.000000   39.800000    64.000000   \n",
       "\n",
       "             Expend  Grad.Rate  \n",
       "count    777.000000  777.00000  \n",
       "mean    9660.171171   65.46332  \n",
       "std     5221.768440   17.17771  \n",
       "min     3186.000000   10.00000  \n",
       "25%     6751.000000   53.00000  \n",
       "50%     8377.000000   65.00000  \n",
       "75%    10830.000000   78.00000  \n",
       "max    56233.000000  118.00000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis\n",
    "\n",
    "It's time to create some data visualizations!\n",
    "\n",
    "** Scatterplot of Grad.Rate versus Room.Board where the points are colored by the Private column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x162a59ccfd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EP6CV7iZgWKL1rFfm7ibJgNWQNGWAhThepbKsx2aEgVEWmk1SJrCSyME4n6E8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162a5dde048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Room.Board','Grad.Rate',data = data, fit_reg=False, hue='Private',palette='coolwarm',size=6,aspect=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatterplot of F.Undergrad versus Outstate where the points are colored by the Private column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Private', 'Apps', 'Accept', 'Enroll', 'Top10perc', 'Top25perc',\n",
       "       'F.Undergrad', 'P.Undergrad', 'Outstate', 'Room.Board', 'Books',\n",
       "       'Personal', 'PhD', 'Terminal', 'S.F.Ratio', 'perc.alumni', 'Expend',\n",
       "       'Grad.Rate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H2rnHjx9X1Ra3OzvBply0vHa5VKKN9tAopvk/SPxsjoZgEDLCBjPklG3zQm/s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162a5b05470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Outstate','F.Undergrad',data=data,fit_reg=False, hue='Private',palette='coolwarm',size=6,aspect=1)\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Stacked histogram showing Out of State Tuition based on the Private column using [sns.FacetGrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x162a5bdbd68>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCIgAUAAFCI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162a5bd6240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g.map(plt.hist,'Outstate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram for the Grad.Rate column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x162a6efb5c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x162a6efb470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There seems to be a private school with a graduation rate of higher than 100%.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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  Enroll  Top10perc  Top25perc  \\\n",
       "Cazenovia College     Yes  3847    3433     527          9         35   \n",
       "\n",
       "                   F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  \\\n",
       "Cazenovia College         1010           12      9384        4840    600   \n",
       "\n",
       "                   Personal  PhD  Terminal  S.F.Ratio  perc.alumni  Expend  \\\n",
       "Cazenovia College       500   22        47       14.3           20    7697   \n",
       "\n",
       "                   Grad.Rate  \n",
       "Cazenovia College        118  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Grad.Rate']>100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Setting that school's graduation rate to 100 so it makes sense.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "data['Grad.Rate']['Cazenovia College'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Private, Apps, Accept, Enroll, Top10perc, Top25perc, F.Undergrad, P.Undergrad, Outstate, Room.Board, Books, Personal, PhD, Terminal, S.F.Ratio, perc.alumni, Expend, Grad.Rate]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Grad.Rate']>100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAEYrf7wKAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAKEVgAAACFCCwAAIBCBBYAAEAh\nAgsAAKAQgQUAAFCIwAIAAChEYAEAABQisAAAAAoRWAAAAIUILAAAgEIaaz0AAMM76aWnR3wbM3v9\nzg0AjpW9KQAAQCECCwAAoBCBBQAAUIjAAgAAKERgAQAAFCKwAAAAChFYAAAAhQgsAACAQgQWAABA\nIQILAACgkMZaDwAwVj23Y/xhP3dmr9+HAcBoYI8NAABQiMACAAAoRGABAAAUIrAAAAAKcZELABiD\nZu7eWOsRDurFUy6t9QgAx8QRLAAAgEIEFgAAQCECCwAAoBCBBQAAUIjAAgAAKERgAQAAFCKwAAAA\nChFYAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQSOOhHuzv78/ixYuzffv29PX15bbbbsuZZ56Z\nRYsWpVKpZMaMGWlvb09Dg04DAAA4ZGCtX78+U6ZMycqVK/Pzn/88f/zHf5yZM2emra0tF154YZYt\nW5YNGzbk8ssvP17zAgAA1K1DHnq64oor8tnPfjZJUq1WM27cuGzZsiWzZ89OksydOzebN28e+SkB\nAABGgUMewZo4cWKSZO/evfnMZz6Ttra2rFixIpVKZejx7u7uYTcyderJaWwcV2Bc6llLy+RajwAH\nVa9rs+mNwcN+rlOxR7empgN3twe773iq1zVV688L/h/USktLU61H2M+R7KOOh3rdlx/MsF9BO3bs\nyPz583PDDTfkYx/7WFauXDn0WE9PT5qbm4fdyJ49bx3blNS9lpbJ6eoaPrbheKvntdnXN/6wnzs4\nWF87Oo5MX9/AfrebmhoPuO94q9c1VevPy1hXD2tzrOrq6q31CPs5kn3UyGuqy335O0XfIX999frr\nr+emm27KggULcu211yZJzjnnnHR2diZJOjo6MmvWrMKjAgAAjE6HDKz7778/b775Zr7+9a9n3rx5\nmTdvXtra2rJq1apcd9116e/vT2tr6/GaFQAAoK4d8hTBJUuWZMmSJQfcv3bt2hEbCAAAYLSqz3e4\nAgAAjEICCwAAoBCBBQAAUIjAAgAAKERgAQAAFOJPdQPACJu5e+N+txsaGur2D/0CtfHcjnr6w74c\nC0ewAAAAChFYAAAAhQgsAACAQgQWAABAIQILAACgEFcRBPg1J7309HHb1sxev+MCgBONvTsAAEAh\nAgsAAKAQgQUAAFCIwAIAAChEYAEAABQisAAAAAoRWAAAAIUILAAAgEIEFgAAQCECCwAAoBCBBQAA\nUIjAAgAAKERgAQAAFCKwAAAAChFYAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQiMACAAAopLHW\nAwBj10kvPT3i2+h7tTEn9Q6M+HYAABJHsAAAAIoRWAAAAIUILAAAgEIEFgAAQCECCwAAoBCBBQAA\nUIjAAgAAKERgAQAAFCKwAAAACmms9QDAyDvppadrPQLAqDVz98Zaj3BQL55yaa1HAA7CESwAAIBC\nBBYAAEAhhxVYP/nJTzJv3rwkySuvvJLrr78+N9xwQ9rb2zM4ODiiAwIAAIwWwwbWAw88kCVLlqS3\ntzdJcs8996StrS3r1q1LtVrNhg0bRnxIAACA0WDYi1ycdtppWbVqVW6//fYkyZYtWzJ79uwkydy5\nc7Np06Zcfvnlh3yNqVNPTmPjuALjUs9aWibXegTeQd+rY/t6Nk3j6/Pf39Bf6wmopYYGZ+kfzDk/\n/36tRzhQnf6/amoame9tI/W6cCxG08+Zw34Ftba2Ztu2bUO3q9VqKpVKkmTixInp7u4ediN79rx1\nDCMyGrS0TE5X1/Brgdo4qXeg1iPUTNP4xvTV6b9/cLA+f2hj5DU0NDjFnmPW11f+e1tTU+OIvC4c\nm6a6/DnznaLviPfuv/4bt56enjQ3Nx/9VAAAACeQIw6sc845J52dnUmSjo6OzJo1q/hQAAAAo9ER\nB9bChQuzatWqXHfddenv709ra+tIzAUAADDqHNa7GKdNm5ZHH300SXL66adn7dq1IzoUAADAaOQy\nMcCY8mavC0sAACPHTxoAAACFCCwAAIBCBBYAAEAhAgsAAKAQF7mAE9BzO8bvd3vmGL6wQ0N/Mjg4\ndv/9AMDx5acOAACAQgQWAABAIQILAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAKEVgAAACFCCwA\nAIBCGms9AAAAR27m7o3FX7OhoSGDg4NH/fEvnnJpwWlgdHIECwAAoBCBBQAAUIjAAgAAKERgAQAA\nFCKwAAAAChFYAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQSGOtB2B0eW7H+IPe3/TGYPr6Dv7Y\nSHn/e3qP6/YAgEObuXtjrUc4qBdPubTWIzCGOIIFAABQiMACAAAoRGABAAAUIrAAAAAKcZGLUeCd\nLiwx1p300tO1HmE/b/bWz+8rZtZ6AACAMap+fiIEAAAY5QQWAABAIQILAACgEIEFAABQiItcMGrV\n00UlAID6NXP3xlqPwDG5otYDHBE/oQIAABQisAAAAAoRWAAAAIUILAAAgEIEFgAAQCFj+iqCJ730\ndK1HOMDbZ86p9QgAAMBRcgQLAACgkKM6gjU4OJg777wz//M//5OmpqYsX748733ve0vPBgAAMKoc\n1RGsp556Kn19fXnkkUfy+c9/Pvfee2/puQAAAEadowqsH/3oR5kz55fvFTrvvPPy/PPPFx0KAABg\nNDqqUwT37t2bSZMmDd0eN25cBgYG0th48JdraZl8dNONtJaP1nqCA0w6yH2Xthz3MY5S03He3hXH\neXsAANRC3fbEQRzVEaxJkyalp6dn6Pbg4OA7xhUAAMBYcVSB9bu/+7vp6OhIkvzXf/1XzjrrrKJD\nAQAAjEaVarVaPdIP+tVVBP/3f/831Wo1d999d84444yRmA8AAGDUOKrAAgAA4ED+0DAAAEAhAgsA\nAKAQgQUAAFCIa6tzxPr7+7N48eJs3749fX19ue2223LmmWdm0aJFqVQqmTFjRtrb29PQoN85/t54\n441cc801+Yd/+Ic0NjZal9SF1atXZ+PGjenv78/111+f2bNnW5vUVH9/fxYtWpTt27enoaEhX/rS\nl3zPpOZ+8pOf5K//+q+zZs2avPLKKwddj48++mgefvjhNDY25rbbbsuHP/zhWo99AF81HLH169dn\nypQpWbduXf7+7/8+X/rSl3LPPfekra0t69atS7VazYYNG2o9JmNQf39/li1blgkTJiSJdUld6Ozs\nzI9//ON8+9vfzpo1a7Jz505rk5r793//9wwMDOThhx/O/Pnz85WvfMW6pKYeeOCBLFmyJL29vUkO\nvg/v6urKmjVr8vDDD+fBBx/Mfffdl76+vhpPfiCBxRG74oor8tnPfjZJUq1WM27cuGzZsiWzZ89O\nksydOzebN2+u5YiMUStWrMgnPvGJvPvd704S65K68IMf/CBnnXVW5s+fn1tvvTWXXHKJtUnNnX76\n6dm3b18GBwezd+/eNDY2WpfU1GmnnZZVq1YN3T7Yenz22WfzO7/zO2lqasrkyZNz2mmn5cUXX6zV\nyO9IYHHEJk6cmEmTJmXv3r35zGc+k7a2tlSr1VQqlaHHu7u7azwlY80TTzyRU045JXPmzBm6z7qk\nHuzZsyfPP/98vvrVr+auu+7KF77wBWuTmjv55JOzffv2XHnllVm6dGnmzZtnXVJTra2taWz8/+9e\nOth63Lt3byZPnjz0nIkTJ2bv3r3HfdbheA8WR2XHjh2ZP39+brjhhnzsYx/LypUrhx7r6elJc3Nz\nDadjLHr88cdTqVTyzDPP5IUXXsjChQuze/fuocetS2plypQpmT59epqamjJ9+vSMHz8+O3fuHHrc\n2qQW/vEf/zEf+tCH8vnPfz47duzIpz71qfT39w89bl1Sa7/+/r9frcdJkyalp6dnv/t/PbjqhSNY\nHLHXX389N910UxYsWJBrr702SXLOOeeks7MzSdLR0ZFZs2bVckTGoIceeihr167NmjVrcvbZZ2fF\nihWZO3eudUnNnX/++Xn66adTrVaza9euvP3227n44outTWqqubl56AfTd73rXRkYGLAvp64cbD1+\n4AMfyI9+9KP09vamu7s7L7/8cs4666waT3qgSrVardZ6CEaX5cuX58knn8z06dOH7vviF7+Y5cuX\np7+/P9OnT8/y5cszbty4Gk7JWDZv3rzceeedaWhoyNKlS61Lau7LX/5yOjs7U61W85d/+ZeZNm2a\ntUlN9fT0ZPHixenq6kp/f39uvPHGnHvuudYlNbVt27Z87nOfy6OPPpqtW7cedD0++uijeeSRR1Kt\nVnPLLbektbW11mMfQGABAAAU4hRBAACAQgQWAABAIQILAACgEIEFAABQiMACAAAoRGABcFwNDAzk\nG9/4Rq688sp89KMfTWtra+6///4c7UVtt23blksvvfSA+5944onMnj07V199da6++upcddVV+chH\nPpKnnnrqkK/36quvZvHixUc1CwA01noAAMaWu+66K6+//noeeeSRNDc3Z+/evZk/f34mT56cT37y\nk0W3demll+bee+8duv3UU09l2bJlueyyy97xY1577bW8+uqrRecAYOwQWAAcNzt37sz69evT0dGR\n5ubmJMmkSZOybNmyvPTSS1m0aFF+/vOf55VXXsmCBQvS29ubb33rW/nFL36R3t7eLF++PBdccEH+\n+7//O1/84heTJDNnzjzs7W/fvj3vete7kiS7du3K4sWL093dna6urvzhH/5hvvCFL2T58uXZtm1b\n7rrrrrS3t+eb3/xmnnzyyezbty8f+tCHsmDBglQqlfKfHABOCE4RBOC4efbZZ3PGGWcMRc6vnHHG\nGWltbU2STJkyJU8++WQuueSSPPzww7n//vuzfv36fPrTn86DDz6YJFm4cGEWLFiQ73znO5k2bdo7\nbm/jxo25+uqr8wd/8Af54Ac/mC1btuTrX/96kuSf//mfc9VVV+XRRx/N+vXrs27duuzevTtLlizJ\nueeem/b29nR0dOT555/PY489lu9+97vZtWtX1q9fP0KfHQBOBI5gAXBc/frRn3/5l3/JN77xjQwO\nDqapqSkzZszIBz7wgSRJQ0NDvva1r2Xjxo3ZunVr/vM//zMNDQ3ZvXt3fvazn+X3fu/3kiTXXHNN\nHn/88YNu61enCO7duzd/8Rd/kVNPPTWnn356kuTmm2/Of/zHf+TBBx/M//3f/6W/vz9vv/32fh//\nzDPP5Nlnn80111yTJPnFL36RU089tfjnBIATh8AC4Lh53/vel5dffjl79+7NpEmTcsUVV+SKK67I\ntm3bcuONNyZJJkyYkCTp6enJn/zJn+Tqq6/OBRdckN/+7d/OQw89lEqlst8FMcaNGzfsdidNmpQV\nK1bkqquuypw5c3L++efn3nvvzauvvpqrrroql112WTZv3nzAhTb27duXT33qU/nzP//zJMmbb755\nWNsDYOxyiiAAx81v/uZv5o/+6I+ycOHCvPnmm0l+GTHf//7309Cw/y7ppz/9aRoaGnLrrbfmoosu\nSkdHR/bt25epU6fm1FNPzfe///0kvzzV73D81m/9VubNm5d77rkn1Wo1mzZtys0335wrr7wyO3bs\nyK5duzI4OJhx48ZlYGAgSXLRRRfln/7pn9LT05OBgYHMnz8/3/ve98p9QgA44TiCBcBxdeedd+Zb\n3/pWbrzxxlSr1fT19eW8887LAw88kNWrVw89b+bMmTn77LNz5ZVXZsKECbngggvy2muvJUlWrlyZ\nO+64I1/5yldy3nnnDX3Mhg0bsnHjxvzVX/3VQbd9yy235LHHHsv69etzyy235Pbbb09zc3N+4zd+\nI+eee262bduWs88+O93d3VmwYEFWrlyZF198MX/2Z3+Wffv2Zc6cOfn4xz8+sp8gAEa1SvVo//AI\nAAAA+3GKIAAAQCECCwAAoBCBBQAAUIjAAgAAKERgAQAAFCKwAAAAChFYAAAAhfw/Uxmav6fBtG8A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162a5bece10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(data,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Means Cluster Creation\n",
    "\n",
    "Now it is time to create the Cluster labels!\n",
    "\n",
    "** Import KMeans from SciKit Learn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Instance of a K Means model with 2 clusters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2,init='k-means++', n_init=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fitting the model to all the data except for the Private label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=20, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop('Private',axis=1)\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Cluster center vectors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.81323468e+03,   1.28716592e+03,   4.91044843e+02,\n",
       "          2.53094170e+01,   5.34708520e+01,   2.18854858e+03,\n",
       "          5.95458894e+02,   1.03957085e+04,   4.31136472e+03,\n",
       "          5.41982063e+02,   1.28033632e+03,   7.04424514e+01,\n",
       "          7.78251121e+01,   1.40997010e+01,   2.31748879e+01,\n",
       "          8.93204634e+03,   6.50926756e+01],\n",
       "       [  1.03631389e+04,   6.55089815e+03,   2.56972222e+03,\n",
       "          4.14907407e+01,   7.02037037e+01,   1.30619352e+04,\n",
       "          2.46486111e+03,   1.07191759e+04,   4.64347222e+03,\n",
       "          5.95212963e+02,   1.71420370e+03,   8.63981481e+01,\n",
       "          9.13333333e+01,   1.40277778e+01,   2.00740741e+01,\n",
       "          1.41705000e+04,   6.75925926e+01]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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, we usually won't have this luxury in the real world.\n",
    "\n",
    "** Creating 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",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['Private'].unique()\n",
    "cluster = {'Yes': 1,'No': 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['Cluster'] = data['Private'].apply(lambda x : cluster[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \\\n",
       "Abilene Christian University       18.1           12    7041         60   \n",
       "Adelphi University                 12.2           16   10527         56   \n",
       "Adrian College                     12.9           30    8735         54   \n",
       "Agnes Scott College                 7.7           37   19016         59   \n",
       "Alaska Pacific University          11.9            2   10922         15   \n",
       "\n",
       "                              Cluster  \n",
       "Abilene Christian University        1  \n",
       "Adelphi University                  1  \n",
       "Adrian College                      1  \n",
       "Agnes Scott College                 1  \n",
       "Alaska Pacific University           1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Lets create a confusion matrix and classification report to see how well the Kmeans clustering worked without being given any labels.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "avg / total       0.29      0.22      0.16       777\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(data['Cluster'],kmeans.labels_))\n",
    "print(classification_report(data['Cluster'],kmeans.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so bad considering the algorithm is purely using the features to cluster the universities into 2 distinct groups! Hopefully you can begin to see how K Means is useful for clustering un-labeled data!"
   ]
  }
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