21 K Means Clustering - Public and Private School.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Means Clustering - Public and Private School"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/publicprivate.jpg\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public.\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 you 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",
    "## 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Data\n",
    "**Read in the College_Data file using read_csv. Figure out how to set the first column as the index.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/College_Data',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the head of the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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",
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       "  </tbody>\n",
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      "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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the info() and describe() methods on the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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",
      " #   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: 115.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "It's time to create some data visualizations!\n",
    "\n",
    "**Create a scatterplot of Grad.Rate versus Room.Board where the points are colored by the Private column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x199dec2dd00>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 484.375x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "\n",
    "sns.lmplot(x='Room.Board', y='Grad.Rate', data=df, hue='Private', palette='coolwarm', height=6, aspect=1, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a scatterplot of F.Undergrad versus Outstate where the points are colored by the Private column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x199ded6afd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 484.375x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "\n",
    "sns.lmplot(x='Outstate', y='F.Undergrad', data=df, hue='Private', palette='coolwarm', height=6, aspect=1, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a stacked histogram showing Out of State Tuition based on the Private column. Try doing this using [sns.FacetGrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html). If that is too tricky, see if you can do it just by using two instances of pandas.plot(kind='hist').**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "\n",
    "g = sns.FacetGrid(df, hue=\"Private\", palette='coolwarm', height=6, aspect=2)\n",
    "g = g.map(plt.hist,'Outstate', bins=20, alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a similar histogram for the Grad.Rate column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "\n",
    "g = sns.FacetGrid(df, hue=\"Private\", palette='coolwarm', height=6, aspect=2)\n",
    "g = g.map(plt.hist,'Grad.Rate', bins=20, alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "df[df['Grad.Rate'] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-bc95ac68ab2d>: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",
      "  df['Grad.Rate']['Cazenovia College'] = 100\n"
     ]
    }
   ],
   "source": [
    "df['Grad.Rate']['Cazenovia College'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "df[df['Grad.Rate'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "\n",
    "g = sns.FacetGrid(df, hue=\"Private\", palette='coolwarm', height=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": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create an instance of a K Means model with 2 clusters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit the model to all the data except for the Private label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(df.drop('Private',axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the cluster center vectors?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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]])"
      ]
     },
     "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, 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",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function that transforms 'Yes' in 0, and 'No' in 1.\n",
    "The reason that was 0 & 1 in opposition with 1 & 0 is because \"our cluster model\" inverted the 0 and 1 for 'private'.\n",
    "'''\n",
    "\n",
    "def converter(private):\n",
    "    if private=='Yes':\n",
    "        return 0\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</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>0</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>0</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>0</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>0</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        0  \n",
       "Adelphi University                  0  \n",
       "Adrian College                      0  \n",
       "Agnes Scott College                 0  \n",
       "Alaska Pacific University           0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create another binary column ('Cluster') based in the 'Private' column.\n",
    "df['Cluster'] = df['Private'].apply(converter)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[531  34]\n",
      " [138  74]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.94      0.86       565\n",
      "           1       0.69      0.35      0.46       212\n",
      "\n",
      "    accuracy                           0.78       777\n",
      "   macro avg       0.74      0.64      0.66       777\n",
      "weighted avg       0.76      0.78      0.75       777\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(df['Cluster'],kmeans.labels_))\n",
    "print(classification_report(df['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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