17-K Means Clustering Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# 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 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",
    "\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 you usually use for data analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "<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",
       "  </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",
       "\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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() and describe() methods on the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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       "<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": [
    "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 0x7f38a2803bd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 418.959x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Grad.Rate', y='Room.Board', hue='Private', data=df, fit_reg=False, palette='husl')"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/renan/anaconda3/envs/tensor_env/lib/python3.7/site-packages/seaborn/regression.py:546: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f38a2803710>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 490.959x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='F.Undergrad', y='Outstate', hue='Private', data=df, fit_reg=False, palette='husl', size=6, aspect=1)"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/renan/anaconda3/envs/tensor_env/lib/python3.7/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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09w+0u4wd1Osjr6kx0Bj2azWto5L+/kaZ0iaser26Q0/2Kgo92oVm9ee5fZ8oagONbB6nc+eeMK+PR/o+9vS8PYbqe7NC1qg+guvq6srxxx+f//iP/0iSrF27Nhs3bszhhx+eGTNmZPny5UmS5cuXZ8aMGZb7AQAAE8qo7/L3sY99LJdddlkWLVqUer2exYsXZ+rUqbniiisyb968XHfddZk6dWoWLVrUjHoBAADGjVEHqkMPPTRf+cpXnrf9iCOOyC233DLawwMAAIxbE+9btwAAAGNEoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAAChJoAIAACip3u4CYE+x/6RqqkUxrH23ViqpVVpc0Eg1GiOuqVqpZP+O4T2oWhlvTxgAYPcEKhgj1aLI9utvGda+xTtOS7Fhc4srGqFKJcUwA+EfFVufzPYblg1r333ee0aZqgAA2sqSPwAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJIEKgAAgJLq7S4AAIDxb/9J1VSLot1ljGuNSiWPb2+0uwzGmEAFAMBuVYsi26+/pd1ljGuTzj+j3SXQBpb8AQAAlOQKFQDsAarVSqZ1tLuKnSv6nsq0jkq7y7BcCyhFoAKAPcE4Xq5Vr1fT39/+IGO5FlCGJX8AAAAlCVQAAAAlCVQAAAAl+Q4VAEDG9407WmGkNwOpVtp/4xAYjwQqAIBkXN+4oxVGejOQfd7rph2wM5b8AQAAlCRQAQAAlCRQAQAAlCRQAQAAlOSmFOygNrmeolK0u4xd2rx9W6pTakOOV4pKBp7qH8OKAADYUzUtUF177bX53Oc+l2XLluXII4/MPffck/nz52f79u05+OCDs2TJknR3dzfrdLRIUSlywY9WtbuMXarXa+nvHxhy/LOvnzWG1QAAsCdrypK/X/7yl7nnnnty8MEHJ0kajUYuvfTSzJ8/PytWrMjMmTNz9dVXN+NUAAAA48aoA9XTTz+dK6+8MldcccXgttWrV2fSpEmZOXNmkuSss87KbbfdNtpTAQAAjCujDlSf+cxncvrpp+eQQw4Z3LZ+/focdNBBgz93dXWl0Whky5Ytoz0dAADAuDGq71D94he/yOrVq3PJJZc0q54ddHfv25LjNltPz37tLqFpNm/flnp96Bs+jBe7qrFaq6a7Z/IYVjM8Rd9TqdeH9xnG9kpSqVRaXNHIjbimSob9nCuVyrD33ZM8uyd6tGvN7M9E7PN4f/+Mh9rGe49aYSTPd0/sz0jVatX07OZ3kIn0e+MLSSv7PqpAdeedd+a+++7LSSedlCT53e9+l3e9610555xzsm7dusH9Nm3alGq1ms7OzhEdf+PGvjQa4/uOcz09+2XDhq3tLqNpqlN2fcOH8WB3N6VoDDTG5WsyraOS/v7G8HYukqIYX+/9SqUy8pqKDPs571UUw+/PHqJer+7QEz3atWb157l9nyjG8/tnvPR8PPeoFUba9z2tP2XUBhrZvIvfQSba740vFEP1vVkha1QfM7znPe/Jj370o6xatSqrVq3KgQcemC9/+cs577zzsm3bttx1111JkptvvjmnnHJKUwoGAAAYL1ryd6iq1WoWL16cBQsW7HDbdAAAgImkqYFq1ao//f2iY489NsuWLWvm4QEAAMYV3ywEAAAoSaACAAAoSaACAAAoqSU3pYB2qlYryZTx97e0tlYqKd5x2rD2Labs0+JqAABoBoGKCaeR5IIfrdrtfmOtVkmKDZuHte81b/6rFlcDAEAzWPIHAABQkkAFAABQkkAFAABQkkAFAABQkkAFAABQkkAFAABQkkAFAABQkkAFAABQkkAFAABQUr3dBQATV2XKPtn+jtOGte/Te9VSDHPfsVTr+33qX7u93WUAAOOUQAW0TKNSyYdu/96w9q32TEtjw+YWVzRynzn5TSZKAGBIlvwBAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUJFABAACUVG93AUwMtUolSTE2J2s0UqvsepfdjbdDJZWx6hAAAGNEoKJJihQbNo/NqSqVFMUuokljDGsZgUrPtHaXAABAk1nyBwAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUJJABQAAUFJ9NA/evHlzPvzhD+fBBx9MR0dHDjvssFx55ZXp6urKPffck/nz52f79u05+OCDs2TJknR3dzerbgAAgLYb1RWqSqWS8847LytWrMiyZcty6KGH5uqrr06j0cill16a+fPnZ8WKFZk5c2auvvrqZtUMAAAwLowqUHV2dub4448f/PmYY47JunXrsnr16kyaNCkzZ85Mkpx11lm57bbbRlcpAADAODOqJX/P1mg08tWvfjWzZs3K+vXrc9BBBw2OdXV1pdFoZMuWLens7Bz2Mbu7921WeS3V07Nfu0toms3bt6Ver438gY1GUqk0v6AhVHZzrt2Nt0dlRHWNx+dQpqbhP2Zk/RkzlaReb9/XTZ997kql0tZaxrtm9mci9nm8v3/GQ23jvUetMJLnuyf2Z6RqtWp6eibvcp+J9HvjC0kr+960QHXVVVdl8uTJedvb3pbvf//7TTnmxo19aTSKphyrVXp69suGDVvbXUbTVKfU0t8/MOLH1SpJUYzNa1WpVHZ7rrGqZSQqKUZU13h7DsPp+84M9zEj7c+YKZL+/kZbTl2vV3c4915F0bZaXgia1Z/n9n2iGM/vn/HS8/Hco1YYad/3tP6UURtoZPMufi+caL83vlAM1fdmhaymBKpFixblgQceyOc///lUq9X09vZm3bp1g+ObNm1KtVod0dUpAACA8W7U120//elPZ/Xq1Vm6dGk6OjqSJEcffXS2bduWu+66K0ly880355RTThntqQAAAMaVUV2h+s1vfpPrr78+hx9+eM4666wkySGHHJKlS5dm8eLFWbBgwQ63TQcAAJhIRhWoXvKSl+Tee+/d6dixxx6bZcuWjebwAAAA45pbtQAAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJQkUAEAAJRUb3cBE0ltcj1FpWh3GaNTqbS7AgAAeMEQqJqoqBS54Eer2l3GqHz2xJPaXQIAALxgWPIHAABQkitUABNc/1+fnIF992l3GaNS6/t96l+7vd1lAMDzCFQAE9zAvvvkwtu/1+4yRuUzJ7/Jf1gAjEuW/AEAAJQkUAEAAJQkUAEAAJRkSTrALlSm7JPt7zitLefeXknyrD9t9/RetRQlaimmvLBvSAEA45lABbALjUolH2rTDR0qlUqK4k+JqtozLY0Nm0d8nGve/FfNLAsAeBZL/gAAAEpyhQoAAJqgWq1kWsfQ40XfU5nWURm7gsaZRqWSx7c32l1G0wlUAADQDEWR7dffMuRwvV5Nf//ECxTDNen8M9pdQktY8gcAAFCSQAUAAFCSQAUAAFCSQAUAAFCSQAUAAFCSQAUAAFCSQAUAAFCSQAUAAFCSQAUAAFBSvd0FAMDuVKbsk+3vOG2X+zy9Vy3FbvYZju2VJMWoD/M8tb7fp/6125t/4DHW/9cnZ2DffZp6zFb1fCgT5bUAxgeBCoBxr1Gp5EO3f2+X+1R7pqWxYfOoz1WpVFIUzf/t/jMnv2lC/Kc7sO8+uXA3r8VItarnQ5korwUwPrR0yd/atWtz5plnZvbs2TnzzDNz//33t/J0AAAAY6qlH9AsWLAgZ599dubOnZtvf/vbmT9/fm688cZWnrJl9p9UTXUnn54VfU9lWkclSbK1UkmtMtaVNV+Z51BJZSxXawC84Axn2WIrNWtJZDGlucv9AF7oWhaoNm7cmDVr1uSf/umfkiRz5szJVVddlU2bNqWrq2tYx6hWx086qSZ5+qvPX+JQq1cy0P9MlKj89UnpfurpMa6suapFSj2Hatf+aUyZ0oKKnm93S0OqlUpeNEa1jER1733SmDK83o7H51BmSc5InsdI+jOW2vlaPLfnZXs0Ht9PIzWc59Cs91DLlp9Vq7nqP+9o/nGHe/qu/dPY9PiojzP/jW9q+vtprJf8VavVVKbuu7NCdr59oqpXUukfQd/3tP6UsbsejbTnE02lkmqbbonXylxRKVo0g61evTof+chHcuuttw5uO/XUU7NkyZK8/OUvb8UpAQAAxpTbpgMAAJTUskDV29ubRx55JAMDA0mSgYGBPProo+nt7W3VKQEAAMZUywJVd3d3ZsyYkeXLlydJli9fnhkzZgz7+1MAAADjXcu+Q5Uk9913X+bNm5cnnngiU6dOzaJFi/LiF7+4VacDAAAYUy0NVAAAABOZm1IAAACUJFABAACUJFABAACUJFABAACUVG93AePJ5s2b8+EPfzgPPvhgOjo6cthhh+XKK69MV1dXjjrqqBx55JGpVp/JoIsXL85RRx2VJFm1alUWL16cgYGBvPzlL88nP/nJ7LPPPrsd4xmzZs1KR0dHJk2alCS55JJLcsIJJ+See+7J/Pnzs3379hx88MFZsmRJuru7k6T0GM94+OGH8/73v3/w561bt6avry8/+9nPhnw9En0fqUWLFmXFihX57W9/m2XLluXII49Mkqxduzbz5s3Lli1b0tnZmUWLFuXwww9v2dieZmd939X8nsQcP0pDvddbMZ+Ya/5kZ33f1fyetOY12ZPsai5pxXta358xVN8ff/zxzJ8/Pxs2bEi9Xs8rXvGKLFiwIHvvvXcefvjhvPGNb8xLXvKSwePccMMNmTZtWpLkX//1X/PFL34xRVHkxBNPzOWXXz74f8CuxnaqYNDmzZuLn/zkJ4M/f+pTnyr+/u//viiKojjyyCOLvr6+5z2mr6+veO1rX1usXbu2KIqiuOyyy4rPfe5zux3jT/7f//t/xb333rvDtoGBgeLkk08u7rzzzqIoimLp0qXFvHnzRjXG0BYuXFh87GMfK4pi569HUeh7GXfeeWexbt265/X0nHPOKb71rW8VRVEU3/rWt4pzzjmnpWN7mp31fVfze1GY40drqPd6s+cTc82Ohur7sz17fi8Kc/xoDTWXtOI9re9/MlTfH3rooeKXv/xlURTP9OvCCy8srr322qIoiuKhhx4qXvOa1+z0eA8++GBxwgknFBs3biwGBgaKc889t/jmN7+527GhWPL3LJ2dnTn++OMHfz7mmGOybt26XT7mjjvuyNFHHz34SfBZZ52V733ve7sdY9dWr16dSZMmZebMmUme6d1tt902qjF27umnn86yZcvyV3/1V7vcT99HbubMment7d1h28aNG7NmzZrMmTMnSTJnzpysWbMmmzZtasnYnmhnfS8zvyfm+OHaWc93xRzfHLvr+3Dn90Tfh2uouaQV72l9/5Oh+n7IIYfkZS97WZKkWq3mz//8z4c1t69YsSInn3xyurq6Uq1Wc8YZZ+S73/3ubseGYsnfEBqNRr761a9m1qxZg9vOOeecDAwM5MQTT8wHP/jBdHR0ZP369TnooIMG9znooIOyfv36JNnlGDu65JJLUhRFjjvuuFx88cXP611XV1cajUa2bNlSeqyzs3NMn9MLxapVq3LAAQfk5S9/+eC2574eU6dO1fcmWb9+fQ444IDUarUkSa1Wy/Tp07N+/foURdH0sT8uaeNPdja/J+b4VmnmfGKuGZmdze+JOb5Znj2XtOI9re87N9Qcvm3btnz961/PxRdfPLjtySefzFvf+tYkyamnnpp3vetdqVQqTZ/bXaEawlVXXZXJkyfnbW97W5LkBz/4Qb7xjW/kpptuyv/+7/9m6dKlba5w4rjpppvyne98J1//+tdTFBgjgrEAAAiHSURBVEWuvPLKdpe0R/n617++w6eXXg8muufO74k5vlXMJ+313Pk98Zo0087mElpvZ33v7+/PRRddlL/4i7/ISSedlCSZPn16fvjDH+Yb3/hGvvjFL2blypX52te+1pKaBKqdWLRoUR544IFcc801g19A++Ml9X333TdnnHFG7r777sHtz760uG7dusF9dzXGn/yxJx0dHTn77LNz9913P693mzZtSrVaTWdnZ+kxnu+RRx7JnXfemdNOO21w285ejz9u1/fR6+3tzSOPPJKBgYEkycDAQB599NH09va2ZIwd7Wx+T8zxrdLs+cRcM3w7m98Tc3yzPHcuacV7Wt+fb2dz+MDAQC655JLsv//+ufzyywf37ejoGLyBR3d3d0477bSWze0C1XN8+tOfzurVq7N06dJ0dHQkSR5//PFs27YtyTMJeMWKFZkxY0aS5IQTTsh///d/5/7770+S3HzzzXnTm9602zGe8dRTT2Xr1q1JkqIo8t3vfjczZszI0UcfnW3btuWuu+5K8kzvTjnllCQpPcbzffOb38wb3vCGwTveDPV6JPreLN3d3ZkxY0aWL1+eJFm+fHlmzJiRrq6ulozxJzub3xNzfKu0Yj4x1wzfc+f3xBzfLDubS1rxntb3He2s741GI/PmzUutVsvHP/7xVCqVwf03btyYP/zhD0mS3//+91m1alVe+tKXJklmz56d22+/PZs2bUqj0cgtt9wyOH/vamwolaIoilY86Rei3/zmN5kzZ04OP/zw7L333kmSQw45JOedd17mz5+fSqWS/v7+vOpVr8pll12WKVOmJEluv/32LFmyJI1GIzNmzMinPvWpTJ48ebdjJA899FA++MEPZmBgII1GI0cccUQuv/zyTJ8+PXfffXcWLFiww61CX/SiFyVJ6TF2NHv27Hz0ox/NiSeemGTXr0ei7yO1cOHCrFy5Mo899limTZuWzs7O3Hrrrbnvvvsyb968PPHEE5k6dWoWLVqUF7/4xUnSkrE9zc76fs011+x0fl+6dGl+8YtfmONHaWc9//znP9+S+cRc8ydDzTHJ8+f3xBzfDEP9rrh06dKWvKf1/RlD9f2MM87I+eefv8OfvTj22GOzYMGCrFy5Mp/97GdTrVbT39+fv/zLv8zf/d3fDX7f+Oabb86XvvSlJMnrXve6zJ8/f1hjOyNQAQAAlGTJHwAAQEkCFQAAQEkCFQAAQEkCFQAAQEkCFQAAQEkCFQAAQEkCFQBj4hvf+EZOO+20vPKVr8zrXve6LFiwIE888cSwHjtr1qz8+Mc/Hta+55xzTm655ZZh1/W5z30ul1xyybD3B4BnE6gAaLl//Md/zNVXX51LL700d911V/7lX/4l69atyzvf+c48/fTT7S4PAErzh30BaKm+vr6ccMIJ+fjHP55TTz11cPuTTz6Zk046KZdccknuuuuuHHDAAbnooouSJD/96U9z6aWX5o477sill16aZcuWpaOjI7VaLe973/vy9re/PR/96Efz7//+7xkYGMhhhx2W66+/Pl/5ylfyhS98IfV6PfV6PW95y1syf/78LFy4MN///vezdevWHH744bnssssyc+bM3HHHHXnf+96XoijS0dGRQw89NN/5zneydevWfPKTn8wdd9yRSqWSt771rbngggtSq9Xa1UYAxql6uwsAYGK7++67s3379rzxjW/cYfuUKVPyhje8IT/+8Y/T0dEx5OOXLFmSn//851m4cGFe+9rXJkluvvnm9PX15Qc/+EE6Ojryq1/9KnvvvXcuuuii3H333Tn99NNzxhlnDB7jFa94Rd7//vdnv/32y4033pgLL7wwq1atyoknnpjzzz8/DzzwQK6++urB/efNm5fu7u6sXLkyv//973P++eent7c3Z511VpO7A8ALnSV/ALTU5s2bM23atNTrz/8Mr6enJ5s3bx7xMev1erZs2ZIHHnggtVotRx99dPbdd98h9587d+5gDeeee26efvrprF27dqf7PvbYY/nhD3+Yyy67LJMnT053d3fe8Y535NZbbx1xnQBMfK5QAdBS06ZNy+bNm9Pf3/+8ULVhw4ZMmzZtxMecO3dufve73+Xiiy/OE088kdNPPz0XXXRR9tprr53u/+Uvfzlf+9rX8uijj6ZSqaSvr2/IILdu3br09/fn9a9//eC2RqOR3t7eEdcJwMQnUAHQUq961avS0dGRlStXPu87VHfccUcuvvjirFmzJtu2bRsce+yxx3Z5zL322isf+MAH8oEPfCAPP/xw3vOe9+TP/uzPdljm90d33XVXvvSlL+WGG27IS17yklSr1bz61a/OH79CXKlUdtj/wAMPTEdHR37yk5/s9KoaADybJX8AtNR+++2X97///Vm4cGHuuOOO/OEPf8jDDz+cD33oQznwwAMzd+7czJgxIz/84Q+zZcuWbNiwIf/8z/+8wzFe9KIX5aGHHhr8+Sc/+UnuvffeDAwMZN999029Xk+1Wt3pvk8++WRqtVq6urrS39+fa6+9Nn19fYPj3d3d+e1vf5tGo5EkmT59el73utflU5/6VPr6+tJoNPLggw/mZz/7WSvbBMALlEAFQMu9+93vzkUXXZTFixfnuOOOy9/8zd+kt7c3N9xwQzo6OjJ37ty89KUvzaxZs3LuuefucCUrSd7znvfkH/7hHzJz5sx8+ctfzmOPPZYLLrggxx13XE499dS85jWvydy5c5Mkb3/727NixYq8+tWvzsKFC/P6178+J5xwQmbPnp1Zs2Zl0qRJOyzfO+WUU5Ikxx9/fN7ylrckSRYvXpw//OEPOfXUU/PqV786F1xwQTZs2DBG3QLghcRt0wEAAEpyhQoAAKAkgQoAAKAkgQoAAKAkgQoAAKAkgQoAAKAkgQoAAKAkgQoAAKAkgQoAAKAkgQoAAKCk/w+qLm5xIfA+2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.FacetGrid(df, hue=\"Private\", size=6, aspect=2, palette='husl')\n",
    "g = g.map(plt.hist, \"Outstate\", alpha=0.8)"
   ]
  },
  {
   "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": [
    "g=sns.FacetGrid(df, hue=\"Private\", size=6, aspect=2, palette='husl')\n",
    "g = g.map(plt.hist, \"Grad.Rate\",alpha=0.8)"
   ]
  },
  {
   "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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df.loc[df[\"Grad.Rate\"] > 100, 'Grad.Rate'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.FacetGrid(df, hue=\"Private\", height=6, aspect=2, palette='husl')\n",
    "g = g.map(plt.hist, \"Grad.Rate\",alpha=0.8, bins=20)"
   ]
  },
  {
   "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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def convert_to(cluster):\n",
    "    if cluster=='Yes':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cluster'] = df['Private'].apply(convert_to)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>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": [
    "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f38a084d850>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(df['Cluster'],kmeans.labels_),annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.21      0.65      0.31       212\n",
      "           1       0.31      0.06      0.10       565\n",
      "\n",
      "    accuracy                           0.22       777\n",
      "   macro avg       0.26      0.36      0.21       777\n",
      "weighted avg       0.29      0.22      0.16       777\n",
      "\n"
     ]
    }
   ],
   "source": [
    "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!\n",
    "\n",
    "## Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}