2 Data Mining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "'Author: hernanda472@gmail.com'\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abilene Christian University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adelphi University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes Scott College</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska Pacific University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Worcester State College</th>\n",
       "      <td>No</td>\n",
       "      <td>2197</td>\n",
       "      <td>1515</td>\n",
       "      <td>543</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>3089</td>\n",
       "      <td>2029</td>\n",
       "      <td>6797</td>\n",
       "      <td>3900</td>\n",
       "      <td>500</td>\n",
       "      <td>1200</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14</td>\n",
       "      <td>4469</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xavier University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1959</td>\n",
       "      <td>1805</td>\n",
       "      <td>695</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "      <td>2849</td>\n",
       "      <td>1107</td>\n",
       "      <td>11520</td>\n",
       "      <td>4960</td>\n",
       "      <td>600</td>\n",
       "      <td>1250</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>13.3</td>\n",
       "      <td>31</td>\n",
       "      <td>9189</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xavier University of Louisiana</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2097</td>\n",
       "      <td>1915</td>\n",
       "      <td>695</td>\n",
       "      <td>34</td>\n",
       "      <td>61</td>\n",
       "      <td>2793</td>\n",
       "      <td>166</td>\n",
       "      <td>6900</td>\n",
       "      <td>4200</td>\n",
       "      <td>617</td>\n",
       "      <td>781</td>\n",
       "      <td>67</td>\n",
       "      <td>75</td>\n",
       "      <td>14.4</td>\n",
       "      <td>20</td>\n",
       "      <td>8323</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yale University</th>\n",
       "      <td>Yes</td>\n",
       "      <td>10705</td>\n",
       "      <td>2453</td>\n",
       "      <td>1317</td>\n",
       "      <td>95</td>\n",
       "      <td>99</td>\n",
       "      <td>5217</td>\n",
       "      <td>83</td>\n",
       "      <td>19840</td>\n",
       "      <td>6510</td>\n",
       "      <td>630</td>\n",
       "      <td>2115</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>5.8</td>\n",
       "      <td>49</td>\n",
       "      <td>40386</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>York College of Pennsylvania</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2989</td>\n",
       "      <td>1855</td>\n",
       "      <td>691</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>2988</td>\n",
       "      <td>1726</td>\n",
       "      <td>4990</td>\n",
       "      <td>3560</td>\n",
       "      <td>500</td>\n",
       "      <td>1250</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>18.1</td>\n",
       "      <td>28</td>\n",
       "      <td>4509</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>777 rows × 18 columns</p>\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",
       "Worcester State College             No   2197    1515     543          4   \n",
       "Xavier University                  Yes   1959    1805     695         24   \n",
       "Xavier University of Louisiana     Yes   2097    1915     695         34   \n",
       "Yale University                    Yes  10705    2453    1317         95   \n",
       "York College of Pennsylvania       Yes   2989    1855     691         28   \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",
       "Worcester State College                26         3089         2029      6797   \n",
       "Xavier University                      47         2849         1107     11520   \n",
       "Xavier University of Louisiana         61         2793          166      6900   \n",
       "Yale University                        99         5217           83     19840   \n",
       "York College of Pennsylvania           63         2988         1726      4990   \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",
       "Worcester State College               3900    500      1200   60        60   \n",
       "Xavier University                     4960    600      1250   73        75   \n",
       "Xavier University of Louisiana        4200    617       781   67        75   \n",
       "Yale University                       6510    630      2115   96        96   \n",
       "York College of Pennsylvania          3560    500      1250   75        75   \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",
       "Worcester State College              21.0           14    4469         40  \n",
       "Xavier University                    13.3           31    9189         83  \n",
       "Xavier University of Louisiana       14.4           20    8323         49  \n",
       "Yale University                       5.8           49   40386         99  \n",
       "York College of Pennsylvania         18.1           28    4509         99  \n",
       "\n",
       "[777 rows x 18 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load Dataset\n",
    "df=pd.read_csv(\"2 Dataset.csv\",index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUSTERING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nanda\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Nanda\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\regression.py:581: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c411cd5ab0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 484.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Room.Board','Grad.Rate',data=df, hue='Private',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nanda\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Nanda\\AppData\\Roaming\\Python\\Python310\\site-packages\\seaborn\\regression.py:581: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c411ae3280>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 672.625x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Outstate','F.Undergrad',data=df, hue='Private',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLASSIFICATION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans=KMeans(n_clusters=2)\n",
    "kmeans.fit(df.drop('Private',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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],\n",
       "       [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",
       "        6.51195815e+01]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 74 138]\n",
      " [ 34 531]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.35      0.46       212\n",
      "           1       0.79      0.94      0.86       565\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": [
    "def converter(cluster):\n",
    "    if cluster=='Yes':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df['Cluster'] = df['Private'].apply(converter)\n",
    "print(confusion_matrix(df['Cluster'],kmeans.labels_))\n",
    "print(classification_report(df['Cluster'],kmeans.labels_))"
   ]
  }
 ],
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