Project6_5.ipynb

{
 "cells": [
  {
   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./project/College_Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": 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>Unnamed: 0</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>0</th>\n",
       "      <td>Abilene Christian University</td>\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>1</th>\n",
       "      <td>Adelphi University</td>\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>2</th>\n",
       "      <td>Adrian College</td>\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>3</th>\n",
       "      <td>Agnes Scott College</td>\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>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Unnamed: 0 Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "0  Abilene Christian University     Yes  1660    1232     721         23   \n",
       "1            Adelphi University     Yes  2186    1924     512         16   \n",
       "2                Adrian College     Yes  1428    1097     336         22   \n",
       "3           Agnes Scott College     Yes   417     349     137         60   \n",
       "4     Alaska Pacific University     Yes   193     146      55         16   \n",
       "\n",
       "   Top25perc  F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  Personal  \\\n",
       "0         52         2885          537      7440        3300    450      2200   \n",
       "1         29         2683         1227     12280        6450    750      1500   \n",
       "2         50         1036           99     11250        3750    400      1165   \n",
       "3         89          510           63     12960        5450    450       875   \n",
       "4         44          249          869      7560        4120    800      1500   \n",
       "\n",
       "   PhD  Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0   70        78       18.1           12    7041         60  \n",
       "1   29        30       12.2           16   10527         56  \n",
       "2   53        66       12.9           30    8735         54  \n",
       "3   92        97        7.7           37   19016         59  \n",
       "4   76        72       11.9            2   10922         15  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Private.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</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>0</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>1</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>2</th>\n",
       "      <td>Adrian College</td>\n",
       "      <td>1</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>3</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>1</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>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Worcester State College</td>\n",
       "      <td>0</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>773</th>\n",
       "      <td>Xavier University</td>\n",
       "      <td>1</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>774</th>\n",
       "      <td>Xavier University of Louisiana</td>\n",
       "      <td>1</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>775</th>\n",
       "      <td>Yale University</td>\n",
       "      <td>1</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>776</th>\n",
       "      <td>York College of Pennsylvania</td>\n",
       "      <td>1</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 × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Unnamed: 0  Private   Apps  Accept  Enroll  \\\n",
       "0      Abilene Christian University        1   1660    1232     721   \n",
       "1                Adelphi University        1   2186    1924     512   \n",
       "2                    Adrian College        1   1428    1097     336   \n",
       "3               Agnes Scott College        1    417     349     137   \n",
       "4         Alaska Pacific University        1    193     146      55   \n",
       "..                              ...      ...    ...     ...     ...   \n",
       "772         Worcester State College        0   2197    1515     543   \n",
       "773               Xavier University        1   1959    1805     695   \n",
       "774  Xavier University of Louisiana        1   2097    1915     695   \n",
       "775                 Yale University        1  10705    2453    1317   \n",
       "776    York College of Pennsylvania        1   2989    1855     691   \n",
       "\n",
       "     Top10perc  Top25perc  F.Undergrad  P.Undergrad  Outstate  Room.Board  \\\n",
       "0           23         52         2885          537      7440        3300   \n",
       "1           16         29         2683         1227     12280        6450   \n",
       "2           22         50         1036           99     11250        3750   \n",
       "3           60         89          510           63     12960        5450   \n",
       "4           16         44          249          869      7560        4120   \n",
       "..         ...        ...          ...          ...       ...         ...   \n",
       "772          4         26         3089         2029      6797        3900   \n",
       "773         24         47         2849         1107     11520        4960   \n",
       "774         34         61         2793          166      6900        4200   \n",
       "775         95         99         5217           83     19840        6510   \n",
       "776         28         63         2988         1726      4990        3560   \n",
       "\n",
       "     Books  Personal  PhD  Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0      450      2200   70        78       18.1           12    7041         60  \n",
       "1      750      1500   29        30       12.2           16   10527         56  \n",
       "2      400      1165   53        66       12.9           30    8735         54  \n",
       "3      450       875   92        97        7.7           37   19016         59  \n",
       "4      800      1500   76        72       11.9            2   10922         15  \n",
       "..     ...       ...  ...       ...        ...          ...     ...        ...  \n",
       "772    500      1200   60        60       21.0           14    4469         40  \n",
       "773    600      1250   73        75       13.3           31    9189         83  \n",
       "774    617       781   67        75       14.4           20    8323         49  \n",
       "775    630      2115   96        96        5.8           49   40386         99  \n",
       "776    500      1250   75        75       18.1           28    4509         99  \n",
       "\n",
       "[777 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Private'] = df.Private.apply(lambda x: 1 if x=='Yes' else 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 777 entries, 0 to 776\n",
      "Data columns (total 19 columns):\n",
      "Unnamed: 0     777 non-null object\n",
      "Private        777 non-null int64\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(17), object(1)\n",
      "memory usage: 115.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.Private\n",
    "x = df.drop('Private', axis=1).drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "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>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>0</th>\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>1</th>\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>2</th>\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>3</th>\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>4</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\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>773</th>\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>774</th>\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>775</th>\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>776</th>\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 × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Apps  Accept  Enroll  Top10perc  Top25perc  F.Undergrad  P.Undergrad  \\\n",
       "0     1660    1232     721         23         52         2885          537   \n",
       "1     2186    1924     512         16         29         2683         1227   \n",
       "2     1428    1097     336         22         50         1036           99   \n",
       "3      417     349     137         60         89          510           63   \n",
       "4      193     146      55         16         44          249          869   \n",
       "..     ...     ...     ...        ...        ...          ...          ...   \n",
       "772   2197    1515     543          4         26         3089         2029   \n",
       "773   1959    1805     695         24         47         2849         1107   \n",
       "774   2097    1915     695         34         61         2793          166   \n",
       "775  10705    2453    1317         95         99         5217           83   \n",
       "776   2989    1855     691         28         63         2988         1726   \n",
       "\n",
       "     Outstate  Room.Board  Books  Personal  PhD  Terminal  S.F.Ratio  \\\n",
       "0        7440        3300    450      2200   70        78       18.1   \n",
       "1       12280        6450    750      1500   29        30       12.2   \n",
       "2       11250        3750    400      1165   53        66       12.9   \n",
       "3       12960        5450    450       875   92        97        7.7   \n",
       "4        7560        4120    800      1500   76        72       11.9   \n",
       "..        ...         ...    ...       ...  ...       ...        ...   \n",
       "772      6797        3900    500      1200   60        60       21.0   \n",
       "773     11520        4960    600      1250   73        75       13.3   \n",
       "774      6900        4200    617       781   67        75       14.4   \n",
       "775     19840        6510    630      2115   96        96        5.8   \n",
       "776      4990        3560    500      1250   75        75       18.1   \n",
       "\n",
       "     perc.alumni  Expend  Grad.Rate  \n",
       "0             12    7041         60  \n",
       "1             16   10527         56  \n",
       "2             30    8735         54  \n",
       "3             37   19016         59  \n",
       "4              2   10922         15  \n",
       "..           ...     ...        ...  \n",
       "772           14    4469         40  \n",
       "773           31    9189         83  \n",
       "774           20    8323         49  \n",
       "775           49   40386         99  \n",
       "776           28    4509         99  \n",
       "\n",
       "[777 rows x 17 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.46881819e-01, -3.21205453e-01, -6.35089011e-02, ...,\n",
       "        -8.67574189e-01, -5.01910084e-01, -3.18251941e-01],\n",
       "       [-2.10884040e-01, -3.87029908e-02, -2.88584214e-01, ...,\n",
       "        -5.44572203e-01,  1.66109850e-01, -5.51261842e-01],\n",
       "       [-4.06865631e-01, -3.76317928e-01, -4.78121319e-01, ...,\n",
       "         5.85934748e-01, -1.77289956e-01, -6.67766793e-01],\n",
       "       ...,\n",
       "       [-2.33895071e-01, -4.23771558e-02, -9.15087008e-02, ...,\n",
       "        -2.21570217e-01, -2.56241250e-01, -9.59029170e-01],\n",
       "       [ 1.99171118e+00,  1.77256262e-01,  5.78332661e-01, ...,\n",
       "         2.12019418e+00,  5.88797079e+00,  1.95359460e+00],\n",
       "       [-3.26765760e-03, -6.68715889e-02, -9.58163623e-02, ...,\n",
       "         4.24433755e-01, -9.87115613e-01,  1.95359460e+00]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x = sc.fit_transform(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88        38\n",
      "           1       0.96      0.97      0.96       118\n",
      "\n",
      "    accuracy                           0.94       156\n",
      "   macro avg       0.92      0.92      0.92       156\n",
      "weighted avg       0.94      0.94      0.94       156\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log = LogisticRegression()\n",
    "log.fit(x_train, y_train)\n",
    "y_pred = log.predict(x_test)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>0</th>\n",
       "      <td>1</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>1</th>\n",
       "      <td>1</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>2</th>\n",
       "      <td>1</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>3</th>\n",
       "      <td>1</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>4</th>\n",
       "      <td>1</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>772</th>\n",
       "      <td>0</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>773</th>\n",
       "      <td>1</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>774</th>\n",
       "      <td>1</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>775</th>\n",
       "      <td>1</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>776</th>\n",
       "      <td>1</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  Top25perc  F.Undergrad  \\\n",
       "0          1   1660    1232     721         23         52         2885   \n",
       "1          1   2186    1924     512         16         29         2683   \n",
       "2          1   1428    1097     336         22         50         1036   \n",
       "3          1    417     349     137         60         89          510   \n",
       "4          1    193     146      55         16         44          249   \n",
       "..       ...    ...     ...     ...        ...        ...          ...   \n",
       "772        0   2197    1515     543          4         26         3089   \n",
       "773        1   1959    1805     695         24         47         2849   \n",
       "774        1   2097    1915     695         34         61         2793   \n",
       "775        1  10705    2453    1317         95         99         5217   \n",
       "776        1   2989    1855     691         28         63         2988   \n",
       "\n",
       "     P.Undergrad  Outstate  Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "0            537      7440        3300    450      2200   70        78   \n",
       "1           1227     12280        6450    750      1500   29        30   \n",
       "2             99     11250        3750    400      1165   53        66   \n",
       "3             63     12960        5450    450       875   92        97   \n",
       "4            869      7560        4120    800      1500   76        72   \n",
       "..           ...       ...         ...    ...       ...  ...       ...   \n",
       "772         2029      6797        3900    500      1200   60        60   \n",
       "773         1107     11520        4960    600      1250   73        75   \n",
       "774          166      6900        4200    617       781   67        75   \n",
       "775           83     19840        6510    630      2115   96        96   \n",
       "776         1726      4990        3560    500      1250   75        75   \n",
       "\n",
       "     S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0         18.1           12    7041         60  \n",
       "1         12.2           16   10527         56  \n",
       "2         12.9           30    8735         54  \n",
       "3          7.7           37   19016         59  \n",
       "4         11.9            2   10922         15  \n",
       "..         ...          ...     ...        ...  \n",
       "772       21.0           14    4469         40  \n",
       "773       13.3           31    9189         83  \n",
       "774       14.4           20    8323         49  \n",
       "775        5.8           49   40386         99  \n",
       "776       18.1           28    4509         99  \n",
       "\n",
       "[777 rows x 18 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = df.drop('Unnamed: 0', axis = 1)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.12553050e-01, -3.46881819e-01, -3.21205453e-01, ...,\n",
       "        -8.67574189e-01, -5.01910084e-01, -3.18251941e-01],\n",
       "       [ 6.12553050e-01, -2.10884040e-01, -3.87029908e-02, ...,\n",
       "        -5.44572203e-01,  1.66109850e-01, -5.51261842e-01],\n",
       "       [ 6.12553050e-01, -4.06865631e-01, -3.76317928e-01, ...,\n",
       "         5.85934748e-01, -1.77289956e-01, -6.67766793e-01],\n",
       "       ...,\n",
       "       [ 6.12553050e-01, -2.33895071e-01, -4.23771558e-02, ...,\n",
       "        -2.21570217e-01, -2.56241250e-01, -9.59029170e-01],\n",
       "       [ 6.12553050e-01,  1.99171118e+00,  1.77256262e-01, ...,\n",
       "         2.12019418e+00,  5.88797079e+00,  1.95359460e+00],\n",
       "       [ 6.12553050e-01, -3.26765760e-03, -6.68715889e-02, ...,\n",
       "         4.24433755e-01, -9.87115613e-01,  1.95359460e+00]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc2 = StandardScaler()\n",
    "z_std = sc2.fit_transform(z)\n",
    "z_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans \n",
    "km = KMeans(n_clusters = 2, init='random',  n_init = 10, max_iter=300, tol=1e-04)\n",
    "z_km = km.fit_predict(z_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Unnamed: 0</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>0</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>1</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>2</th>\n",
       "      <td>Adrian College</td>\n",
       "      <td>1</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>3</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>1</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>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Unnamed: 0  Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "0  Abilene Christian University        1  1660    1232     721         23   \n",
       "1            Adelphi University        1  2186    1924     512         16   \n",
       "2                Adrian College        1  1428    1097     336         22   \n",
       "3           Agnes Scott College        1   417     349     137         60   \n",
       "4     Alaska Pacific University        1   193     146      55         16   \n",
       "\n",
       "   Top25perc  F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  Personal  \\\n",
       "0         52         2885          537      7440        3300    450      2200   \n",
       "1         29         2683         1227     12280        6450    750      1500   \n",
       "2         50         1036           99     11250        3750    400      1165   \n",
       "3         89          510           63     12960        5450    450       875   \n",
       "4         44          249          869      7560        4120    800      1500   \n",
       "\n",
       "   PhD  Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0   70        78       18.1           12    7041         60  \n",
       "1   29        30       12.2           16   10527         56  \n",
       "2   53        66       12.9           30    8735         54  \n",
       "3   92        97        7.7           37   19016         59  \n",
       "4   76        72       11.9            2   10922         15  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1) = plt.subplots(1,2)\n",
    "ax0.scatter(z['Books'],z['Terminal'], c=z_km, marker='o', edgecolor = 'black', s=50, cmap='rainbow')\n",
    "ax0.grid()\n",
    "ax1.scatter(z['PhD'],z['Grad.Rate'], c=z_km, marker='o', edgecolor = 'black', s=50, cmap='rainbow')\n",
    "ax1.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xee3b5c8>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distortions = []\n",
    "for i in range(1,21):\n",
    "    km = KMeans(n_clusters = i, init = 'k-means++', n_init = 10, max_iter = 300, random_state=0)\n",
    "    km.fit(z_std)\n",
    "    distortions.append(km.inertia_) #std\n",
    "plt.plot(range(1,21), distortions, marker = '^')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "km = KMeans(n_clusters = 2, init='random',  n_init = 10, max_iter=300, tol=1e-04)\n",
    "z_km = km.fit_predict(z_std)\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(1,2)\n",
    "ax0.scatter(z['Terminal'],z['Private'], c=z_km, marker='o', edgecolor = 'black', s=50, cmap='rainbow')\n",
    "ax0.grid()\n",
    "ax1.scatter(z['Private'],z['Grad.Rate'], c=z_km, marker='o', edgecolor = 'black', s=50, cmap='rainbow')\n",
    "ax1.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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