Kmeans Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "univ_df= pd.read_csv('College_Data', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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": [
       "                             Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "Abilene Christian University     Yes  1660    1232     721         23   \n",
       "Adelphi University               Yes  2186    1924     512         16   \n",
       "Adrian College                   Yes  1428    1097     336         22   \n",
       "Agnes Scott College              Yes   417     349     137         60   \n",
       "Alaska Pacific University        Yes   193     146      55         16   \n",
       "\n",
       "                              Top25perc  F.Undergrad  P.Undergrad  Outstate  \\\n",
       "Abilene Christian University         52         2885          537      7440   \n",
       "Adelphi University                   29         2683         1227     12280   \n",
       "Adrian College                       50         1036           99     11250   \n",
       "Agnes Scott College                  89          510           63     12960   \n",
       "Alaska Pacific University            44          249          869      7560   \n",
       "\n",
       "                              Room.Board  Books  Personal  PhD  Terminal  \\\n",
       "Abilene Christian University        3300    450      2200   70        78   \n",
       "Adelphi University                  6450    750      1500   29        30   \n",
       "Adrian College                      3750    400      1165   53        66   \n",
       "Agnes Scott College                 5450    450       875   92        97   \n",
       "Alaska Pacific University           4120    800      1500   76        72   \n",
       "\n",
       "                              S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "Abilene Christian University       18.1           12    7041         60  \n",
       "Adelphi University                 12.2           16   10527         56  \n",
       "Adrian College                     12.9           30    8735         54  \n",
       "Agnes Scott College                 7.7           37   19016         59  \n",
       "Alaska Pacific University          11.9            2   10922         15  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      "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": [
    "univ_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29b227915c0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 413.25x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Grad.Rate', 'Expend',data=univ_df,hue='Private')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\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 0x29b2271e4e0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 485.25x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('Outstate','F.Undergrad',data=univ_df, hue='Private',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\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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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "g = sns.FacetGrid(univ_df,hue=\"Private\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'Outstate',bins=30,alpha=0.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q5oGOD6bmQXsr1Ptg6g/YyxDrt8Rorj9Ke++bk1Ha/5Gq39ZIGp2TW1K7Tlr6mNlopMjzxmh4/mFkGK5ZGXCb9s2bN+eyyy7LBRdckHPPPTe33npr32Xbt2/PlClTBryRnp46W7bsGFqnHBFTp7a7rxiU0TArzY6O9Oxu3ZbDqRvp2T30c03VaaQ+QJsHOj5wzRy8t0K9D6Z+W7MFt3UE+x919Udp731zMkr7P1L1e+tGbvxGa+ovmdvi3nurbNk69OeN0fD8w8jQ6lk55pjOfo8f9C2Wn//857noooty1VVX5bzzzkuSnHLKKdmwYUOSZP369Tn99NMLtwoAADA6HTRgffazn80rr7ySv/7rv86CBQuyYMGCLFy4MCtXrsz8+fPT3d2defPmHaleAQAARrSDLhFcsmRJlixZst/xNWvWtKwhAACA0coJrAAAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAppDncDAK108a81MmVcuXqvdCernugtVxBgjGg0qjQ7OoZcZ0dP/3WqujfdO3YOuT4MlYAFHNWmjEu+sqxcvXMK1gIYS+o6Wb6uHnKdtmaVnt3711k8x8IsRgaTCAAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIhdBAEOQeeE5MqZr703NWHX6/8/pJoFt5EHAIaXgAVwCBr169u+T+9MNm8des35Nw29BgAwMlgiCAAAUIiABQAAUIiABQAAUIiABQAAUIiABQAAUIhdBAGOQntvJ9+fw9livn1csqN7cNfdu36VpD7A+3mHUvNA9ff2Sney6oneQy8IAIUIWABHob23k+/P4WwxP/+mg9c8UP2qSup66DUPVH9v5xxGLQAoyRJBAACAQgQsAACAQgQsAACAQgQsAACAQgQsAACAQuwiCMBRY6Dt6Qdr723gbf0OwKEQsAA4agy0Pf1g7b0NvK3fATgUlggCAAAUImABAAAUImABAAAUImABAAAUImABAAAUYhdBYMRoHzcpVV3wfZ/uKuPGlSsHADAQAQsYMaq6kXXL63L1GlVmX1usHADAgCwRBAAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKGRQAev73/9+FixYkCR59tlnc/755+eCCy7I9ddfn97e3pY2CAAAMFoMGLBWrVqVJUuWpKurK0ly8803Z+HChbn33ntT13UeeuihljcJAAAwGgwYsI477risXLmy7+uNGzdm1qxZSZLZs2fn0UcfbV13AAAAo0hzoCvMmzcvmzZt6vu6rutUVZUk6ejoyNatWwe8kba2KlOntg+hTY6UtraG+2qY9XY1UvcM7rr1YIs2kl3jDr2XtiqZ0Oh/GXCJWenqbaRnrx+i6koajWpINfe2p3RVruR+9UrVPlCdodQf6HsPp/ahfM9gf0+H+zO24nfWX53OCcknZpb9k+UJu5O2Zov+DLpqYe0W1q/yf3VHaf9HRf1R0nvfrLxBoxGvYdjHcL2uHTBgvVGj8fpAb9++PVOmTBnwe3p66mzZsuNQb4phMHVqu/tqmHU0O7Ju+eCiU9VoZPPA73HknGXJ7d859L+XXDynys5X+p+HErPS7OjI8nWv/6xXzmzkZ4P4eQZreudr/60HnUQHZ+96pWofqM5Q6g/0vYdT+1C+Z891q+rg33e4P2Mrfmf91WnUyZeXlam5x4dvSXp2t+hvmOtG62q3sH5b8//qjtL+j4r6o6T3vll5g97eKlu2eg3D61r9uvaYYzr7PX7IbyOccsop2bBhQ5Jk/fr1Of3004fWGQAAwFHikAPW1VdfnZUrV2b+/Pnp7u7OvHnzWtEXAADAqDOoJYLHHnts1q5dmyQ5/vjjs2bNmpY2BQAAMBo50TAAAEAhAhYAAEAhh7yLIEDy2nbyHc2OoRXprnLlzNf31O48jK3kAQBGEgELOCx1Twa9nfyBVI1qn23m5980xKYAAIaZJYIAAACFCFgAAACFCFgAAACFCFgAAACFCFgAAACF2EUQAI6wqkqunFn2Pc5XupNVT/QWrQnAoROwAOAIq3uTrywrW/OcwvUAODyWCAIAABQiYAEAABQiYAEAABQiYAEAABQiYAEAABRiF0EYAzonHN6W0BO6kwnNjv4vrIfYFADAUUjAgjGgUR/eltDTO5O6t/8k9dtLqqE1BQBwFLJEEAAAoBABCwAAoBABCwAAoBABCwAAoBABCwAAoBC7CALAUWDP6Rgm7Dq80zL0p31csqN732NDrf9Kd7Lqid4hdgYwcglYAHAU2HM6humdyeatZWrOv2n/UzwMtf45ywa8CsCoZokgAABAIQIWAABAIQIWAABAIQIWAABAIQIWAABAIXYRBACOmD3byb9RlaTOoW8zb9t3YKQRsACAI2bPdvJvVFVJXR/6NvC2fQdGGksEAQAAChGwAAAAChGwAAAAChGwAAAAChGwAAAACrGLIGPeuPZJqavWvdfQaCS9h7KDcHeVqlG1rJ9DUSWv/QD9qJNUB7gMAGCsErAY8+qqkeXr6pbVXzK3keXrBp+wrpxZDXqL4umdh9nUINU58HbJdX1oWyn3p9X9AwAcad5+BgAAKETAAgAAKETAAgAAKETAAgAAKETAAgAAKMQugiPASNomfEdPlWZHxyHVr+redO/YeRidjX4X/1ojU8Yd/DoTdiVXzhz8/ds5QD0AXtc54dAeYwfzmNw+LtnRfXj9HKj+K93JqicO5ZwdwGglYI0AI2mb8LZmlZ7dh9bL4jlj94PQKeOSryw7+HWmdx7adubzbxpSSwBjSqMe+HF4b4N5TJ5/06HVHEz9cw6zHjD6jN1XxgAAAIUJWAAAAIUIWAAAAIUIWAAAAIUIWAAAAIXYRZAhazQOfWv3wZjQUyW9SVUndV1ml8W66s2O7rG5pTwAHGl7n87kUE9bciCv7E5W/WDIZaBlBCyGrK7Tkm3mr5xZ5SvLXtvytu4tU3/OYh/aAsCRsvfpTA71tCUH8v5lQ68BreTVJgAAQCECFgAAQCECFgAAQCECFgAAQCECFgAAQCFjehfB9nGTUtVlM+ZY3QZ8721YS+n8v3pVkjTK3E+NtqSjMXmfY1X3azsWHo7Owj8zAAxW6efeCbuSpWc0sqO7XE3Pk2PDuPZJqavWfW5T1b3p3jF6Xl+P6YBV1Y2sW152e/Gxug343tuwljL/ptf+W6fMtq5J0tuT3L9o32ND2TZ2T48AcKSVfu6d3pnMvrZsTc+TY0NdNVpyyp49Fs8ZXa+vR1e3AAAAI5iABQAAUMhhLRHs7e3NsmXL8sMf/jDjx4/PjTfemLe97W2lewMAABhVDusTrK997WvZtWtX7r///lx55ZW55ZZbSvcFAAAw6hxWwHr88cdz1llnJUlOO+20PPnkk0WbAgAAGI2quq4PecuPxYsX533ve1/mzJmTJJk7d26+9rWvpdkc05sSAgAAY9xhfYI1efLkbN++ve/r3t5e4QoAABjzDitgzZw5M+vXr0+SfO9738vJJ59ctCkAAIDR6LCWCO7ZRfBHP/pR6rrOTTfdlBNOOKEV/QEAAIwahxWwAAAA2J8TDQMAABQiYAEAABQiYAEAABRib/UxrLu7O4sWLcpzzz2XXbt25dJLL82JJ56Ya665JlVV5aSTTsr111+fRkMO5zW/+MUv8od/+If5u7/7uzSbTbNCv+6+++58/etfT3d3d84///zMmjXLrLCP7u7uXHPNNXnuuefSaDRyww03eExhP9///vdz2223ZfXq1Xn22Wf7nY+/+qu/yje+8Y00m80sWrQop5566nC3zTDYe1aeeuqp3HDDDWlra8v48eOzYsWKvPnNb87atWtz3333pdls5tJLL81v//Zvt6wfj1xj2IMPPpipU6fm3nvvzapVq3LDDTfk5ptvzsKFC3Pvvfemrus89NBDw90mI0R3d3euu+66TJw4MUnMCv3asGFDvvvd7+bzn/98Vq9eneeff96ssJ9169Zl9+7due+++3LZZZflM5/5jDlhH6tWrcqSJUvS1dWVpP/nnI0bN+Y//uM/8s///M+544478slPfnKYu2Y4vHFWli9fnqVLl2b16tU5++yzs2rVqrz44otZvXp17rvvvvzt3/5t7rjjjuzatatlPQlYY9g555yTj33sY31ft7W1ZePGjZk1a1aSZPbs2Xn00UeHqz1GmBUrVuTDH/5wfvmXfzlJzAr9euSRR3LyySfnsssuyyWXXJK5c+eaFfZz/PHHp6enJ729vdm2bVuazaY5YR/HHXdcVq5c2fd1f/Px+OOP58wzz0xVVfmVX/mV9PT05KWXXhqulhkmb5yVO+64I29/+9uTJD09PZkwYUJ+8IMf5F3velfGjx+fzs7OHHfccXn66adb1pOANYZ1dHRk8uTJ2bZtWy6//PIsXLgwdV2nqqq+y7du3TrMXTISPPDAA5k2bVrOOuusvmNmhf68/PLLefLJJ3PnnXfmk5/8ZD7xiU+YFfbT3t6e5557Lu9///uzdOnSLFiwwJywj3nz5qXZfP0vWfqbj23btmXy5Ml91zE3Y9MbZ2XPG8Hf+c53smbNmlx44YXZtm1bOjs7+67T0dGRbdu2tawnf4M1xm3evDmXXXZZLrjggpx77rm59dZb+y7bvn17pkyZMozdMVJ88YtfTFVVeeyxx/LUU0/l6quv3uddQrPCHlOnTs2MGTMyfvz4zJgxIxMmTMjzzz/fd7lZIUn+4R/+IWeeeWauvPLKbN68OX/yJ3+S7u7uvsvNCW+099/j7ZmPyZMnZ/v27fsc3/tFNGPXl770pdx111255557Mm3atCM+Kz7BGsN+/vOf56KLLspVV12V8847L0lyyimnZMOGDUmS9evX5/TTTx/OFhkhPve5z2XNmjVZvXp13v72t2fFihWZPXu2WWE/v/Ebv5FvfvObqes6L7zwQnbu3Jn3vOc9ZoV9TJkype/FzZve9Kbs3r3b8w8H1d98zJw5M4888kh6e3vzs5/9LL29vZk2bdowd8pw+9d//de+1yxvfetbkySnnnpqHn/88XR1dWXr1q155plncvLJJ7esh6qu67pl1RnRbrzxxnz5y1/OjBkz+o4tXrw4N954Y7q7uzNjxozceOONaWtrG8YuGWkWLFiQZcuWpdFoZOnSpWaF/Xz605/Ohg0bUtd1Pv7xj+fYY481K+xj+/btWbRoUV588cV0d3fnIx/5SN75zneaE/axadOmXHHFFVm7dm1+8pOf9DsfK1euzPr169Pb25trr71WMB+j9szK5z//+bznPe/J9OnT+z4F/83f/M1cfvnlWbt2be6///7UdZ0/+7M/y7x581rWj4AFAABQiCWCAAAAhQhYAAAAhQhYAAAAhQhYAAAAhQhYAAAAhTjRMADD4qc//WluvfXWPP/885k4cWImTpyYq666KieddNIh1Vm/fn2+9KUv5ZZbbuk7tmnTpvz+7/9+3vGOdyRJurq60t7enjvvvDNvetOb+q2zZcuWfPOb38y55557+D8UAGOegAXAEbdz585ceumlueGGG/Kud70rSfKDH/wgn/rUp7J69eoit3HiiSfuU+v222/PF77whXz0ox/t9/o//OEP8/Wvf13AAmBIBCwAjriHH3447373u/vCVZKceuqp+ad/+qdcc8012bJlS7Zs2ZK77rort912W55//vm8/PLLmT17dhYuXJhnnnkmixYtyqRJkzJp0qQDfiq1R13X2bx5c4477rgkr4WtJ598Mtu3b88JJ5yQm2++OZ/97Gfz9NNP5/7778/s2bOzdOnSdHV1ZcKECbnhhhsyffr0lv5OADg6CFgAHHGbNm3qCztJcumll2bbtm353//930yfPj1z587NhRdemE2bNuW0007Lhz70oXR1dfUFrDvvvDOXX355fuu3fiv33HNP/uu//mu/2/jP//zPLFiwIFu2bElXV1fOPffcfPCDH8y2bYrYRqgAAAJWSURBVNsyZcqU/P3f/316e3vze7/3e3nhhRdyySWX5L777sv8+fOzcOHCLFiwIHPmzMljjz2W2267LbfffvuR/BUBMEoJWAAccW95y1vy5JNP9n191113JUn+6I/+KG95y1ty/PHHJ0mmTp2aJ554Iv/+7/+eyZMnZ9euXUmSH//4xzn11FOTJDNnzuw3YO1ZIvjqq6/mkksuyS/90i+l2WxmwoQJeemll3LFFVekvb09O3bsSHd39z7f+6Mf/Sh33313/uZv/iZ1XWfcuHEt+T0AcPQRsAA44t773vdm1apV+d73vpfTTjstSfLss8/m+eefz4QJE1JVVZLkgQceSGdnZz71qU/l2Wefzdq1a1PXdWbMmJHvfve7mT179j5BrT8TJ07Mbbfdlg984AOZOXNmnnvuuWzevDmf+cxn8tJLL+WrX/1q6rpOo9FIb29vkmTGjBm56KKLMnPmzDzzzDP59re/3dpfCABHjaqu63q4mwBg7Nm0aVNuv/32vPjii9m9e3eazWYuvvjifPnLX87v/u7vZvbs2fnxj3+cK664Ih0dHZk0aVI2b96cf/zHf0xVVfn4xz+eZrOZadOmZcKECbnlllvy6U9/Ouecc06mTZuWK664ImvXru27vQcffDBr1qzJypUr8+d//udpa2vL+PHj8+qrr+baa6/NsccemwsvvDDz58/Pe9/73ixbtixdXV159dVXs3jx4n3+XgwADkTAAgAAKMSJhgEAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAoRsAAAAAr5/3Z17C65KdpmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "p= sns.FacetGrid(univ_df,hue=\"Private\",palette='cool',size=6,aspect=2)\n",
    "p = p.map(plt.hist,'Grad.Rate',bins=30,alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "univ_df[univ_df['Grad.Rate']>100]\n",
    "univ_df['Grad.Rate']['Cazenovia College']=90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Private, Apps, Accept, Enroll, Top10perc, Top25perc, F.Undergrad, P.Undergrad, Outstate, Room.Board, Books, Personal, PhD, Terminal, S.F.Ratio, perc.alumni, Expend, Grad.Rate]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_df[univ_df['Grad.Rate']>100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\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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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "p= sns.FacetGrid(univ_df,hue=\"Private\",palette='cool',size=6,aspect=2)\n",
    "p = p.map(plt.hist,'Grad.Rate',bins=30,alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(univ_df.drop('Private',axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def converter(priv):\n",
    "    if priv=='Yes':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "univ_df['priv']=univ_df['Private'].apply(converter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "      <th>priv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <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  priv  \n",
       "Abilene Christian University       18.1           12    7041         60     1  \n",
       "Adelphi University                 12.2           16   10527         56     1  \n",
       "Adrian College                     12.9           30    8735         54     1  \n",
       "Agnes Scott College                 7.7           37   19016         59     1  \n",
       "Alaska Pacific University          11.9            2   10922         15     1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 74 138]\n",
      " [ 34 531]]\n",
      "\n",
      "\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": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(univ_df['priv'],kmeans.labels_))\n",
    "print('\\n')\n",
    "print(classification_report(univ_df['priv'],kmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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