Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>instr</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instr  class  nb.repeat  attendance  difficulty  Q1  Q2  Q3  Q4  Q5  ...  \\\n",
       "0      1      2          1           0           4   3   3   3   3   3  ...   \n",
       "1      1      2          1           1           3   3   3   3   3   3  ...   \n",
       "2      1      2          1           2           4   5   5   5   5   5  ...   \n",
       "3      1      2          1           1           3   3   3   3   3   3  ...   \n",
       "4      1      2          1           0           1   1   1   1   1   1  ...   \n",
       "\n",
       "   Q19  Q20  Q21  Q22  Q23  Q24  Q25  Q26  Q27  Q28  \n",
       "0    3    3    3    3    3    3    3    3    3    3  \n",
       "1    3    3    3    3    3    3    3    3    3    3  \n",
       "2    5    5    5    5    5    5    5    5    5    5  \n",
       "3    3    3    3    3    3    3    3    3    3    3  \n",
       "4    1    1    1    1    1    1    1    1    1    1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('turkiye-student-evaluation_generic.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5820 entries, 0 to 5819\n",
      "Data columns (total 33 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   instr       5820 non-null   int64\n",
      " 1   class       5820 non-null   int64\n",
      " 2   nb.repeat   5820 non-null   int64\n",
      " 3   attendance  5820 non-null   int64\n",
      " 4   difficulty  5820 non-null   int64\n",
      " 5   Q1          5820 non-null   int64\n",
      " 6   Q2          5820 non-null   int64\n",
      " 7   Q3          5820 non-null   int64\n",
      " 8   Q4          5820 non-null   int64\n",
      " 9   Q5          5820 non-null   int64\n",
      " 10  Q6          5820 non-null   int64\n",
      " 11  Q7          5820 non-null   int64\n",
      " 12  Q8          5820 non-null   int64\n",
      " 13  Q9          5820 non-null   int64\n",
      " 14  Q10         5820 non-null   int64\n",
      " 15  Q11         5820 non-null   int64\n",
      " 16  Q12         5820 non-null   int64\n",
      " 17  Q13         5820 non-null   int64\n",
      " 18  Q14         5820 non-null   int64\n",
      " 19  Q15         5820 non-null   int64\n",
      " 20  Q16         5820 non-null   int64\n",
      " 21  Q17         5820 non-null   int64\n",
      " 22  Q18         5820 non-null   int64\n",
      " 23  Q19         5820 non-null   int64\n",
      " 24  Q20         5820 non-null   int64\n",
      " 25  Q21         5820 non-null   int64\n",
      " 26  Q22         5820 non-null   int64\n",
      " 27  Q23         5820 non-null   int64\n",
      " 28  Q24         5820 non-null   int64\n",
      " 29  Q25         5820 non-null   int64\n",
      " 30  Q26         5820 non-null   int64\n",
      " 31  Q27         5820 non-null   int64\n",
      " 32  Q28         5820 non-null   int64\n",
      "dtypes: int64(33)\n",
      "memory usage: 1.5 MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5820, 33)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instr         0\n",
       "class         0\n",
       "nb.repeat     0\n",
       "attendance    0\n",
       "difficulty    0\n",
       "Q1            0\n",
       "Q2            0\n",
       "Q3            0\n",
       "Q4            0\n",
       "Q5            0\n",
       "Q6            0\n",
       "Q7            0\n",
       "Q8            0\n",
       "Q9            0\n",
       "Q10           0\n",
       "Q11           0\n",
       "Q12           0\n",
       "Q13           0\n",
       "Q14           0\n",
       "Q15           0\n",
       "Q16           0\n",
       "Q17           0\n",
       "Q18           0\n",
       "Q19           0\n",
       "Q20           0\n",
       "Q21           0\n",
       "Q22           0\n",
       "Q23           0\n",
       "Q24           0\n",
       "Q25           0\n",
       "Q26           0\n",
       "Q27           0\n",
       "Q28           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of       instr  class  nb.repeat  attendance  difficulty  Q1  Q2  Q3  Q4  Q5  \\\n",
       "0         1      2          1           0           4   3   3   3   3   3   \n",
       "1         1      2          1           1           3   3   3   3   3   3   \n",
       "2         1      2          1           2           4   5   5   5   5   5   \n",
       "3         1      2          1           1           3   3   3   3   3   3   \n",
       "4         1      2          1           0           1   1   1   1   1   1   \n",
       "...     ...    ...        ...         ...         ...  ..  ..  ..  ..  ..   \n",
       "5815      3     13          1           0           1   1   1   1   1   1   \n",
       "5816      3     13          1           3           4   4   4   4   4   4   \n",
       "5817      3     13          1           0           4   5   5   5   5   5   \n",
       "5818      3     13          1           1           2   1   1   1   1   1   \n",
       "5819      3     13          1           1           2   1   1   1   1   1   \n",
       "\n",
       "      ...  Q19  Q20  Q21  Q22  Q23  Q24  Q25  Q26  Q27  Q28  \n",
       "0     ...    3    3    3    3    3    3    3    3    3    3  \n",
       "1     ...    3    3    3    3    3    3    3    3    3    3  \n",
       "2     ...    5    5    5    5    5    5    5    5    5    5  \n",
       "3     ...    3    3    3    3    3    3    3    3    3    3  \n",
       "4     ...    1    1    1    1    1    1    1    1    1    1  \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "5815  ...    1    1    1    1    1    1    1    1    1    1  \n",
       "5816  ...    5    5    5    5    4    5    5    5    5    5  \n",
       "5817  ...    5    5    5    5    5    5    5    5    5    5  \n",
       "5818  ...    1    1    1    1    1    1    1    1    1    1  \n",
       "5819  ...    1    1    1    1    1    1    1    1    1    1  \n",
       "\n",
       "[5820 rows x 33 columns]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instr  class  nb.repeat  attendance  difficulty  Q1  Q2  Q3  Q4  Q5  ...  \\\n",
       "0      1      2          1           0           4   3   3   3   3   3  ...   \n",
       "1      1      2          1           1           3   3   3   3   3   3  ...   \n",
       "2      1      2          1           2           4   5   5   5   5   5  ...   \n",
       "3      1      2          1           1           3   3   3   3   3   3  ...   \n",
       "4      1      2          1           0           1   1   1   1   1   1  ...   \n",
       "\n",
       "   Q19  Q20  Q21  Q22  Q23  Q24  Q25  Q26  Q27  Q28  \n",
       "0    3    3    3    3    3    3    3    3    3    3  \n",
       "1    3    3    3    3    3    3    3    3    3    3  \n",
       "2    5    5    5    5    5    5    5    5    5    5  \n",
       "3    3    3    3    3    3    3    3    3    3    3  \n",
       "4    1    1    1    1    1    1    1    1    1    1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>instr</th>\n",
       "      <th>class</th>\n",
       "      <th>nb.repeat</th>\n",
       "      <th>attendance</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>...</th>\n",
       "      <th>Q19</th>\n",
       "      <th>Q20</th>\n",
       "      <th>Q21</th>\n",
       "      <th>Q22</th>\n",
       "      <th>Q23</th>\n",
       "      <th>Q24</th>\n",
       "      <th>Q25</th>\n",
       "      <th>Q26</th>\n",
       "      <th>Q27</th>\n",
       "      <th>Q28</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instr  class  nb.repeat  attendance  difficulty  Q1  Q2  Q3  Q4  Q5  ...  \\\n",
       "1      1      2          1           0           4   3   3   3   3   3  ...   \n",
       "2      1      2          1           1           3   3   3   3   3   3  ...   \n",
       "3      1      2          1           2           4   5   5   5   5   5  ...   \n",
       "4      1      2          1           1           3   3   3   3   3   3  ...   \n",
       "5      1      2          1           0           1   1   1   1   1   1  ...   \n",
       "\n",
       "   Q19  Q20  Q21  Q22  Q23  Q24  Q25  Q26  Q27  Q28  \n",
       "1    3    3    3    3    3    3    3    3    3    3  \n",
       "2    3    3    3    3    3    3    3    3    3    3  \n",
       "3    5    5    5    5    5    5    5    5    5    5  \n",
       "4    3    3    3    3    3    3    3    3    3    3  \n",
       "5    1    1    1    1    1    1    1    1    1    1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2 = pd.read_csv('turkiye-student-evaluation_R_Specific.csv')\n",
    "dataset2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.38255613,  4.06131261,  1.1373057 ,  1.95423143,  2.85837651,\n",
       "         4.05699482,  4.23056995,  4.26165803,  4.1925734 ,  4.29015544,\n",
       "         4.25388601,  4.25043178,  4.21416235,  4.26597582,  4.29447323,\n",
       "         4.30310881,  4.21502591,  4.37737478,  4.38687392,  4.38082902,\n",
       "         4.36096718,  4.38428325,  4.38255613,  4.38255613,  4.38255613,\n",
       "         4.3851468 ,  4.40414508,  4.36355786,  4.33937824,  4.39119171,\n",
       "         4.35319516,  4.27979275,  4.38860104],\n",
       "       [ 2.57473842,  4.0568012 ,  1.17115097,  1.64723468,  2.94768311,\n",
       "         2.58071749,  2.7264574 ,  2.8445441 ,  2.76008969,  2.77653214,\n",
       "         2.78176383,  2.71150972,  2.6980568 ,  2.79970105,  2.71300448,\n",
       "         2.77578475,  2.67339312,  3.00523169,  3.06875934,  3.0470852 ,\n",
       "         2.87294469,  3.1980568 ,  2.91255605,  2.95216741,  3.01270553,\n",
       "         3.00523169,  3.00672646,  2.86846039,  2.85052317,  3.0426009 ,\n",
       "         2.92152466,  2.81240658,  3.0612855 ],\n",
       "       [ 2.56563246, 10.53540175,  1.29832936,  1.67143994,  2.80111376,\n",
       "         2.47653142,  2.6778043 ,  2.93237868,  2.69769292,  2.69689737,\n",
       "         2.75735879,  2.65712013,  2.62848051,  2.88703262,  2.70644391,\n",
       "         2.94908512,  2.65871122,  2.92283214,  3.03420843,  3.03102625,\n",
       "         2.80668258,  3.28957836,  2.94908512,  3.03818616,  3.06205251,\n",
       "         3.13842482,  3.14478918,  2.90373906,  2.81463803,  3.11535402,\n",
       "         2.95385839,  2.88305489,  3.11933174],\n",
       "       [ 2.35      , 10.35438596,  1.21666667,  1.9122807 ,  2.79385965,\n",
       "         4.05964912,  4.22894737,  4.26403509,  4.23859649,  4.2745614 ,\n",
       "         4.23596491,  4.2254386 ,  4.19385965,  4.29035088,  4.28333333,\n",
       "         4.3245614 ,  4.16578947,  4.37105263,  4.40614035,  4.40438596,\n",
       "         4.34122807,  4.42280702,  4.36403509,  4.40263158,  4.42982456,\n",
       "         4.43684211,  4.44385965,  4.38508772,  4.34210526,  4.42368421,\n",
       "         4.36666667,  4.31052632,  4.40350877],\n",
       "       [ 2.54368932,  7.73462783,  1.25458468,  1.08306365,  2.41639698,\n",
       "         1.25134844,  1.24703344,  1.30744337,  1.26105717,  1.21898598,\n",
       "         1.23193096,  1.22869471,  1.21790723,  1.31607335,  1.18662352,\n",
       "         1.29018339,  1.20604099,  1.21467098,  1.21898598,  1.2416397 ,\n",
       "         1.16073355,  1.34412082,  1.18770227,  1.20819849,  1.2038835 ,\n",
       "         1.2373247 ,  1.25782093,  1.18122977,  1.19093851,  1.25566343,\n",
       "         1.1995685 ,  1.19093851,  1.22330097]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmn = KMeans(n_clusters=5)\n",
    "kmn.fit(dataset)\n",
    "clusterCenter = kmn.cluster_centers_\n",
    "clusterCenter"
   ]
  }
 ],
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