technical.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"encoded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>name</th>\n",
       "      <th>rollno</th>\n",
       "      <th>branch</th>\n",
       "      <th>year</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>pt</th>\n",
       "      <th>st</th>\n",
       "      <th>ppl</th>\n",
       "      <th>...</th>\n",
       "      <th>sgpa</th>\n",
       "      <th>personality</th>\n",
       "      <th>behaviour</th>\n",
       "      <th>decison</th>\n",
       "      <th>adaptablilty</th>\n",
       "      <th>life</th>\n",
       "      <th>hobby</th>\n",
       "      <th>hstudy</th>\n",
       "      <th>match</th>\n",
       "      <th>Total_personality_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Naman Chaudhary</td>\n",
       "      <td>1803211033</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Utsav Mehrotra</td>\n",
       "      <td>1803211058</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Akshat Atray</td>\n",
       "      <td>1803211003</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Shivam Sharma</td>\n",
       "      <td>1900321290059</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Aditya saini</td>\n",
       "      <td>2000321530010</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0             name         rollno  \\\n",
       "0           0  Naman Chaudhary     1803211033   \n",
       "1           1   Utsav Mehrotra     1803211058   \n",
       "2           2     Akshat Atray     1803211003   \n",
       "3           3    Shivam Sharma  1900321290059   \n",
       "4           4     Aditya saini  2000321530010   \n",
       "\n",
       "                                              branch  year  gender  age  pt  \\\n",
       "0                                               CEIT     4       1   20   1   \n",
       "1                                               CEIT     4       1   21   1   \n",
       "2                                               CEIT     4       1   21   1   \n",
       "3                                               CEIT     3       1   20   1   \n",
       "4  Computer Science( CS- AI/ML, CS - Data Science...     2       1   20   1   \n",
       "\n",
       "   st  ppl  ...  sgpa  personality  behaviour  decison  adaptablilty  life  \\\n",
       "0   8    1  ...     3            4          4        3             3     3   \n",
       "1   2    3  ...     2            3          5        1             5     5   \n",
       "2  11    1  ...     2            4          5        2             5     4   \n",
       "3   7    2  ...     3            4          4        4             3     2   \n",
       "4   6    1  ...     2            4          5        3             5     4   \n",
       "\n",
       "   hobby  hstudy  match  Total_personality_score  \n",
       "0      4       0      1                      3.4  \n",
       "1      1       0      1                      3.8  \n",
       "2      0       0      1                      4.0  \n",
       "3      0       0      1                      3.4  \n",
       "4      4       2      1                      4.2  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'name', 'rollno', 'branch', 'year', 'gender', 'age', 'pt',\n",
       "       'st', 'ppl', 'spl', 'c_rate', 'w_rate', 'certified', '12', 'sgpa',\n",
       "       'personality', 'behaviour', 'decison', 'adaptablilty', 'life', 'hobby',\n",
       "       'hstudy', 'match', 'Total_personality_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>name</th>\n",
       "      <th>rollno</th>\n",
       "      <th>branch</th>\n",
       "      <th>year</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>pt</th>\n",
       "      <th>st</th>\n",
       "      <th>ppl</th>\n",
       "      <th>spl</th>\n",
       "      <th>...</th>\n",
       "      <th>sgpa</th>\n",
       "      <th>personality</th>\n",
       "      <th>behaviour</th>\n",
       "      <th>decison</th>\n",
       "      <th>adaptablilty</th>\n",
       "      <th>life</th>\n",
       "      <th>hobby</th>\n",
       "      <th>hstudy</th>\n",
       "      <th>match</th>\n",
       "      <th>Total_personality_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naman Chaudhary</td>\n",
       "      <td>1803211033</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utsav Mehrotra</td>\n",
       "      <td>1803211058</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akshat Atray</td>\n",
       "      <td>1803211003</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shivam Sharma</td>\n",
       "      <td>1900321290059</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aditya saini</td>\n",
       "      <td>2000321530010</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Sanskar garg</td>\n",
       "      <td>2000320120151</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Sumit Jaiswal</td>\n",
       "      <td>2000320120176</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Kapil Panwar</td>\n",
       "      <td>2000320120093</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Santosh Pandit</td>\n",
       "      <td>2000320120152</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Dev Tayal</td>\n",
       "      <td>2000320120070</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name         rollno  \\\n",
       "0    Naman Chaudhary     1803211033   \n",
       "1     Utsav Mehrotra     1803211058   \n",
       "2       Akshat Atray     1803211003   \n",
       "3      Shivam Sharma  1900321290059   \n",
       "4       Aditya saini  2000321530010   \n",
       "..               ...            ...   \n",
       "323     Sanskar garg  2000320120151   \n",
       "324    Sumit Jaiswal  2000320120176   \n",
       "325     Kapil Panwar  2000320120093   \n",
       "326   Santosh Pandit  2000320120152   \n",
       "327        Dev Tayal  2000320120070   \n",
       "\n",
       "                                                branch  year  gender  age  pt  \\\n",
       "0                                                 CEIT     4       1   20   1   \n",
       "1                                                 CEIT     4       1   21   1   \n",
       "2                                                 CEIT     4       1   21   1   \n",
       "3                                                 CEIT     3       1   20   1   \n",
       "4    Computer Science( CS- AI/ML, CS - Data Science...     2       1   20   1   \n",
       "..                                                 ...   ...     ...  ...  ..   \n",
       "323  Computer Science( CS- AI/ML, CS - Data Science...     2       1   19  14   \n",
       "324  Computer Science( CS- AI/ML, CS - Data Science...     2       1   20  12   \n",
       "325  Computer Science( CS- AI/ML, CS - Data Science...     2       1   20   1   \n",
       "326  Computer Science( CS- AI/ML, CS - Data Science...     2       1   19   1   \n",
       "327  Computer Science( CS- AI/ML, CS - Data Science...     2       1   17   3   \n",
       "\n",
       "     st  ppl  spl  ...  sgpa  personality  behaviour  decison  adaptablilty  \\\n",
       "0     8    1    3  ...     3            4          4        3             3   \n",
       "1     2    3    1  ...     2            3          5        1             5   \n",
       "2    11    1    3  ...     2            4          5        2             5   \n",
       "3     7    2    3  ...     3            4          4        4             3   \n",
       "4     6    1    3  ...     2            4          5        3             5   \n",
       "..   ..  ...  ...  ...   ...          ...        ...      ...           ...   \n",
       "323   3    2    1  ...     2            4          5        5             4   \n",
       "324   5    2    1  ...     3            3          3        3             3   \n",
       "325   2    2    3  ...     2            1          4        3             3   \n",
       "326   3    2    3  ...     2            3          4        3             3   \n",
       "327   1    1    3  ...     1            5          3        1             2   \n",
       "\n",
       "     life  hobby  hstudy  match  Total_personality_score  \n",
       "0       3      4       0      1                      3.4  \n",
       "1       5      1       0      1                      3.8  \n",
       "2       4      0       0      1                      4.0  \n",
       "3       2      0       0      1                      3.4  \n",
       "4       4      4       2      1                      4.2  \n",
       "..    ...    ...     ...    ...                      ...  \n",
       "323     2      0       0      0                      4.0  \n",
       "324     3      1       0      0                      3.0  \n",
       "325     3      1       1      0                      2.8  \n",
       "326     3      1       0      1                      3.2  \n",
       "327     3      0       0      0                      2.8  \n",
       "\n",
       "[328 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "technical = df[[\"pt\",\"st\",\"ppl\",\"spl\",\"c_rate\",\"w_rate\",\"certified\",\"sgpa\",\"12\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>328 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pt  st  ppl  spl  c_rate  w_rate  certified  sgpa  12\n",
       "0     1   8    1    3       3       3          1     3   3\n",
       "1     1   2    3    1       3       4          0     2   3\n",
       "2     1  11    1    3       4       5          1     2   2\n",
       "3     1   7    2    3       2       3          0     3   2\n",
       "4     1   6    1    3       3       4          0     2   2\n",
       "..   ..  ..  ...  ...     ...     ...        ...   ...  ..\n",
       "323  14   3    2    1       1       1          1     2   2\n",
       "324  12   5    2    1       3       3          0     3   3\n",
       "325   1   2    2    3       1       3          0     2   3\n",
       "326   1   3    2    3       3       2          0     2   2\n",
       "327   3   1    1    3       2       1          0     1   1\n",
       "\n",
       "[328 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "technical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pltu\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn_extra.cluster import KMedoids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_digits=13\n",
    "models = [\n",
    "     (\n",
    "         KMedoids(metric=\"manhattan\", n_clusters=num_digits, \n",
    "         init=\"heuristic\", max_iter=2),\"Manhattan metric\",\n",
    "     ),\n",
    "     (\n",
    "         KMedoids(metric=\"euclidean\", n_clusters=num_digits,  \n",
    "         init=\"heuristic\", max_iter=2),\"Euclidean metric\",\n",
    "     ),\n",
    "     (KMedoids(metric=\"cosine\", n_clusters=num_digits, init=\"heuristic\", \n",
    "      max_iter=2), \"Cosine metric\", ),\n",
    " ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(328, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "technical.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5  3  5  5  2  2  4  0  7 11  9 11 11  3  8  9  9  6  9  7  5  9  9  7\n",
      "  9  1  2  2  7  3  3  2  3  4  1  2  9  9  2  4  9  9  8  6  9  6  9  0\n",
      "  6  8 11  6  8  8  3  0 10  5  3 10  6  5  3  2  5  8  1  7 11  3  2  0\n",
      "  9 12 12  3  4  3  9  3  9  6  3  1  6 10  2  4  1  4  7  4  4  0  8  3\n",
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      "  3 12 11 10  5  2  8 10  2  1  9  1  9  5  6 12  1  5  0  5 12  2  1  8\n",
      " 12  3 10  0  2  8 11  0 10  3  6  3  0  8  5  0 11  9  8 10  3  7  6  3\n",
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      "  6  6  9 11  7  6  9  9  0  3  7  7  6  3  5  7  9  4  2  4  3  0  1  2\n",
      "  0  9  7  0  2  3  2  3 11  0  7  7  6  3  3 11]\n",
      "[ 1  9  1  1  1  6  3 10  8 10  8 10  7  5  5 11 11  7  8  3  1 11 11  8\n",
      "  8  7  2  2  3  5  5 11  5  2 12  8  8  8  8  2  8  8  5  7  8  0  8  9\n",
      "  0  5 10  0  6  2  5  9  2 11  9  6  8 11  5  1 11  5  7  3  5  5  1  5\n",
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      "  2  5  0  7  2  2 12  1  1  8  4  5  8  9 10  6  9  7  3  3  3  6  8  5\n",
      "  5  4 10  6  1  2  5  6  2  0  0  7  8 11  0  0  7  1  9 11  4  2  7  5\n",
      "  4  6 12  5  2  1 10  5  6  5  0  5  9  6  1  9  7  8  5  2 12  3  0  5\n",
      "  1  3  3  9 11  5  0  1  0  5 12  5  0  9  3  9  5  3  9  9  8  5  3 12\n",
      "  5  8  8 12  5  9  8  2 11  1  1  0  8  0 10 10  0  2 11  5 10  5  0  0\n",
      "  3  0 11  7  7  8  8 11  5  5  8  3  0 12 11  0 11  3  2  2  5  9  2  2\n",
      "  5 11  7  5  2 12  1 12 10  9  3  0  0  5  5  4]\n",
      "[ 0  5  0  0  0  9  2  7 12  8 12 10 10  5  5  3 11  1  6  6  0 11 11  6\n",
      "  6  1  4  4  6  5  5  3  5  9  1  3 11 11  6  9 12 11  4 12 11 10 11  7\n",
      "  8  5  8 10  1  4  5  1  4  3  5  3 12  3  5  0 12  5 10  6  7  5  2  5\n",
      "  6  2 10  5 12  5 11  5 11  8  5  1  8  3  2  6  1  4  6  4  2  7  1  7\n",
      "  2 10  8  0 11  6 11  1  1  1  3  5 11  8  7  0 12  1  3  7  0  3 11  6\n",
      "  8 10  3  3  1 10  1  4  5  5 12  4  9  0  0  7  8  8 10 12  2  7  5  5\n",
      "  5  6 11  0  3  0  3 10  3 11  5  3  4  1  7  7  8  5  7  8  5  6  6  6\n",
      "  2  7  8  1  3  3  1  0  0 11  9  7 11  1  7  0  5 10 10  2  6 12 11  7\n",
      "  5  8  8  0  0  0  5  3  4  8 10  1 12  0 12  8  8  0  7  0 10  0  1  5\n",
      "  8  5  1  7  2  0  8  7  0  5 10  2  1  0  0  7  8 12  5  6  1  1 10  0\n",
      "  0  2  6  7 11  5  8  0 10  5  7  5 10  7  6  5  5  8  7  5 12  7  9  0\n",
      "  5  6 11  1  5  5  6  6  3  0  0  8 12  8  7  7  8  4  0  4 10  5 10  8\n",
      " 10 10  3  8 10  6  6  3  7  5 12  4 10  9  3  8 12  4  0  2  5  1  2  2\n",
      "  7  3  1  5  2  4 12  5  7  7  6  8  8  5  5  7]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:275: UserWarning: Cluster 12 is empty! self.labels_[self.medoid_indices_[12]] may not be labeled with its corresponding cluster (12).\n",
      "  warnings.warn(\n",
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:243: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  warnings.warn(\n",
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:243: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  warnings.warn(\n",
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:243: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "num_cols, num_rows,red_data = 9,328,technical\n",
    "second_model = []\n",
    "for i, (model, description) in enumerate(models):\n",
    "    Z = model.fit(technical)\n",
    "    second_model.append(Z.labels_)\n",
    "    print(Z.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMedoids(metric=\"euclidean\", n_clusters=13,  \n",
    "         init=\"heuristic\", max_iter=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:243: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KMedoids(max_iter=2, n_clusters=13)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(technical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[1,1,1,1,1,1,1,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Roll=df['rollno']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=pd.DataFrame(Roll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>1803211058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1803211003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900321290059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000321530010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2000320120151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2000320120176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>2000320120093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>2000320120152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>2000320120070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            rollno\n",
       "0       1803211033\n",
       "1       1803211058\n",
       "2       1803211003\n",
       "3    1900321290059\n",
       "4    2000321530010\n",
       "..             ...\n",
       "323  2000320120151\n",
       "324  2000320120176\n",
       "325  2000320120093\n",
       "326  2000320120152\n",
       "327  2000320120070\n",
       "\n",
       "[328 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "u=pd.Series(model.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f[\"cluster\"]=u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.loc[f[\"cluster\"]==5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.loc[df[\"rollno\"]==\"1900320120011\"].columns ,df.loc[df[\"rollno\"]==\"1900320120011\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "technical = df[[\"pt\",\"st\",\"ppl\",\"spl\",\"c_rate\",\"w_rate\",\"certified\",\"sgpa\",\"12\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models[1].predict([1,2,0,1,3,2,0,3,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(second_model[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "behaviour=df[[\"personality\",\"behaviour\",\"decison\",\"adaptablilty\",\"life\",\"hobby\",\"hstudy\",\"match\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>328 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     personality  behaviour  decison  adaptablilty  life  hobby  hstudy  match\n",
       "0              4          4        3             3     3      4       0      1\n",
       "1              3          5        1             5     5      1       0      1\n",
       "2              4          5        2             5     4      0       0      1\n",
       "3              4          4        4             3     2      0       0      1\n",
       "4              4          5        3             5     4      4       2      1\n",
       "..           ...        ...      ...           ...   ...    ...     ...    ...\n",
       "323            4          5        5             4     2      0       0      0\n",
       "324            3          3        3             3     3      1       0      0\n",
       "325            1          4        3             3     3      1       1      0\n",
       "326            3          4        3             3     3      1       0      1\n",
       "327            5          3        1             2     3      0       0      0\n",
       "\n",
       "[328 rows x 8 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behaviour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=KMedoids(metric=\"euclidean\", n_clusters=num_digits,  \n",
    "         init=\"heuristic\", max_iter=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:275: UserWarning: Cluster 3 is empty! self.labels_[self.medoid_indices_[3]] may not be labeled with its corresponding cluster (3).\n",
      "  warnings.warn(\n",
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:275: UserWarning: Cluster 6 is empty! self.labels_[self.medoid_indices_[6]] may not be labeled with its corresponding cluster (6).\n",
      "  warnings.warn(\n",
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:243: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KMedoids(max_iter=2, n_clusters=13)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(behaviour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict([[3,3,3,3,3,1,2,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 12,  8,  4,  5,  5,  5, 12, 12,  5,  0,  5, 12, 12,  8,  7,  9,\n",
       "       10,  8,  7,  9,  5,  1,  0,  1,  8,  3,  9, 11,  1,  7,  2,  7,  5,\n",
       "        4,  1,  7,  4,  1,  5,  1,  2,  7,  9, 12,  0,  0,  8,  5,  5,  7,\n",
       "        5,  9,  1,  4,  7,  3,  2, 12,  9,  0,  2,  1,  7,  8, 12,  5, 11,\n",
       "        0,  4, 12,  1,  1,  3,  5,  9, 12,  8,  2,  7,  7,  9,  0,  0,  0,\n",
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       "        1,  0,  2,  0,  0,  5,  0, 12,  3, 11,  2, 12,  2,  4,  5,  2,  4,\n",
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       "        0,  9,  2,  5, 12,  4,  3,  7,  1, 12,  0,  0,  4,  2,  4,  5,  7,\n",
       "       12,  4,  2,  8,  5,  9, 11, 10,  2,  4,  7, 11, 11,  2,  5, 10,  1,\n",
       "        4, 11,  9, 10,  0,  0,  7,  5,  7,  2,  9,  0,  4,  7, 12,  8,  4,\n",
       "        4,  9,  8,  7, 10,  0,  2,  2,  7,  3,  0,  2,  5, 11,  7, 11,  4,\n",
       "        7,  7,  8,  3,  4,  9,  9,  1,  2,  1, 12, 10,  0,  7,  7,  2,  4,\n",
       "        8,  8,  2,  4,  4,  4,  4, 11, 11,  7, 10,  7,  4,  9,  5,  7,  3,\n",
       "       12,  4,  9,  5,  0, 12,  3,  5,  5,  3, 12,  1, 12,  4,  7,  1, 12,\n",
       "       11,  4,  0, 11,  2, 11,  0,  4,  9,  1,  5,  0,  0,  5,  2,  8, 11,\n",
       "        2,  8, 12,  4,  9,  9,  7,  5, 12,  4,  2,  4, 11,  5,  5,  4,  4,\n",
       "        2,  0,  0,  4,  0,  2,  4,  9,  4,  9,  0,  4, 12, 11,  4, 11,  3,\n",
       "        9,  4,  4,  4, 12], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "behv=pd.DataFrame(Roll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "u1=pd.Series(model2.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "behv[\"behv_cluster\"]=u1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     47\n",
       "0     37\n",
       "5     37\n",
       "2     36\n",
       "7     35\n",
       "12    30\n",
       "9     27\n",
       "1     20\n",
       "8     19\n",
       "11    19\n",
       "3     14\n",
       "10     7\n",
       "Name: behv_cluster, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behv[\"behv_cluster\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rollno</th>\n",
       "      <th>behv_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>31</th>\n",
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       "      <th>172</th>\n",
       "      <td>1900321290035</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2000321530064</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2000321530023</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2000320129004</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1803211018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1803211037</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1900320120011</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1900321290053</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2000321530029</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>1900321290045</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2000321530011</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2021B0123005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2000320120195</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>2000320120164</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>2000320120177</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>2000320120078</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rollno behv_cluster\n",
       "31   2000321530131            2\n",
       "41   2000321530059            2\n",
       "57   2000321530077            2\n",
       "61   2000321530074            2\n",
       "78   2000321530046            2\n",
       "92   1900320120051            2\n",
       "95   2000320120005            2\n",
       "104  2000320120001            2\n",
       "112  1900321290063            2\n",
       "114  2000321530131            2\n",
       "117     1803211028            2\n",
       "120  2000321530041            2\n",
       "125  2000321530032            2\n",
       "131  2000320120056            2\n",
       "132  2000321530019            2\n",
       "133  1900320150001            2\n",
       "135  2000320120088            2\n",
       "139  2000321530019            2\n",
       "155  2000320120078            2\n",
       "166  2000320120101            2\n",
       "172  1900321290035            2\n",
       "178  2000321530064            2\n",
       "183  2000321530023            2\n",
       "196  2000320129004            2\n",
       "210     1803211018            2\n",
       "211     1803211037            2\n",
       "215  1900320120011            2\n",
       "229  1900321290053            2\n",
       "236  2000321530029            2\n",
       "240  1900321290045            2\n",
       "276  2000321530011            2\n",
       "286   2021B0123005            2\n",
       "289  2000320120195            2\n",
       "299  2000320120164            2\n",
       "306  2000320120177            2\n",
       "311  2000320120078            2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behv.loc[behv[\"behv_cluster\"]==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['name', 'rollno', 'branch', 'year', 'gender', 'age', 'pt', 'st', 'ppl',\n",
      "       'spl', 'c_rate', 'w_rate', 'certified', '12', 'sgpa', 'personality',\n",
      "       'behaviour', 'decison', 'adaptablilty', 'life', 'hobby', 'hstudy',\n",
      "       'match', 'Total_personality_score'],\n",
      "      dtype='object') [['Himani yadav' '1900321290035' 'CEIT' 3 0 19 3 7 2 3 3 1 0 3 3 3 4 3 4\n",
      "  3 1 2 0 3.4]]\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[df[\"rollno\"]==\"1900321290035\"].columns ,df.loc[df[\"rollno\"]==\"1900321290035\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rollno</th>\n",
       "      <th>branch</th>\n",
       "      <th>year</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>pt</th>\n",
       "      <th>st</th>\n",
       "      <th>ppl</th>\n",
       "      <th>spl</th>\n",
       "      <th>...</th>\n",
       "      <th>sgpa</th>\n",
       "      <th>personality</th>\n",
       "      <th>behaviour</th>\n",
       "      <th>decison</th>\n",
       "      <th>adaptablilty</th>\n",
       "      <th>life</th>\n",
       "      <th>hobby</th>\n",
       "      <th>hstudy</th>\n",
       "      <th>match</th>\n",
       "      <th>Total_personality_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naman Chaudhary</td>\n",
       "      <td>1803211033</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
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       "      <td>8</td>\n",
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       "      <td>...</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Utsav Mehrotra</td>\n",
       "      <td>1803211058</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akshat Atray</td>\n",
       "      <td>1803211003</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shivam Sharma</td>\n",
       "      <td>1900321290059</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aditya saini</td>\n",
       "      <td>2000321530010</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Sanskar garg</td>\n",
       "      <td>2000320120151</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Sumit Jaiswal</td>\n",
       "      <td>2000320120176</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Kapil Panwar</td>\n",
       "      <td>2000320120093</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Santosh Pandit</td>\n",
       "      <td>2000320120152</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Dev Tayal</td>\n",
       "      <td>2000320120070</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name         rollno  \\\n",
       "0    Naman Chaudhary     1803211033   \n",
       "1     Utsav Mehrotra     1803211058   \n",
       "2       Akshat Atray     1803211003   \n",
       "3      Shivam Sharma  1900321290059   \n",
       "4       Aditya saini  2000321530010   \n",
       "..               ...            ...   \n",
       "323     Sanskar garg  2000320120151   \n",
       "324    Sumit Jaiswal  2000320120176   \n",
       "325     Kapil Panwar  2000320120093   \n",
       "326   Santosh Pandit  2000320120152   \n",
       "327        Dev Tayal  2000320120070   \n",
       "\n",
       "                                                branch  year  gender  age  pt  \\\n",
       "0                                                 CEIT     4       1   20   1   \n",
       "1                                                 CEIT     4       1   21   1   \n",
       "2                                                 CEIT     4       1   21   1   \n",
       "3                                                 CEIT     3       1   20   1   \n",
       "4    Computer Science( CS- AI/ML, CS - Data Science...     2       1   20   1   \n",
       "..                                                 ...   ...     ...  ...  ..   \n",
       "323  Computer Science( CS- AI/ML, CS - Data Science...     2       1   19  14   \n",
       "324  Computer Science( CS- AI/ML, CS - Data Science...     2       1   20  12   \n",
       "325  Computer Science( CS- AI/ML, CS - Data Science...     2       1   20   1   \n",
       "326  Computer Science( CS- AI/ML, CS - Data Science...     2       1   19   1   \n",
       "327  Computer Science( CS- AI/ML, CS - Data Science...     2       1   17   3   \n",
       "\n",
       "     st  ppl  spl  ...  sgpa  personality  behaviour  decison  adaptablilty  \\\n",
       "0     8    1    3  ...     3            4          4        3             3   \n",
       "1     2    3    1  ...     2            3          5        1             5   \n",
       "2    11    1    3  ...     2            4          5        2             5   \n",
       "3     7    2    3  ...     3            4          4        4             3   \n",
       "4     6    1    3  ...     2            4          5        3             5   \n",
       "..   ..  ...  ...  ...   ...          ...        ...      ...           ...   \n",
       "323   3    2    1  ...     2            4          5        5             4   \n",
       "324   5    2    1  ...     3            3          3        3             3   \n",
       "325   2    2    3  ...     2            1          4        3             3   \n",
       "326   3    2    3  ...     2            3          4        3             3   \n",
       "327   1    1    3  ...     1            5          3        1             2   \n",
       "\n",
       "     life  hobby  hstudy  match  Total_personality_score  \n",
       "0       3      4       0      1                      3.4  \n",
       "1       5      1       0      1                      3.8  \n",
       "2       4      0       0      1                      4.0  \n",
       "3       2      0       0      1                      3.4  \n",
       "4       4      4       2      1                      4.2  \n",
       "..    ...    ...     ...    ...                      ...  \n",
       "323     2      0       0      0                      4.0  \n",
       "324     3      1       0      0                      3.0  \n",
       "325     3      1       1      0                      2.8  \n",
       "326     3      1       0      1                      3.2  \n",
       "327     3      0       0      0                      2.8  \n",
       "\n",
       "[328 rows x 24 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>certified</th>\n",
       "      <th>sgpa</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1803211033</td>\n",
       "      <td>Naman Chaudhary</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1803211058</td>\n",
       "      <td>Utsav Mehrotra</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1803211003</td>\n",
       "      <td>Akshat Atray</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900321290059</td>\n",
       "      <td>Shivam Sharma</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000321530010</td>\n",
       "      <td>Aditya saini</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rollno             name  pt  st  ppl  spl  c_rate  w_rate  \\\n",
       "0     1803211033  Naman Chaudhary   1   8    1    3       3       3   \n",
       "1     1803211058   Utsav Mehrotra   1   2    3    1       3       4   \n",
       "2     1803211003     Akshat Atray   1  11    1    3       4       5   \n",
       "3  1900321290059    Shivam Sharma   1   7    2    3       2       3   \n",
       "4  2000321530010     Aditya saini   1   6    1    3       3       4   \n",
       "\n",
       "   certified  sgpa  12  \n",
       "0          1     3   3  \n",
       "1          0     2   3  \n",
       "2          1     2   2  \n",
       "3          0     3   2  \n",
       "4          0     2   2  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"rollno\",\"name\",\"pt\",\"st\",\"ppl\",\"spl\",\"c_rate\",\"w_rate\",\"certified\",\"sgpa\",\"12\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ut=pd.read_csv(\"Data Collection.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Your Name</th>\n",
       "      <th>University Roll No</th>\n",
       "      <th>Branch</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Choose your Primary Technology</th>\n",
       "      <th>Choose your Secondary Technology</th>\n",
       "      <th>Primary Programming Language</th>\n",
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       "      <th>What is your B.Tech aggregate SGPA ?</th>\n",
       "      <th>What is your Personality Type ?</th>\n",
       "      <th>What is your behavior ?</th>\n",
       "      <th>How you take your decisions ?</th>\n",
       "      <th>How easily can you adapt changes ?</th>\n",
       "      <th>Your approach towards life ?</th>\n",
       "      <th>Additional Interests.</th>\n",
       "      <th>Are you preparing for any exam ?</th>\n",
       "      <th>With what type of person do you want to be in contact ?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021/11/25 11:20:41 AM GMT+5:30</td>\n",
       "      <td>Naman Chaudhary</td>\n",
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       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Front-End</td>\n",
       "      <td>Cyber Security</td>\n",
       "      <td>C++</td>\n",
       "      <td>...</td>\n",
       "      <td>71% - 80%</td>\n",
       "      <td>7.1 - 8.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Designing</td>\n",
       "      <td>No</td>\n",
       "      <td>Person with same personality traits as yours.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021/11/25 11:20:49 AM GMT+5:30</td>\n",
       "      <td>Utsav Mehrotra</td>\n",
       "      <td>1803211058</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Front-End</td>\n",
       "      <td>Back-End/ API</td>\n",
       "      <td>Python</td>\n",
       "      <td>...</td>\n",
       "      <td>71% - 80%</td>\n",
       "      <td>8.1 - 9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>None of Above</td>\n",
       "      <td>No</td>\n",
       "      <td>Person with same personality traits as yours.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021/11/25 11:21:07 AM GMT+5:30</td>\n",
       "      <td>Akshat Atray</td>\n",
       "      <td>1803211003</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Front-End</td>\n",
       "      <td>IoS Development</td>\n",
       "      <td>C++</td>\n",
       "      <td>...</td>\n",
       "      <td>81% - 90%</td>\n",
       "      <td>8.1 - 9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Sports</td>\n",
       "      <td>No</td>\n",
       "      <td>Person with same personality traits as yours.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021/11/25 11:21:09 AM GMT+5:30</td>\n",
       "      <td>Shivam Sharma</td>\n",
       "      <td>1900321290059</td>\n",
       "      <td>CEIT</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Front-End</td>\n",
       "      <td>Database</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>81% - 90%</td>\n",
       "      <td>7.1 - 8.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Sports</td>\n",
       "      <td>No</td>\n",
       "      <td>Person with same personality traits as yours.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021/11/25 11:22:16 AM GMT+5:30</td>\n",
       "      <td>Aditya saini</td>\n",
       "      <td>2000321530010</td>\n",
       "      <td>Computer Science( CS- AI/ML, CS - Data Science...</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Front-End</td>\n",
       "      <td>Android Development</td>\n",
       "      <td>C++</td>\n",
       "      <td>...</td>\n",
       "      <td>81% - 90%</td>\n",
       "      <td>8.1 - 9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Designing</td>\n",
       "      <td>UPSC</td>\n",
       "      <td>Person with same personality traits as yours.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Timestamp        Your Name University Roll No  \\\n",
       "0  2021/11/25 11:20:41 AM GMT+5:30  Naman Chaudhary         1803211033   \n",
       "1  2021/11/25 11:20:49 AM GMT+5:30   Utsav Mehrotra         1803211058   \n",
       "2  2021/11/25 11:21:07 AM GMT+5:30     Akshat Atray         1803211003   \n",
       "3  2021/11/25 11:21:09 AM GMT+5:30    Shivam Sharma      1900321290059   \n",
       "4  2021/11/25 11:22:16 AM GMT+5:30     Aditya saini      2000321530010   \n",
       "\n",
       "                                              Branch  Current Year Gender  \\\n",
       "0                                               CEIT             4   Male   \n",
       "1                                               CEIT             4   Male   \n",
       "2                                               CEIT             4   Male   \n",
       "3                                               CEIT             3   Male   \n",
       "4  Computer Science( CS- AI/ML, CS - Data Science...             2   Male   \n",
       "\n",
       "   Age Choose your Primary Technology Choose your Secondary Technology  \\\n",
       "0   20                      Front-End                   Cyber Security   \n",
       "1   21                      Front-End                    Back-End/ API   \n",
       "2   21                      Front-End                  IoS Development   \n",
       "3   20                      Front-End                         Database   \n",
       "4   20                      Front-End              Android Development   \n",
       "\n",
       "  Primary Programming Language  ... What was your 12th percentage ?  \\\n",
       "0                          C++  ...                       71% - 80%   \n",
       "1                       Python  ...                       71% - 80%   \n",
       "2                          C++  ...                       81% - 90%   \n",
       "3                            C  ...                       81% - 90%   \n",
       "4                          C++  ...                       81% - 90%   \n",
       "\n",
       "   What is your B.Tech aggregate SGPA ?  What is your Personality Type ?  \\\n",
       "0                             7.1 - 8.0                                4   \n",
       "1                             8.1 - 9.0                                3   \n",
       "2                             8.1 - 9.0                                4   \n",
       "3                             7.1 - 8.0                                4   \n",
       "4                             8.1 - 9.0                                4   \n",
       "\n",
       "  What is your behavior ?  How you take your decisions ?  \\\n",
       "0                        4                             3   \n",
       "1                        5                             1   \n",
       "2                        5                             2   \n",
       "3                        4                             4   \n",
       "4                        5                             3   \n",
       "\n",
       "  How easily can you adapt changes ?  Your approach towards life ?  \\\n",
       "0                                  3                             3   \n",
       "1                                  5                             1   \n",
       "2                                  5                             2   \n",
       "3                                  3                             4   \n",
       "4                                  5                             2   \n",
       "\n",
       "   Additional Interests.  Are you preparing for any exam ?  \\\n",
       "0              Designing                                No   \n",
       "1          None of Above                                No   \n",
       "2                 Sports                                No   \n",
       "3                 Sports                                No   \n",
       "4              Designing                              UPSC   \n",
       "\n",
       "   With what type of person do you want to be in contact ?  \n",
       "0      Person with same personality traits as yours.        \n",
       "1      Person with same personality traits as yours.        \n",
       "2      Person with same personality traits as yours.        \n",
       "3      Person with same personality traits as yours.        \n",
       "4      Person with same personality traits as yours.        \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ut.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def technical_score(d):\n",
    "    score=(500*d[\"ppl\"])+(450*d[\"pt\"])++(400*d[\"c_rate\"])+(350*d[\"spl\"])+(300*d[\"st\"])+(250*d[\"w_rate\"])+(200*d[\"sgpa\"])+(150*d[\"certified\"])+(100*d[\"12\"])\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=technical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   pt  st  ppl  spl  c_rate  w_rate  certified  sgpa  12\n",
       "0   1   8    1    3       3       3          1     3   3\n",
       "1   1   2    3    1       3       4          0     2   3\n",
       "2   1  11    1    3       4       5          1     2   2\n",
       "3   1   7    2    3       2       3          0     3   2\n",
       "4   1   6    1    3       3       4          0     2   2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-9bea6c31490b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp[\"score\"]=temp.apply(technical_score,axis=1)\n"
     ]
    }
   ],
   "source": [
    "temp[\"score\"]=temp.apply(technical_score,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt  st  ppl  spl  c_rate  w_rate  certified  sgpa  12  score\n",
       "0   1   8    1    3       3       3          1     3   3   7400\n",
       "1   1   2    3    1       3       4          0     2   3   5800\n",
       "2   1  11    1    3       4       5          1     2   2   8900\n",
       "3   1   7    2    3       2       3          0     3   2   6950\n",
       "4   1   6    1    3       3       4          0     2   2   6600"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-55-9678a7568e6d>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  technical[\"score\"]=technical.apply(technical_score,axis=1)\n"
     ]
    }
   ],
   "source": [
    "technical[\"score\"]=technical.apply(technical_score,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pt</th>\n",
       "      <th>st</th>\n",
       "      <th>ppl</th>\n",
       "      <th>spl</th>\n",
       "      <th>c_rate</th>\n",
       "      <th>w_rate</th>\n",
       "      <th>certified</th>\n",
       "      <th>sgpa</th>\n",
       "      <th>12</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>7400</td>\n",
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       "      <td>5800</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8900</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pt  st  ppl  spl  c_rate  w_rate  certified  sgpa  12  score\n",
       "0     1   8    1    3       3       3          1     3   3   7400\n",
       "1     1   2    3    1       3       4          0     2   3   5800\n",
       "2     1  11    1    3       4       5          1     2   2   8900\n",
       "3     1   7    2    3       2       3          0     3   2   6950\n",
       "4     1   6    1    3       3       4          0     2   2   6600\n",
       "..   ..  ..  ...  ...     ...     ...        ...   ...  ..    ...\n",
       "323  14   3    2    1       1       1          1     2   2   9950\n",
       "324  12   5    2    1       3       3          0     3   3  11100\n",
       "325   1   2    2    3       1       3          0     2   3   4950\n",
       "326   1   3    2    3       3       2          0     2   2   5700\n",
       "327   3   1    1    3       2       1          0     1   1   4550\n",
       "\n",
       "[328 rows x 10 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "technical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3=KMedoids(metric=\"euclidean\", n_clusters=13,  \n",
    "         init=\"heuristic\", max_iter=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "n_samples=1 should be >= n_clusters=13.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-63-833e12fe3c8a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mkmeans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtechnical\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"score\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    982\u001b[0m                                 accept_large_sparse=False)\n\u001b[0;32m    983\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 984\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    985\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    986\u001b[0m         \u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36m_check_params\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    810\u001b[0m         \u001b[1;31m# n_clusters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    811\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 812\u001b[1;33m             raise ValueError(f\"n_samples={X.shape[0]} should be >= \"\n\u001b[0m\u001b[0;32m    813\u001b[0m                              f\"n_clusters={self.n_clusters}.\")\n\u001b[0;32m    814\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: n_samples=1 should be >= n_clusters=13."
     ]
    }
   ],
   "source": [
    "Y=np.arange([1,329,1])\n",
    "kmeans = KMeans(n_clusters=13, random_state=0).fit([Y,technical[\"score\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=np.arange(start=1,stop=329,step=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr=[] \n",
    "for i in range(328):\n",
    "    arr.append((technical[\"score\"][i],Y[i]))\n",
    "arr=np.array(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=13, random_state=0).fit(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  9,  7,  6, 10,  2, 10,  9,  1,  2,  7,  6,  2,  2,  5, 11,  7,\n",
       "        7,  7, 10,  1, 11,  3,  1,  1,  1,  6, 10, 12,  5,  5,  1,  9,  5,\n",
       "       10, 10,  1,  1,  7, 10,  1, 12,  9,  1,  1,  1,  7,  2, 11,  9, 12,\n",
       "        8,  6,  5,  5,  9, 10,  7,  9,  2,  3, 12,  9,  2,  4,  2, 12,  1,\n",
       "        0,  5, 12,  9,  3,  6,  2,  2,  5,  9,  1,  0, 12, 11,  9, 12,  4,\n",
       "        2, 10,  2,  6, 10, 12,  2,  6,  2,  2,  9,  1,  1, 12,  9,  4, 12,\n",
       "        5,  9,  0,  5,  7,  9,  7,  7,  9, 12,  3,  9,  1,  6,  5, 10,  1,\n",
       "        6,  6, 11,  2,  7, 12,  1,  2,  6,  2, 10,  1, 10, 10,  1,  1,  2,\n",
       "        6,  6,  3, 10,  6,  2,  0,  5,  1,  1,  3,  6,  7,  1,  2,  4,  3,\n",
       "        7,  5,  1, 12,  0,  2,  2,  1,  5,  2, 10,  9, 10,  2,  2, 10, 10,\n",
       "        7,  1, 12, 10,  5,  2,  7,  7, 12,  2,  7,  2,  9, 10,  6,  6, 12,\n",
       "       10, 10,  2,  1,  2,  2, 10, 10, 10,  1,  9,  5,  0, 10,  3, 11,  6,\n",
       "        3,  7,  8,  7, 12,  6, 10,  7,  6,  9,  6,  5, 10,  2,  5,  9,  9,\n",
       "        2,  2,  9,  2,  2,  4,  0,  9, 10,  1,  1, 12,  1,  9, 10,  0,  1,\n",
       "        4,  9,  1,  1, 10,  5,  7,  9,  3, 10,  4,  9, 10,  9, 11,  9,  2,\n",
       "       10,  5, 10,  5,  2,  3,  5, 10,  5,  5,  1, 12,  0,  9,  9,  1, 12,\n",
       "        7,  6, 10,  4,  3,  7,  9,  9,  7, 10,  7,  5,  1,  0,  3,  4,  1,\n",
       "        3,  3,  2,  6,  7,  1,  1,  9,  9, 12,  6,  1,  0, 12,  7,  1,  6,\n",
       "        5, 10,  0,  9,  2,  6,  9,  3,  9,  9,  5,  5,  1,  2,  5,  9,  6,\n",
       "        3, 11,  5,  9,  5])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.predict([[5500,200]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def behaviour_score(s):\n",
    "    st=((350*s[\"behaviour\"])+(300*s[\"personality\"])+(250*s[\"hobby\"])+(200*s[\"hstudy\"])+(150*s[\"adaptablilty\"])+(100*s[\"decison\"])+(50*s[\"life\"]))\n",
    "    if(s[\"match\"]==0):\n",
    "        st*=-1  \n",
    "    return st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-114-f94f0c200f4d>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  behaviour[\"score\"]=behaviour.apply(behaviour_score,axis=1)\n"
     ]
    }
   ],
   "source": [
    "behaviour[\"score\"]=behaviour.apply(behaviour_score,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>personality</th>\n",
       "      <th>behaviour</th>\n",
       "      <th>decison</th>\n",
       "      <th>adaptablilty</th>\n",
       "      <th>life</th>\n",
       "      <th>hobby</th>\n",
       "      <th>hstudy</th>\n",
       "      <th>match</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>4000</td>\n",
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       "      <td>4</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>3550</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>323</th>\n",
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       "      <td>5</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-3050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     personality  behaviour  decison  adaptablilty  life  hobby  hstudy  \\\n",
       "0              4          4        3             3     3      4       0   \n",
       "1              3          5        1             5     5      1       0   \n",
       "2              4          5        2             5     4      0       0   \n",
       "3              4          4        4             3     2      0       0   \n",
       "4              4          5        3             5     4      4       2   \n",
       "..           ...        ...      ...           ...   ...    ...     ...   \n",
       "323            4          5        5             4     2      0       0   \n",
       "324            3          3        3             3     3      1       0   \n",
       "325            1          4        3             3     3      1       1   \n",
       "326            3          4        3             3     3      1       0   \n",
       "327            5          3        1             2     3      0       0   \n",
       "\n",
       "     match  score  \n",
       "0        1   4500  \n",
       "1        1   4000  \n",
       "2        1   4100  \n",
       "3        1   3550  \n",
       "4        1   5600  \n",
       "..     ...    ...  \n",
       "323      0  -4150  \n",
       "324      0  -3100  \n",
       "325      0  -3050  \n",
       "326      1   3450  \n",
       "327      0  -3100  \n",
       "\n",
       "[328 rows x 9 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behaviour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr=[] \n",
    "for i in range(328):\n",
    "    arr.append((technical[\"score\"][i],behaviour[\"score\"][i]))\n",
    "arr=np.array(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans1 = KMeans(n_clusters=13, random_state=0).fit(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  7,  0,  5,  7,  7,  4,  2,  0,  7, 10,  7,  2,  2, 12,  3,  9,\n",
       "        9, 10,  8,  1, 10,  9,  0,  9,  1,  5,  5,  5,  2, 11,  0,  2, 12,\n",
       "        8,  8,  9,  9,  9,  7,  9,  0, 12,  1,  9,  1,  9,  4, 10,  7,  0,\n",
       "        6,  5,  2, 12,  2,  8,  1, 11,  2, 10,  5,  2,  2,  6,  7,  0,  1,\n",
       "       12, 11,  0,  2,  9,  5,  7,  7, 12, 12,  0, 12,  5,  3,  7,  0,  3,\n",
       "        8,  5,  7,  4,  5,  5,  7,  7,  2,  7, 11,  0,  0,  8,  2,  3,  0,\n",
       "       11,  4, 12, 12,  9, 11, 10,  9, 12,  8, 10, 11,  0,  5, 11,  4,  9,\n",
       "        8,  5,  3,  2,  9,  5,  0,  4,  8,  4,  7,  0,  7,  7,  0,  0,  7,\n",
       "        8,  5,  6,  7,  4,  7, 11, 12,  1,  1, 10,  8,  9,  0,  8,  3, 10,\n",
       "       10, 11,  9,  1, 12,  4,  2,  0,  2,  7,  7,  4,  5,  2,  2,  7,  5,\n",
       "        9,  0,  1,  5, 12,  2,  9,  9,  0,  2,  9,  2,  2,  7,  5,  8,  5,\n",
       "        5,  5,  4,  5,  7,  4,  8,  7,  8,  0, 11, 12, 11,  5,  6,  3,  7,\n",
       "        9,  1,  3,  1,  5,  7,  7,  1,  5, 11,  4, 11,  7,  2, 12,  2,  2,\n",
       "        8,  2,  4,  2,  2,  3, 12,  2,  7,  5,  0,  5,  0,  2,  5, 11,  1,\n",
       "        3, 12,  0,  0,  8, 12,  9,  2,  9,  8,  3, 11,  5, 11, 10,  2,  2,\n",
       "        7,  2,  5,  4,  7, 10, 12,  4, 12, 11,  0,  5, 12, 11, 11,  9,  5,\n",
       "        9,  5,  7,  3, 10,  9,  4, 11,  9,  2, 10, 12,  0, 11, 10,  3,  1,\n",
       "       10, 10,  7,  5, 10,  0,  1,  4,  7,  1,  5,  1, 12,  0, 10,  9,  5,\n",
       "       11,  5, 12,  2,  7,  5, 12, 10,  2,  2, 11, 11,  1,  2, 12,  2,  5,\n",
       "        6,  6, 11,  2, 11])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans1.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans1.predict([[6200,3450]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_medoids = KMedoids(metric=\"euclidean\", n_clusters=13,  \n",
    "         init=\"heuristic\", max_iter=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\utkar\\pycharmprojects\\pythonproject\\venv\\lib\\site-packages\\sklearn_extra\\cluster\\_k_medoids.py:243: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KMedoids(max_iter=2, n_clusters=13)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_medoids.fit(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12, 10,  5,  9,  9,  6,  4, 11,  9, 11,  9,  8,  8, 12, 10, 10,\n",
       "       10, 11,  6,  6, 10, 10, 11,  3,  6,  2,  7,  0,  4,  6, 11, 12, 12,\n",
       "        6,  6, 10,  3, 10,  9,  3, 11, 12,  6,  3,  6, 10,  6, 10,  9,  3,\n",
       "        6,  5,  4, 12, 12,  6,  6,  6,  4, 10,  3,  4,  4,  6,  9, 11,  6,\n",
       "       12,  6,  3,  4, 10,  2,  9,  9, 12, 12, 10, 12,  3, 10,  9,  5, 10,\n",
       "        6,  5,  9,  6,  7,  3,  9,  9,  4,  9,  6, 10,  3,  6,  4, 10, 11,\n",
       "        6,  6, 12, 12, 10,  6, 10, 10, 12,  6, 10,  6, 11,  1,  6,  6,  3,\n",
       "        6,  5, 10,  4, 10,  3, 11,  6,  6,  6,  9, 11,  9,  9, 11, 11,  9,\n",
       "        6,  0,  6,  9,  6,  9,  6, 12,  6,  6, 10,  6, 10, 11,  6, 10, 10,\n",
       "       11,  6,  3,  6, 12,  6,  4, 11, 12,  9,  8,  6,  7,  8,  4,  9,  7,\n",
       "       10,  3,  6,  8, 12,  8, 10, 10, 11,  8, 10,  4,  4,  9,  8,  6,  3,\n",
       "        8,  7,  6,  3,  9,  6,  6,  9,  6, 11,  6, 12,  6,  7,  6, 10,  8,\n",
       "       10,  6, 10,  6,  3,  9,  9,  6,  5,  6,  6,  6,  9,  4, 12,  4, 12,\n",
       "        6,  4,  6,  4,  4, 10, 12,  4,  9,  3, 11,  3, 11, 12,  7,  6,  6,\n",
       "       10, 12, 11,  3,  6, 12, 10,  4, 10,  6, 10,  6,  7,  6, 10,  4,  4,\n",
       "        9, 12,  5,  6,  9, 10, 12,  6, 12,  6,  3,  3, 12,  6,  6,  3,  3,\n",
       "       10,  1,  9, 10, 10, 10,  6,  6, 10,  7, 11, 12, 11,  6, 10, 10,  6,\n",
       "       10, 10,  8,  2, 10,  3,  6,  6, 12,  6,  8,  6, 12, 11, 10,  3,  0,\n",
       "        6,  8, 12, 12,  9,  1, 12, 10,  4, 12,  6,  6,  6,  4, 12,  4,  1,\n",
       "        6,  6,  6, 12,  6], dtype=int64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_medoids.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7], dtype=int64)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_medoids.predict([[6200,3450]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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