Student_Perfomance (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bece4114",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40f445a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"new_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "191ff3fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Roll No.</th>\n",
       "      <th>Mis No.</th>\n",
       "      <th>Email Id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>10th %</th>\n",
       "      <th>12th %</th>\n",
       "      <th>CET</th>\n",
       "      <th>...</th>\n",
       "      <th>Related Field</th>\n",
       "      <th>Certified Courses</th>\n",
       "      <th>Certificates(Sports)</th>\n",
       "      <th>KT</th>\n",
       "      <th>no.of kts</th>\n",
       "      <th>One word about BVCOE</th>\n",
       "      <th>Unnamed: 28</th>\n",
       "      <th>Unnamed: 29</th>\n",
       "      <th>Unnamed: 30</th>\n",
       "      <th>Unnamed: 31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2-16-2023 14:22:28</td>\n",
       "      <td>Utkarsha</td>\n",
       "      <td>Baviskar</td>\n",
       "      <td>1</td>\n",
       "      <td>21142003</td>\n",
       "      <td>utkarshayy02@gmail.com</td>\n",
       "      <td>Female</td>\n",
       "      <td>85.0</td>\n",
       "      <td>92.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>Android development</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Motivating</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-5-2023 13:30:38</td>\n",
       "      <td>Sanket</td>\n",
       "      <td>Bhosale</td>\n",
       "      <td>2</td>\n",
       "      <td>20141001</td>\n",
       "      <td>sanketbhosale1833@gmail.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>88.8</td>\n",
       "      <td>76.92</td>\n",
       "      <td>83.85</td>\n",
       "      <td>...</td>\n",
       "      <td>Frontend Web Development</td>\n",
       "      <td>Java, Web Development, Data Science, Machine L...</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Innovative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2-5-2023 12:09:47</td>\n",
       "      <td>Khushbu</td>\n",
       "      <td>Bonde</td>\n",
       "      <td>3</td>\n",
       "      <td>20141002</td>\n",
       "      <td>khushbubonde30@gmail.com</td>\n",
       "      <td>Female</td>\n",
       "      <td>90.0</td>\n",
       "      <td>83.23</td>\n",
       "      <td>87.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2-17-2023 14:20:00</td>\n",
       "      <td>Anannya</td>\n",
       "      <td>Chammat</td>\n",
       "      <td>4</td>\n",
       "      <td>20141003</td>\n",
       "      <td>anannyachamat269@gmail.com</td>\n",
       "      <td>Female</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>Miss Prachi Mule</td>\n",
       "      <td>Java, Pyhton, web development</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>nice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2-16-2023 16:46:37</td>\n",
       "      <td>Abhishek</td>\n",
       "      <td>Chauhan</td>\n",
       "      <td>5</td>\n",
       "      <td>20141004</td>\n",
       "      <td>chauhan.abhishek.motilal@gmail.com</td>\n",
       "      <td>Male</td>\n",
       "      <td>84.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>79.78</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python, Web Development</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nothing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Timestamp First Name  Last Name  Roll No.   Mis No.  \\\n",
       "0     2-16-2023 14:22:28  Utkarsha   Baviskar          1  21142003   \n",
       "1      2-5-2023 13:30:38    Sanket    Bhosale          2  20141001   \n",
       "2      2-5-2023 12:09:47   Khushbu       Bonde         3  20141002   \n",
       "3     2-17-2023 14:20:00    Anannya    Chammat         4  20141003   \n",
       "4     2-16-2023 16:46:37   Abhishek    Chauhan         5  20141004   \n",
       "\n",
       "                             Email Id  Gender  10th %  12th %   CET   ...  \\\n",
       "0              utkarshayy02@gmail.com  Female    85.0   92.30   0.00  ...   \n",
       "1         sanketbhosale1833@gmail.com    Male    88.8   76.92  83.85  ...   \n",
       "2            khushbubonde30@gmail.com  Female    90.0   83.23  87.00  ...   \n",
       "3          anannyachamat269@gmail.com  Female    84.0   94.00   0.00  ...   \n",
       "4  chauhan.abhishek.motilal@gmail.com    Male    84.0   64.00  79.78  ...   \n",
       "\n",
       "               Related Field  \\\n",
       "0       Android development    \n",
       "1  Frontend Web Development    \n",
       "2                        NaN   \n",
       "3           Miss Prachi Mule   \n",
       "4                        NaN   \n",
       "\n",
       "                                  Certified Courses   Certificates(Sports)  \\\n",
       "0                                              Other                    No   \n",
       "1  Java, Web Development, Data Science, Machine L...                    No   \n",
       "2                                             Python                    No   \n",
       "3                      Java, Pyhton, web development                   yes   \n",
       "4                            Python, Web Development                    No   \n",
       "\n",
       "   KT  no.of kts One word about BVCOE  Unnamed: 28 Unnamed: 29 Unnamed: 30  \\\n",
       "0   1          1          Motivating           NaN         NaN         NaN   \n",
       "1   0        NaN          Innovative           NaN         NaN         NaN   \n",
       "2   0        NaN                 Nice          NaN         NaN         NaN   \n",
       "3   1          1                 nice          NaN         NaN         NaN   \n",
       "4   0        NaN              Nothing          NaN         NaN         NaN   \n",
       "\n",
       "   Unnamed: 31  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
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     "execution_count": 3,
     "metadata": {},
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    }
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "daffbe63",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Timestamp', 'Email Id','Related Field','One word about BVCOE','Unnamed: 28','Unnamed: 29','Unnamed: 30','Unnamed: 31'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abd1c1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Mis No.','Clubs'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e4ab40b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Roll No.</th>\n",
       "      <th>Gender</th>\n",
       "      <th>10th %</th>\n",
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       "      <th>SEM II SGPA</th>\n",
       "      <th>...</th>\n",
       "      <th>Sports</th>\n",
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       "      <th>in_clubs</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Utkarsha</td>\n",
       "      <td>Baviskar</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>85.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>Badminton</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sanket</td>\n",
       "      <td>Bhosale</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>88.8</td>\n",
       "      <td>76.92</td>\n",
       "      <td>83.85</td>\n",
       "      <td>66.67</td>\n",
       "      <td>9.78</td>\n",
       "      <td>9.75</td>\n",
       "      <td>...</td>\n",
       "      <td>Cricket</td>\n",
       "      <td>1</td>\n",
       "      <td>Reading, Playing Cricket</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Java, Web Development, Data Science, Machine L...</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Khushbu</td>\n",
       "      <td>Bonde</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>90.0</td>\n",
       "      <td>83.23</td>\n",
       "      <td>87.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.43</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Reading</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Python</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anannya</td>\n",
       "      <td>Chammat</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.70</td>\n",
       "      <td>...</td>\n",
       "      <td>Badminton, Kho-kho</td>\n",
       "      <td>1</td>\n",
       "      <td>Graphic logo making</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Java, Pyhton, web development</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abhishek</td>\n",
       "      <td>Chauhan</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>84.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>79.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.83</td>\n",
       "      <td>9.70</td>\n",
       "      <td>...</td>\n",
       "      <td>Nothing</td>\n",
       "      <td>0</td>\n",
       "      <td>Reading Books</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Python, Web Development</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Last Name  Roll No.  Gender  10th %  12th %   CET    JEE   \\\n",
       "0  Utkarsha   Baviskar          1  Female    85.0   92.30   0.00   0.00   \n",
       "1    Sanket    Bhosale          2    Male    88.8   76.92  83.85  66.67   \n",
       "2   Khushbu       Bonde         3  Female    90.0   83.23  87.00  57.00   \n",
       "3    Anannya    Chammat         4  Female    84.0   94.00   0.00   0.00   \n",
       "4   Abhishek    Chauhan         5    Male    84.0   64.00  79.78   0.00   \n",
       "\n",
       "   SEM I SGPA  SEM II SGPA  ...              Sports  played_sports  \\\n",
       "0        0.00         0.00  ...          Badminton               1   \n",
       "1        9.78         9.75  ...            Cricket               1   \n",
       "2        9.44         9.43  ...                  No              0   \n",
       "3        9.94         9.70  ...  Badminton, Kho-kho              1   \n",
       "4        9.83         9.70  ...            Nothing               0   \n",
       "\n",
       "                     Hobbies  in_clubs Certifications  Internships  \\\n",
       "0                      Dance         1              0          1.0   \n",
       "1  Reading, Playing Cricket          0              1          1.0   \n",
       "2                    Reading         1              0          0.0   \n",
       "3        Graphic logo making         1              1          1.0   \n",
       "4              Reading Books         0              1          0.0   \n",
       "\n",
       "                                  Certified Courses   Certificates(Sports) KT  \\\n",
       "0                                              Other                    No  1   \n",
       "1  Java, Web Development, Data Science, Machine L...                    No  0   \n",
       "2                                             Python                    No  0   \n",
       "3                      Java, Pyhton, web development                   yes  1   \n",
       "4                            Python, Web Development                    No  0   \n",
       "\n",
       "  no.of kts  \n",
       "0         1  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3         1  \n",
       "4       NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98d09cfc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab8fa1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['average_sem'] = df[['SEMIIISGPA','SEMIVSGPA']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c4b0f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6fa52703",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1=df.loc[:,['10th%','12th%','SEMISGPA','SEMIISGPA','SEMIIISGPA','SEMIVSGPA','CET','JEE','average_sem']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f5e1c33",
   "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>10th%</th>\n",
       "      <th>12th%</th>\n",
       "      <th>SEMISGPA</th>\n",
       "      <th>SEMIISGPA</th>\n",
       "      <th>SEMIIISGPA</th>\n",
       "      <th>SEMIVSGPA</th>\n",
       "      <th>CET</th>\n",
       "      <th>JEE</th>\n",
       "      <th>average_sem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85.0</td>\n",
       "      <td>92.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.8</td>\n",
       "      <td>76.92</td>\n",
       "      <td>9.78</td>\n",
       "      <td>9.75</td>\n",
       "      <td>9.39</td>\n",
       "      <td>8.83</td>\n",
       "      <td>83.85</td>\n",
       "      <td>66.67</td>\n",
       "      <td>9.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.0</td>\n",
       "      <td>83.23</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.52</td>\n",
       "      <td>8.04</td>\n",
       "      <td>87.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>8.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.70</td>\n",
       "      <td>8.83</td>\n",
       "      <td>8.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>9.83</td>\n",
       "      <td>9.70</td>\n",
       "      <td>8.22</td>\n",
       "      <td>7.39</td>\n",
       "      <td>79.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   10th%  12th%  SEMISGPA  SEMIISGPA  SEMIIISGPA  SEMIVSGPA    CET    JEE  \\\n",
       "0   85.0  92.30      0.00       0.00        9.04       0.00   0.00   0.00   \n",
       "1   88.8  76.92      9.78       9.75        9.39       8.83  83.85  66.67   \n",
       "2   90.0  83.23      9.44       9.43        9.52       8.04  87.00  57.00   \n",
       "3   84.0  94.00      9.94       9.70        8.83       8.04   0.00   0.00   \n",
       "4   84.0  64.00      9.83       9.70        8.22       7.39  79.78   0.00   \n",
       "\n",
       "   average_sem  \n",
       "0        4.520  \n",
       "1        9.110  \n",
       "2        8.780  \n",
       "3        8.435  \n",
       "4        7.805  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03ed6d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df_1.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac0f0a9f",
   "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>10th%</th>\n",
       "      <th>12th%</th>\n",
       "      <th>SEMISGPA</th>\n",
       "      <th>SEMIISGPA</th>\n",
       "      <th>SEMIIISGPA</th>\n",
       "      <th>SEMIVSGPA</th>\n",
       "      <th>CET</th>\n",
       "      <th>JEE</th>\n",
       "      <th>average_sem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.890985</td>\n",
       "      <td>0.981915</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.454957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.930818</td>\n",
       "      <td>0.818298</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.898618</td>\n",
       "      <td>0.740778</td>\n",
       "      <td>0.916960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.885426</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.932376</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.883744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.880503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.849019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.880503</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.854999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.785606</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.920335</td>\n",
       "      <td>0.982979</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.885757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.880503</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.675169</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.807247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.890985</td>\n",
       "      <td>0.744681</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.482263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.853045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.901468</td>\n",
       "      <td>0.712766</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.820332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.966457</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.946630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.402617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       10th%     12th%  SEMISGPA  SEMIISGPA  SEMIIISGPA  SEMIVSGPA       CET  \\\n",
       "0   0.890985  0.981915     0.000      0.000       0.904      0.000  0.000000   \n",
       "1   0.930818  0.818298     0.978      0.975       0.939      0.883  0.898618   \n",
       "2   0.943396  0.885426     0.944      0.943       0.952      0.804  0.932376   \n",
       "3   0.880503  1.000000     0.994      0.970       0.883      0.804  0.000000   \n",
       "4   0.880503  0.680851     0.983      0.970       0.822      0.739  0.854999   \n",
       "..       ...       ...       ...        ...         ...        ...       ...   \n",
       "71  0.920335  0.982979     0.000      0.000       0.943      0.817  0.000000   \n",
       "72  0.880503  0.638298     0.972      0.895       0.843      0.761  0.675169   \n",
       "73  0.890985  0.744681     1.000      0.960       0.930      0.765  0.482263   \n",
       "74  0.901468  0.712766     0.989      0.900       0.890      0.740  0.000000   \n",
       "75  0.966457  0.680851     0.861      0.673       0.800      0.000  0.946630   \n",
       "\n",
       "         JEE  average_sem  \n",
       "0   0.000000     0.454957  \n",
       "1   0.740778     0.916960  \n",
       "2   0.633333     0.883744  \n",
       "3   0.000000     0.849019  \n",
       "4   0.000000     0.785606  \n",
       "..       ...          ...  \n",
       "71  0.000000     0.885757  \n",
       "72  0.788889     0.807247  \n",
       "73  1.000000     0.853045  \n",
       "74  0.000000     0.820332  \n",
       "75  0.000000     0.402617  \n",
       "\n",
       "[76 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df_max_scaled = df_1.copy()\n",
    "  \n",
    "\n",
    "for column in df_max_scaled.columns:\n",
    "    df_max_scaled[column] = df_max_scaled[column]  / df_max_scaled[column].abs().max()\n",
    "      \n",
    "\n",
    "display(df_max_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "545f6c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns                      \n",
    "import matplotlib.pyplot as plt             \n",
    "%matplotlib inline     \n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2e02e4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['12th%'], df['SEMIVSGPA'])\n",
    "ax.set_xlabel('12th%')\n",
    "ax.set_ylabel('SEMIVSGPA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03958b8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f93a52ba",
   "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>10th%</th>\n",
       "      <th>12th%</th>\n",
       "      <th>SEMISGPA</th>\n",
       "      <th>SEMIISGPA</th>\n",
       "      <th>SEMIIISGPA</th>\n",
       "      <th>SEMIVSGPA</th>\n",
       "      <th>CET</th>\n",
       "      <th>JEE</th>\n",
       "      <th>average_sem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10th%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.425319</td>\n",
       "      <td>0.131946</td>\n",
       "      <td>0.113271</td>\n",
       "      <td>0.417482</td>\n",
       "      <td>0.328172</td>\n",
       "      <td>0.415090</td>\n",
       "      <td>0.331194</td>\n",
       "      <td>0.392677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12th%</th>\n",
       "      <td>0.425319</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.473336</td>\n",
       "      <td>-0.393034</td>\n",
       "      <td>0.486185</td>\n",
       "      <td>0.223297</td>\n",
       "      <td>-0.057853</td>\n",
       "      <td>-0.005287</td>\n",
       "      <td>0.316338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMISGPA</th>\n",
       "      <td>0.131946</td>\n",
       "      <td>-0.473336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929582</td>\n",
       "      <td>0.059307</td>\n",
       "      <td>0.218567</td>\n",
       "      <td>0.473680</td>\n",
       "      <td>0.348929</td>\n",
       "      <td>0.208336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIISGPA</th>\n",
       "      <td>0.113271</td>\n",
       "      <td>-0.393034</td>\n",
       "      <td>0.929582</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022123</td>\n",
       "      <td>0.213097</td>\n",
       "      <td>0.522251</td>\n",
       "      <td>0.350141</td>\n",
       "      <td>0.194441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIIISGPA</th>\n",
       "      <td>0.417482</td>\n",
       "      <td>0.486185</td>\n",
       "      <td>0.059307</td>\n",
       "      <td>0.022123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.356274</td>\n",
       "      <td>0.093741</td>\n",
       "      <td>0.093066</td>\n",
       "      <td>0.559263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEMIVSGPA</th>\n",
       "      <td>0.328172</td>\n",
       "      <td>0.223297</td>\n",
       "      <td>0.218567</td>\n",
       "      <td>0.213097</td>\n",
       "      <td>0.356274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174738</td>\n",
       "      <td>0.174095</td>\n",
       "      <td>0.973844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CET</th>\n",
       "      <td>0.415090</td>\n",
       "      <td>-0.057853</td>\n",
       "      <td>0.473680</td>\n",
       "      <td>0.522251</td>\n",
       "      <td>0.093741</td>\n",
       "      <td>0.174738</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.413064</td>\n",
       "      <td>0.177824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JEE</th>\n",
       "      <td>0.331194</td>\n",
       "      <td>-0.005287</td>\n",
       "      <td>0.348929</td>\n",
       "      <td>0.350141</td>\n",
       "      <td>0.093066</td>\n",
       "      <td>0.174095</td>\n",
       "      <td>0.413064</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_sem</th>\n",
       "      <td>0.392677</td>\n",
       "      <td>0.316338</td>\n",
       "      <td>0.208336</td>\n",
       "      <td>0.194441</td>\n",
       "      <td>0.559263</td>\n",
       "      <td>0.973844</td>\n",
       "      <td>0.177824</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                10th%     12th%  SEMISGPA  SEMIISGPA  SEMIIISGPA  SEMIVSGPA  \\\n",
       "10th%        1.000000  0.425319  0.131946   0.113271    0.417482   0.328172   \n",
       "12th%        0.425319  1.000000 -0.473336  -0.393034    0.486185   0.223297   \n",
       "SEMISGPA     0.131946 -0.473336  1.000000   0.929582    0.059307   0.218567   \n",
       "SEMIISGPA    0.113271 -0.393034  0.929582   1.000000    0.022123   0.213097   \n",
       "SEMIIISGPA   0.417482  0.486185  0.059307   0.022123    1.000000   0.356274   \n",
       "SEMIVSGPA    0.328172  0.223297  0.218567   0.213097    0.356274   1.000000   \n",
       "CET          0.415090 -0.057853  0.473680   0.522251    0.093741   0.174738   \n",
       "JEE          0.331194 -0.005287  0.348929   0.350141    0.093066   0.174095   \n",
       "average_sem  0.392677  0.316338  0.208336   0.194441    0.559263   0.973844   \n",
       "\n",
       "                  CET       JEE  average_sem  \n",
       "10th%        0.415090  0.331194     0.392677  \n",
       "12th%       -0.057853 -0.005287     0.316338  \n",
       "SEMISGPA     0.473680  0.348929     0.208336  \n",
       "SEMIISGPA    0.522251  0.350141     0.194441  \n",
       "SEMIIISGPA   0.093741  0.093066     0.559263  \n",
       "SEMIVSGPA    0.174738  0.174095     0.973844  \n",
       "CET          1.000000  0.413064     0.177824  \n",
       "JEE          0.413064  1.000000     0.177089  \n",
       "average_sem  0.177824  0.177089     1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "c= df_1.corr()\n",
    "sns.heatmap(c,cmap=\"BrBG\",annot=True)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ca52e9ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Female' 'Male' 'male']\n"
     ]
    }
   ],
   "source": [
    "print(df['Gender'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d98b023",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = df['Gender'].replace(['male'], 'Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f355433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reading books                                       3\n",
       "Reading                                             2\n",
       "Reading books                                       2\n",
       "cricket                                             2\n",
       "Dance                                               1\n",
       "                                                   ..\n",
       "Coding                                              1\n",
       "Drawing                                             1\n",
       "Listening to music                                  1\n",
       "Drawing, panting                                    1\n",
       "Coding , playing video games, chess , Badminton     1\n",
       "Name: Hobbies, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hobbies'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f4f47ad1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Female  Male  KT  Internships  Certifications  in_clubs\n",
      "0        1     0   1          1.0               0         1\n",
      "1        0     1   0          1.0               1         0\n",
      "2        1     0   0          0.0               0         1\n",
      "3        1     0   1          1.0               1         1\n",
      "4        0     1   0          0.0               1         0\n",
      "..     ...   ...  ..          ...             ...       ...\n",
      "71       0     1   0          1.0               0         0\n",
      "72       0     1   0          0.0               0         1\n",
      "73       0     1   0          1.0               1         1\n",
      "74       0     1   0          0.0               0         1\n",
      "75       0     1   1          0.0               1         0\n",
      "\n",
      "[76 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# one_hot_encoded_data = pd.get_dummies(df_2, columns = ['Gender'])\n",
    "print(one_hot_encoded_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa4e02ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Female\n",
       "1      Male\n",
       "2    Female\n",
       "3    Female\n",
       "4      Male\n",
       "Name: Gender, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = df['Gender']\n",
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a0a35294",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoded_data.drop(['Female','Male'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a3814441",
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_col = df[\"played_sports\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "64995c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoded_data = one_hot_encoded_data.join(extracted_col1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8f460a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_col1 = df[\"in_clubs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "48418f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_col2 = df[\"Certifications\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "95d5eb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_col3 = df[\"Internships\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9659b206",
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_col4 = df[\"KT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4525c37e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c3fea1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '10th%')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_max_scaled[\"average_sem\"], \n",
    "            df_max_scaled[\"10th%\"])\n",
    "\n",
    "plt.xlabel(\"average_sem\")\n",
    "plt.ylabel(\"10th%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5277b2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '12th%')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_max_scaled[\"average_sem\"], \n",
    "            df_max_scaled[\"12th%\"])\n",
    "\n",
    "plt.xlabel(\"average_sem\")\n",
    "plt.ylabel(\"12th%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "50237a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant_cols = [\"average_sem\", \"10th%\", \"12th%\"]\n",
    "\n",
    "clust_df = clust_df[relevant_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "06424302",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_max_scaled = pd.merge(one_hot_encoded_data,df_max_scaled,left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cfb5d50c",
   "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>KT</th>\n",
       "      <th>Internships</th>\n",
       "      <th>Certifications</th>\n",
       "      <th>in_clubs</th>\n",
       "      <th>Female_x</th>\n",
       "      <th>Male_x</th>\n",
       "      <th>Female_y</th>\n",
       "      <th>Male_y</th>\n",
       "      <th>10th%</th>\n",
       "      <th>12th%</th>\n",
       "      <th>SEMISGPA</th>\n",
       "      <th>SEMIISGPA</th>\n",
       "      <th>SEMIIISGPA</th>\n",
       "      <th>SEMIVSGPA</th>\n",
       "      <th>CET</th>\n",
       "      <th>JEE</th>\n",
       "      <th>average_sem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.890985</td>\n",
       "      <td>0.981915</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.454957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.930818</td>\n",
       "      <td>0.818298</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.898618</td>\n",
       "      <td>0.740778</td>\n",
       "      <td>0.916960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.885426</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.932376</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.883744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.880503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.849019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.880503</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.854999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.785606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.920335</td>\n",
       "      <td>0.982979</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.885757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.880503</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.675169</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.807247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.890985</td>\n",
       "      <td>0.744681</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.482263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.853045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.901468</td>\n",
       "      <td>0.712766</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.820332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.966457</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.946630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.402617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    KT  Internships  Certifications  in_clubs  Female_x  Male_x  Female_y  \\\n",
       "0    1          1.0               0         1         1       0         1   \n",
       "1    0          1.0               1         0         0       1         0   \n",
       "2    0          0.0               0         1         1       0         1   \n",
       "3    1          1.0               1         1         1       0         1   \n",
       "4    0          0.0               1         0         0       1         0   \n",
       "..  ..          ...             ...       ...       ...     ...       ...   \n",
       "71   0          1.0               0         0         0       1         0   \n",
       "72   0          0.0               0         1         0       1         0   \n",
       "73   0          1.0               1         1         0       1         0   \n",
       "74   0          0.0               0         1         0       1         0   \n",
       "75   1          0.0               1         0         0       1         0   \n",
       "\n",
       "    Male_y     10th%     12th%  SEMISGPA  SEMIISGPA  SEMIIISGPA  SEMIVSGPA  \\\n",
       "0        0  0.890985  0.981915     0.000      0.000       0.904      0.000   \n",
       "1        1  0.930818  0.818298     0.978      0.975       0.939      0.883   \n",
       "2        0  0.943396  0.885426     0.944      0.943       0.952      0.804   \n",
       "3        0  0.880503  1.000000     0.994      0.970       0.883      0.804   \n",
       "4        1  0.880503  0.680851     0.983      0.970       0.822      0.739   \n",
       "..     ...       ...       ...       ...        ...         ...        ...   \n",
       "71       1  0.920335  0.982979     0.000      0.000       0.943      0.817   \n",
       "72       1  0.880503  0.638298     0.972      0.895       0.843      0.761   \n",
       "73       1  0.890985  0.744681     1.000      0.960       0.930      0.765   \n",
       "74       1  0.901468  0.712766     0.989      0.900       0.890      0.740   \n",
       "75       1  0.966457  0.680851     0.861      0.673       0.800      0.000   \n",
       "\n",
       "         CET       JEE  average_sem  \n",
       "0   0.000000  0.000000     0.454957  \n",
       "1   0.898618  0.740778     0.916960  \n",
       "2   0.932376  0.633333     0.883744  \n",
       "3   0.000000  0.000000     0.849019  \n",
       "4   0.854999  0.000000     0.785606  \n",
       "..       ...       ...          ...  \n",
       "71  0.000000  0.000000     0.885757  \n",
       "72  0.675169  0.788889     0.807247  \n",
       "73  0.482263  1.000000     0.853045  \n",
       "74  0.000000  0.000000     0.820332  \n",
       "75  0.946630  0.000000     0.402617  \n",
       "\n",
       "[76 rows x 17 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_max_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3214385c",
   "metadata": {},
   "outputs": [],
   "source": [
    "clust_df = df_max_scaled.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "74624caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(clust_df)\n",
    "\n",
    "scaled_data = scaler.transform(clust_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "000c51f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_clusters(df, maximum_K):\n",
    "    \n",
    "    clusters_centers = []\n",
    "    k_values = []\n",
    "    \n",
    "    for k in range(1, maximum_K):\n",
    "        \n",
    "        kmeans_model = KMeans(n_clusters = k)\n",
    "        kmeans_model.fit(df)\n",
    "        \n",
    "        clusters_centers.append(kmeans_model.inertia_)\n",
    "        k_values.append(k)\n",
    "        \n",
    "    \n",
    "    return clusters_centers, k_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "35379d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_elbow_plot(clusters_centers, k_values):\n",
    "    \n",
    "    figure = plt.subplots(figsize = (12, 6))\n",
    "    plt.plot(k_values, clusters_centers, 'o-', color = 'orange')\n",
    "    plt.xlabel(\"Number of Clusters (K)\")\n",
    "    plt.ylabel(\"Cluster Inertia\")\n",
    "    plt.title(\"Elbow Plot of KMeans\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d63dfda1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sanika\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters_centers, k_values = find_best_clusters(scaled_data, 12)\n",
    "\n",
    "generate_elbow_plot(clusters_centers, k_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7bf823a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_model = KMeans(n_clusters = 4)\n",
    "\n",
    "kmeans_model.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "72e4dd3a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    average_sem     10th%     12th%  clusters\n",
      "0      0.454957  0.890985  0.981915         3\n",
      "1      0.916960  0.930818  0.818298         0\n",
      "2      0.883744  0.943396  0.885426         0\n",
      "3      0.849019  0.880503  1.000000         0\n",
      "4      0.785606  0.880503  0.680851         2\n",
      "..          ...       ...       ...       ...\n",
      "71     0.885757  0.920335  0.982979         0\n",
      "72     0.807247  0.880503  0.638298         2\n",
      "73     0.853045  0.890985  0.744681         2\n",
      "74     0.820332  0.901468  0.712766         2\n",
      "75     0.402617  0.966457  0.680851         3\n",
      "\n",
      "[76 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "clust_df[\"clusters\"] = kmeans_model.labels_\n",
    "\n",
    "print(clust_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4ef8fc46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1dd9fc1b820>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(clust_df[\"average_sem\"], \n",
    "            clust_df[\"10th%\"], \n",
    "            c = clust_df[\"clusters\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57e796d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6f10238a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9d5925dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "58d8c922",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "dcb2c053",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sanika\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster    \n",
      "0        39    1.000000\n",
      "         60    0.993457\n",
      "         44    0.967287\n",
      "         61    0.956215\n",
      "         51    0.954202\n",
      "1        35    0.949673\n",
      "         32    0.895320\n",
      "         49    0.890287\n",
      "         64    0.883744\n",
      "         67    0.879215\n",
      "2        0     0.454957\n",
      "         21    0.441872\n",
      "         14    0.431303\n",
      "         75    0.402617\n",
      "3        57    0.454957\n",
      "         8     0.354303\n",
      "Name: average_sem, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(clust_df)\n",
    "\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(data_scaled)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "kmeans = KMeans(n_clusters=4, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "kmeans.fit(data_scaled)\n",
    "\n",
    "\n",
    "cluster_labels = kmeans.predict(data_scaled)\n",
    "clust_df['cluster'] = cluster_labels\n",
    "top_students = clust_df.groupby('cluster')['average_sem'].nlargest(5)\n",
    "print(top_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f2e2c161",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f63beec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b5e1058",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5764e251",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls_data = pd.merge(df[['FirstName','LastName']],clust_df['clusters'],left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "08e8764b",
   "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>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Utkarsha</td>\n",
       "      <td>Baviskar</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sanket</td>\n",
       "      <td>Bhosale</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Khushbu</td>\n",
       "      <td>Bonde</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anannya</td>\n",
       "      <td>Chammat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abhishek</td>\n",
       "      <td>Chauhan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Aniruddha</td>\n",
       "      <td>Thakur</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Jay</td>\n",
       "      <td>Thakur</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Vivek</td>\n",
       "      <td>Thombre</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Adarsh</td>\n",
       "      <td>Vaidya</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>KARTIK</td>\n",
       "      <td>VATSAL</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FirstName   LastName  clusters\n",
       "0   Utkarsha   Baviskar          3\n",
       "1     Sanket    Bhosale          0\n",
       "2    Khushbu       Bonde         0\n",
       "3     Anannya    Chammat         0\n",
       "4    Abhishek    Chauhan         2\n",
       "..        ...        ...       ...\n",
       "71  Aniruddha     Thakur         0\n",
       "72        Jay     Thakur         2\n",
       "73      Vivek   Thombre          2\n",
       "74    Adarsh      Vaidya         2\n",
       "75    KARTIK      VATSAL         3\n",
       "\n",
       "[76 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c6b51108",
   "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>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abhishek</td>\n",
       "      <td>Chauhan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaurang</td>\n",
       "      <td>Deshpande</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ayush</td>\n",
       "      <td>Fulsundar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Ghule</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kunal</td>\n",
       "      <td>Gole</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Harsh</td>\n",
       "      <td>Gupta</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hardeek</td>\n",
       "      <td>Karkare</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Manoj</td>\n",
       "      <td>Kondhavale</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Shreyash</td>\n",
       "      <td>Kshirsagar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Anurag</td>\n",
       "      <td>Mishra</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Mohammad Danish</td>\n",
       "      <td>Shakil Ahmad</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Maneet</td>\n",
       "      <td>Monga</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Yashraj</td>\n",
       "      <td>Mundada</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Naima</td>\n",
       "      <td>Farhat</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Manisha</td>\n",
       "      <td>Pandey</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Harsh</td>\n",
       "      <td>Patel</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Chaitanya</td>\n",
       "      <td>Patil</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Amit</td>\n",
       "      <td>Pawar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Varad</td>\n",
       "      <td>Pawar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Ashray Kumar</td>\n",
       "      <td>Punase</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Mrunal</td>\n",
       "      <td>Raipure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Aditya</td>\n",
       "      <td>Rakate</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Rane</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Aditi</td>\n",
       "      <td>Rathod</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Anand</td>\n",
       "      <td>Sharma</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Pranali</td>\n",
       "      <td>Shendre</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Shubham</td>\n",
       "      <td>Shirsat</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Amarjot Singh</td>\n",
       "      <td>Phull</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Vivek</td>\n",
       "      <td>Singh</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Shubham</td>\n",
       "      <td>Sonawane</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Mahesh</td>\n",
       "      <td>Takale</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>SHRADDHA</td>\n",
       "      <td>TANDEL</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Mitesh</td>\n",
       "      <td>Teli</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Jay</td>\n",
       "      <td>Thakur</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Vivek</td>\n",
       "      <td>Thombre</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Adarsh</td>\n",
       "      <td>Vaidya</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           FirstName       LastName  clusters\n",
       "4           Abhishek        Chauhan         2\n",
       "7           Gaurang      Deshpande          2\n",
       "10             Ayush     Fulsundar          2\n",
       "12           Gaurav          Ghule          2\n",
       "13             Kunal           Gole         2\n",
       "15             Harsh          Gupta         2\n",
       "22           Hardeek        Karkare         2\n",
       "25            Manoj      Kondhavale         2\n",
       "26         Shreyash      Kshirsagar         2\n",
       "31            Anurag         Mishra         2\n",
       "32  Mohammad Danish   Shakil Ahmad          2\n",
       "33            Maneet          Monga         2\n",
       "35           Yashraj        Mundada         2\n",
       "36             Naima         Farhat         2\n",
       "40          Manisha          Pandey         2\n",
       "41             Harsh          Patel         2\n",
       "42         Chaitanya          Patil         2\n",
       "49              Amit          Pawar         2\n",
       "50            Varad          Pawar          2\n",
       "52      Ashray Kumar         Punase         2\n",
       "54            Mrunal        Raipure         2\n",
       "55            Aditya         Rakate         2\n",
       "56           Mahesh            Rane         2\n",
       "58             Aditi         Rathod         2\n",
       "62            Anand         Sharma          2\n",
       "63          Pranali        Shendre          2\n",
       "64           Shubham        Shirsat         2\n",
       "65     Amarjot Singh          Phull         2\n",
       "66             Vivek          Singh         2\n",
       "67          Shubham       Sonawane          2\n",
       "68           Mahesh          Takale         2\n",
       "69          SHRADDHA         TANDEL         2\n",
       "70            Mitesh           Teli         2\n",
       "72               Jay         Thakur         2\n",
       "73             Vivek       Thombre          2\n",
       "74           Adarsh          Vaidya         2"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls_data[ls_data['clusters'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f82291ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(name):\n",
    "    pred_cluster = ls_data[ls_data['FirstName']==f'{name}']['clusters']\n",
    "    pred_cluster = int(pred_cluster)\n",
    "#     print(pred_cluster)\n",
    "\n",
    "    if pred_cluster == 0 :\n",
    "        print(\"Most probable student for securing a job\")\n",
    "    elif pred_cluster == 2:\n",
    "        print(\"Not likely to be selected\")\n",
    "    else:\n",
    "        print(\"Most likely\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "447f0ea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most probable student for securing a job\n"
     ]
    }
   ],
   "source": [
    "prediction('Sanika')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "290640ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "17863225",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(ls_data,open('ls_data.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "968f1aa8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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