pma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af574479",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import sklearn as sk\n",
    "from pandas_profiling import ProfileReport\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d92b3c16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Gender Home Location Level of Education  Age(Years)  Number of Subjects  \\\n",
      "0       Male         Urban     Under Graduate          18                  11   \n",
      "1       Male         Urban     Under Graduate          19                   7   \n",
      "2       Male         Rural     Under Graduate          18                   5   \n",
      "3       Male         Urban     Under Graduate          18                   5   \n",
      "4       Male         Rural     Under Graduate          18                   5   \n",
      "...      ...           ...                ...         ...                 ...   \n",
      "1028  Female         Rural     Under Graduate          19                   3   \n",
      "1029  Female         Rural     Under Graduate          20                   6   \n",
      "1030  Female         Rural     Under Graduate          20                   3   \n",
      "1031  Female         Rural     Under Graduate          19                   6   \n",
      "1032  Female         Urban     Under Graduate          20                   6   \n",
      "\n",
      "     Device type used to attend classes Economic status  Family size  \\\n",
      "0                                Laptop    Middle Class            4   \n",
      "1                                Laptop    Middle Class            4   \n",
      "2                                Laptop    Middle Class            5   \n",
      "3                                Laptop    Middle Class            4   \n",
      "4                                Laptop    Middle Class            4   \n",
      "...                                 ...             ...          ...   \n",
      "1028                             Mobile    Middle Class            6   \n",
      "1029                            Desktop    Middle Class            3   \n",
      "1030                             Mobile    Middle Class            3   \n",
      "1031                             Mobile    Middle Class            2   \n",
      "1032                             Laptop    Middle Class            4   \n",
      "\n",
      "      Internet facility in your locality Are you involved in any sports?  ...  \\\n",
      "0                                      5                              No  ...   \n",
      "1                                      1                             Yes  ...   \n",
      "2                                      2                              No  ...   \n",
      "3                                      4                             Yes  ...   \n",
      "4                                      3                              No  ...   \n",
      "...                                  ...                             ...  ...   \n",
      "1028                                   5                             Yes  ...   \n",
      "1029                                   1                              No  ...   \n",
      "1030                                   2                              No  ...   \n",
      "1031                                   3                             Yes  ...   \n",
      "1032                                   3                              No  ...   \n",
      "\n",
      "     Time spent on social media (Hours)  Interested in Gaming?  \\\n",
      "0                                     1                     No   \n",
      "1                                     1                    Yes   \n",
      "2                                     1                     No   \n",
      "3                                     2                     No   \n",
      "4                                     2                    Yes   \n",
      "...                                 ...                    ...   \n",
      "1028                                  4                    Yes   \n",
      "1029                                  2                    Yes   \n",
      "1030                                  3                     No   \n",
      "1031                                  2                    Yes   \n",
      "1032                                  1                    Yes   \n",
      "\n",
      "      Have separate room for studying?  Engaged in group studies?  \\\n",
      "0                                   No                         No   \n",
      "1                                  Yes                         No   \n",
      "2                                  Yes                         No   \n",
      "3                                   No                        yes   \n",
      "4                                  Yes                        yes   \n",
      "...                                ...                        ...   \n",
      "1028                               Yes                        yes   \n",
      "1029                               Yes                         No   \n",
      "1030                               Yes                        yes   \n",
      "1031                                No                         No   \n",
      "1032                               Yes                        yes   \n",
      "\n",
      "     Average marks scored before pandemic in traditional classroom  \\\n",
      "0                                                91-100              \n",
      "1                                                91-100              \n",
      "2                                                 71-80              \n",
      "3                                                91-100              \n",
      "4                                                 81-90              \n",
      "...                                                 ...              \n",
      "1028                                             91-100              \n",
      "1029                                              71-80              \n",
      "1030                                              71-80              \n",
      "1031                                              61-70              \n",
      "1032                                              81-90              \n",
      "\n",
      "     Your interaction in online mode  \\\n",
      "0                                  1   \n",
      "1                                  1   \n",
      "2                                  1   \n",
      "3                                  1   \n",
      "4                                  3   \n",
      "...                              ...   \n",
      "1028                               4   \n",
      "1029                               5   \n",
      "1030                               2   \n",
      "1031                               3   \n",
      "1032                               5   \n",
      "\n",
      "     Clearing doubts with faculties in online mode Interested in?  \\\n",
      "0                                                1      Practical   \n",
      "1                                                1         Theory   \n",
      "2                                                1           Both   \n",
      "3                                                2         Theory   \n",
      "4                                                3           Both   \n",
      "...                                            ...            ...   \n",
      "1028                                             4         Theory   \n",
      "1029                                             5         Theory   \n",
      "1030                                             2         Theory   \n",
      "1031                                             3           Both   \n",
      "1032                                             4      Practical   \n",
      "\n",
      "      Performance in online  Your level of satisfaction in Online Education  \n",
      "0                         6                                         Average  \n",
      "1                         3                                             Bad  \n",
      "2                         6                                             Bad  \n",
      "3                         4                                             Bad  \n",
      "4                         6                                         Average  \n",
      "...                     ...                                             ...  \n",
      "1028                      8                                         Average  \n",
      "1029                     10                                         Average  \n",
      "1030                      6                                         Average  \n",
      "1031                      6                                            Good  \n",
      "1032                     10                                         Average  \n",
      "\n",
      "[1033 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv (r'C:\\Users\\pelgu\\OneDrive\\pma datascience\\data.csv')\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d166de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1263fb60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1033 entries, 0 to 1032\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype \n",
      "---  ------                                                         --------------  ----- \n",
      " 0   Gender                                                         1033 non-null   object\n",
      " 1   Home Location                                                  1033 non-null   object\n",
      " 2   Level of Education                                             1033 non-null   object\n",
      " 3   Age(Years)                                                     1033 non-null   int64 \n",
      " 4   Number of Subjects                                             1033 non-null   int64 \n",
      " 5   Device type used to attend classes                             1033 non-null   object\n",
      " 6   Economic status                                                1033 non-null   object\n",
      " 7   Family size                                                    1033 non-null   int64 \n",
      " 8   Internet facility in your locality                             1033 non-null   int64 \n",
      " 9   Are you involved in any sports?                                1033 non-null   object\n",
      " 10  Do elderly people monitor you?                                 1033 non-null   object\n",
      " 11  Study time (Hours)                                             1033 non-null   int64 \n",
      " 12  Sleep time (Hours)                                             1033 non-null   int64 \n",
      " 13  Time spent on social media (Hours)                             1033 non-null   int64 \n",
      " 14  Interested in Gaming?                                          1033 non-null   object\n",
      " 15  Have separate room for studying?                               1033 non-null   object\n",
      " 16  Engaged in group studies?                                      1033 non-null   object\n",
      " 17  Average marks scored before pandemic in traditional classroom  1033 non-null   object\n",
      " 18  Your interaction in online mode                                1033 non-null   int64 \n",
      " 19  Clearing doubts with faculties in online mode                  1033 non-null   int64 \n",
      " 20  Interested in?                                                 1033 non-null   object\n",
      " 21  Performance in online                                          1033 non-null   int64 \n",
      " 22  Your level of satisfaction in Online Education                 1033 non-null   object\n",
      "dtypes: int64(10), object(13)\n",
      "memory usage: 185.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c05cae2",
   "metadata": {},
   "source": [
    "https://medium.com/@chaimgluck1/working-with-pandas-fixing-messy-column-names-42a54a6659cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d6e4920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pelgu\\AppData\\Local\\Temp/ipykernel_16508/1690174868.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')\n"
     ]
    }
   ],
   "source": [
    "df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476d46d6",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/11346283/renaming-column-names-in-pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1cc2037",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'device_type_used_to_attend_classes': 'device_type', 'internet_facility_in_your_locality': 'internet_facility', 'are_you_involved_in_any_sports?':'does_sports','do_elderly_people_monitor_you?':'elderly_monitor','time_spent_on_social_media_hours':'socialmedia_hours','interested_in_gaming?':'gaming_interest','have_separate_room_for_studying?':'study_room','engaged_in_group_studies?':'group_study','average_marks_scored_before_pandemic_in_traditional_classroom':'prepandemic_grades','your_interaction_in_online_mode':'online_interaction','clearing_doubts_with_faculties_in_online_mode':'online_doubts','interested_in?':'interests','your_level_of_satisfaction_in_online_education':'satisfaction_level'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "878f57f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index=df[df['prepandemic_grades'] == '20-Nov'].index, inplace=True)\n",
    "df.drop(index=df[df['prepandemic_grades'] == '0-10'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9977e79c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>home_location</th>\n",
       "      <th>level_of_education</th>\n",
       "      <th>ageyears</th>\n",
       "      <th>number_of_subjects</th>\n",
       "      <th>device_type</th>\n",
       "      <th>economic_status</th>\n",
       "      <th>family_size</th>\n",
       "      <th>internet_facility</th>\n",
       "      <th>does_sports</th>\n",
       "      <th>elderly_monitor</th>\n",
       "      <th>study_time_hours</th>\n",
       "      <th>sleep_time_hours</th>\n",
       "      <th>socialmedia_hours</th>\n",
       "      <th>gaming_interest</th>\n",
       "      <th>study_room</th>\n",
       "      <th>group_study</th>\n",
       "      <th>prepandemic_grades</th>\n",
       "      <th>online_interaction</th>\n",
       "      <th>online_doubts</th>\n",
       "      <th>interests</th>\n",
       "      <th>performance_in_online</th>\n",
       "      <th>satisfaction_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
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       "      <td>11</td>\n",
       "      <td>Laptop</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>91-100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Practical</td>\n",
       "      <td>6</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Under Graduate</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91-100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Theory</td>\n",
       "      <td>3</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Under Graduate</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71-80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Both</td>\n",
       "      <td>6</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Under Graduate</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>91-100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Theory</td>\n",
       "      <td>4</td>\n",
       "      <td>Bad</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Under Graduate</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>81-90</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Both</td>\n",
       "      <td>6</td>\n",
       "      <td>Average</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Under Graduate</td>\n",
       "      <td>19</td>\n",
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       "      <td>Mobile</td>\n",
       "      <td>Middle Class</td>\n",
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       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>91-100</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Theory</td>\n",
       "      <td>8</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>0</td>\n",
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       "      <td>Under Graduate</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71-80</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Theory</td>\n",
       "      <td>10</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
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       "      <td>Under Graduate</td>\n",
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       "      <td>3</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71-80</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Theory</td>\n",
       "      <td>6</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Under Graduate</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61-70</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Both</td>\n",
       "      <td>6</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Under Graduate</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>81-90</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Practical</td>\n",
       "      <td>10</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  home_location level_of_education  ageyears  number_of_subjects  \\\n",
       "0          1              1     Under Graduate        18                  11   \n",
       "1          1              1     Under Graduate        19                   7   \n",
       "2          1              0     Under Graduate        18                   5   \n",
       "3          1              1     Under Graduate        18                   5   \n",
       "4          1              0     Under Graduate        18                   5   \n",
       "...      ...            ...                ...       ...                 ...   \n",
       "1028       0              0     Under Graduate        19                   3   \n",
       "1029       0              0     Under Graduate        20                   6   \n",
       "1030       0              0     Under Graduate        20                   3   \n",
       "1031       0              0     Under Graduate        19                   6   \n",
       "1032       0              1     Under Graduate        20                   6   \n",
       "\n",
       "     device_type economic_status  family_size  internet_facility  does_sports  \\\n",
       "0         Laptop    Middle Class            4                  5            0   \n",
       "1         Laptop    Middle Class            4                  1            1   \n",
       "2         Laptop    Middle Class            5                  2            0   \n",
       "3         Laptop    Middle Class            4                  4            1   \n",
       "4         Laptop    Middle Class            4                  3            0   \n",
       "...          ...             ...          ...                ...          ...   \n",
       "1028      Mobile    Middle Class            6                  5            1   \n",
       "1029     Desktop    Middle Class            3                  1            0   \n",
       "1030      Mobile    Middle Class            3                  2            0   \n",
       "1031      Mobile    Middle Class            2                  3            1   \n",
       "1032      Laptop    Middle Class            4                  3            0   \n",
       "\n",
       "      elderly_monitor  study_time_hours  sleep_time_hours  socialmedia_hours  \\\n",
       "0                   1                 3                 6                  1   \n",
       "1                   1                 7                 5                  1   \n",
       "2                   1                 6                 7                  1   \n",
       "3                   1                 3                 6                  2   \n",
       "4                   0                 8                 7                  2   \n",
       "...               ...               ...               ...                ...   \n",
       "1028                1                 4                 8                  4   \n",
       "1029                0                 3                 7                  2   \n",
       "1030                0                 3                 6                  3   \n",
       "1031                0                 4                 6                  2   \n",
       "1032                0                 5                 7                  1   \n",
       "\n",
       "      gaming_interest  study_room  group_study prepandemic_grades  \\\n",
       "0                   0           0            0             91-100   \n",
       "1                   1           1            0             91-100   \n",
       "2                   0           1            0              71-80   \n",
       "3                   0           0            1             91-100   \n",
       "4                   1           1            1              81-90   \n",
       "...               ...         ...          ...                ...   \n",
       "1028                1           1            1             91-100   \n",
       "1029                1           1            0              71-80   \n",
       "1030                0           1            1              71-80   \n",
       "1031                1           0            0              61-70   \n",
       "1032                1           1            1              81-90   \n",
       "\n",
       "      online_interaction  online_doubts  interests  performance_in_online  \\\n",
       "0                      1              1  Practical                      6   \n",
       "1                      1              1     Theory                      3   \n",
       "2                      1              1       Both                      6   \n",
       "3                      1              2     Theory                      4   \n",
       "4                      3              3       Both                      6   \n",
       "...                  ...            ...        ...                    ...   \n",
       "1028                   4              4     Theory                      8   \n",
       "1029                   5              5     Theory                     10   \n",
       "1030                   2              2     Theory                      6   \n",
       "1031                   3              3       Both                      6   \n",
       "1032                   5              4  Practical                     10   \n",
       "\n",
       "     satisfaction_level  \n",
       "0               Average  \n",
       "1                   Bad  \n",
       "2                   Bad  \n",
       "3                   Bad  \n",
       "4               Average  \n",
       "...                 ...  \n",
       "1028            Average  \n",
       "1029            Average  \n",
       "1030            Average  \n",
       "1031               Good  \n",
       "1032            Average  \n",
       "\n",
       "[1026 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting Atrition 'Yes' to 1 and 'No' to 0\n",
    "df['gender'] = np.where(df['gender'] == 'Male', 1, 0)\n",
    "df.astype({\"gender\": int})\n",
    "df['home_location'] = np.where(df['home_location'] == 'Urban', 1, 0)\n",
    "df.astype({\"home_location\": int})\n",
    "df['does_sports'] = np.where(df['does_sports'] == 'Yes', 1, 0)\n",
    "df.astype({\"does_sports\": int})\n",
    "df['elderly_monitor'] = np.where(df['elderly_monitor'] == 'Yes', 1, 0)\n",
    "df.astype({\"elderly_monitor\": int})\n",
    "df['gaming_interest'] = np.where(df['gaming_interest'] == 'Yes', 1, 0)\n",
    "df.astype({\"gaming_interest\": int})\n",
    "df['study_room'] = np.where(df['study_room'] == 'Yes', 1, 0)\n",
    "df.astype({\"study_room\": int})\n",
    "df['group_study'] = np.where(df['group_study'] == 'yes', 1, 0)\n",
    "df.astype({\"group_study\": int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ebffed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['prepandemic_grades'] = df['prepandemic_grades'].replace(['91-100', '81-90', '71-80', '61-70', '51-60', '41-50', '31-40', '21-30', '11-20', '1-10'], [10,9, 8, 7,6,5,4,3,2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e050631",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['satisfaction_level'] = df['satisfaction_level'].replace(['Bad', 'Average', 'Good'], [1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "be18e7e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 20))\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "            square=True, ax=ax, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5072facf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee2d5ed00>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Marital Status vs Attrition chart\n",
    "sns.catplot(x = 'economic_status', y='satisfaction_level', kind = 'bar', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a74146c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee38f5250>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Marital Status vs Attrition chart\n",
    "sns.catplot(x = 'prepandemic_grades', y='satisfaction_level', kind = 'bar', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18a4333c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee2d925e0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Marital Status vs Attrition chart\n",
    "sns.catplot(x = 'performance_in_online', y='satisfaction_level', kind = 'bar', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d946f9df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee2e9ef40>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Marital Status vs Attrition chart\n",
    "sns.catplot(x = 'online_interaction', y='satisfaction_level', kind = 'bar', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79dc8e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='device_type', ylabel='Count'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "sns.histplot(binwidth=0.5, x=\"device_type\", hue=\"satisfaction_level\", data=df, stat=\"count\", multiple=\"stack\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "11e11c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee2f634f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Marital Status vs Attrition chart\n",
    "sns.catplot(x = 'level_of_education', y='satisfaction_level', kind = 'bar', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3a07499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee2fbde20>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Age vs Attrition chart\n",
    "sns.catplot(x = 'ageyears', y='satisfaction_level', kind = 'bar', data=df, aspect = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af1611d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='satisfaction_level', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "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": [
    "# Plot attrition frequency\n",
    "sns.countplot(x='satisfaction_level', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a219fb",
   "metadata": {},
   "source": [
    "for column in df:\n",
    "        sns_plot = sns.displot(df[column])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b2775e44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Dummy Variables for different Departments\n",
    "df = pd.get_dummies(df, columns=[\"level_of_education\"], prefix=[\"education\"])\n",
    "df = pd.get_dummies(df, columns=[\"device_type\"], prefix=[\"device\"])\n",
    "df = pd.get_dummies(df, columns=[\"economic_status\"], prefix=[\"status\"])\n",
    "df = pd.get_dummies(df, columns=[\"interests\"], prefix=[\"interests\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "37a65d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate target values\n",
    "y = df['satisfaction_level']\n",
    "# Separate variables and remove ID column\n",
    "X = df.drop(['satisfaction_level'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "07562427",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dea075f1",
   "metadata": {},
   "source": [
    "profile = ProfileReport(df, title=\"Pandas Profiling IMA\")\n",
    "\n",
    "profile.to_file(\"eda_PMA.html\")\n",
    "\n",
    "profile.to_file(\"eda_PMA.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3a93e1",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/18172851/deleting-dataframe-row-in-pandas-based-on-column-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2fd74f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split train and test date\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ee6eae25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(820, 30)\n",
      "(206, 30)\n",
      "(820,)\n",
      "(206,)\n"
     ]
    }
   ],
   "source": [
    "#Print split datasets\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1aee8832",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import scaler to normalize data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9d4d02c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert scaled data back to dataframe\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled,columns = list(X))\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled,columns = list(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "409a3fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance (ElasticNet regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.6366732364128057\n",
      "R2 score is 0.16216826946180385\n",
      "\n",
      "\n",
      "Testing performance (ElasticNet regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.6097999652944661\n",
      "R2 score is 0.1472070299796241\n",
      "\n",
      "\n",
      "Beta weights/co-efficients (ElasticNet regularisation)\n",
      "-----------------------------------------\n",
      "gender: -0.0\n",
      "home_location: -0.0\n",
      "ageyears: 0.0\n",
      "number_of_subjects: -0.0\n",
      "family_size: -0.0\n",
      "internet_facility: 0.0\n",
      "does_sports: -0.0\n",
      "elderly_monitor: 0.0\n",
      "study_time_hours: 0.0\n",
      "sleep_time_hours: 0.0\n",
      "socialmedia_hours: -0.0\n",
      "gaming_interest: -0.0\n",
      "study_room: 0.0\n",
      "group_study: 0.0\n",
      "prepandemic_grades: -0.0\n",
      "online_interaction: 0.0539\n",
      "online_doubts: 0.0486\n",
      "performance_in_online: 0.0239\n",
      "satisfaction_level: 0.0\n",
      "education_Post Graduate: -0.0\n",
      "education_School: -0.0\n",
      "education_Under Graduate: 0.0\n",
      "device_Desktop: -0.0\n",
      "device_Laptop: 0.0\n",
      "device_Mobile: -0.0\n",
      "status_Middle Class: 0.0\n",
      "status_Poor: -0.0\n",
      "status_Rich: -0.0\n",
      "interests_Both: -0.0\n",
      "interests_Practical: 0.0\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 30 is out of bounds for axis 0 with size 30",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16508/143850866.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Beta weights/co-efficients (ElasticNet regularisation)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"-----------------------------------------\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\": \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbetas\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\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[0;32m     38\u001b[0m     \u001b[0mcounter\u001b[0m \u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 30 is out of bounds for axis 0 with size 30"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "elastic_model = ElasticNet(alpha = 0.5, l1_ratio=0.5)\n",
    "\n",
    "elastic_model_fit = elastic_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "boston_predict = elastic_model_fit.predict(X_train_scaled)\n",
    "\n",
    "rmse = (np.sqrt(mean_squared_error(y_train, boston_predict)))\n",
    "r2 = r2_score(y_train, boston_predict)\n",
    "\n",
    "print(\"Training performance (ElasticNet regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "boston_predict_test = elastic_model_fit.predict(X_test_scaled)\n",
    "\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, boston_predict_test)))\n",
    "r2 = r2_score(y_test, boston_predict_test)\n",
    "\n",
    "print(\"Testing performance (ElasticNet regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "betas = elastic_model_fit.coef_\n",
    "\n",
    "counter = 0\n",
    "for col in df.columns:\n",
    "    if counter == 0:\n",
    "        print(\"Beta weights/co-efficients (ElasticNet regularisation)\")\n",
    "        print(\"-----------------------------------------\")\n",
    "    print(col + \": \" + str(round(betas[counter], 4)))\n",
    "    counter +=1\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd651418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance (L2 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.5379722666785183\n",
      "R2 score is 0.40180415599490993\n",
      "\n",
      "\n",
      "Testing performance (L2 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.537085499826187\n",
      "R2 score is 0.33846065948623205\n",
      "\n",
      "\n",
      "Beta weights/co-efficients (L2 regularisation)\n",
      "-----------------------------------------\n",
      "gender: -0.0165\n",
      "home_location: -0.0053\n",
      "ageyears: 0.0296\n",
      "number_of_subjects: 0.0131\n",
      "family_size: -0.0036\n",
      "internet_facility: 0.0141\n",
      "does_sports: -0.0209\n",
      "elderly_monitor: -0.008\n",
      "study_time_hours: 0.0012\n",
      "sleep_time_hours: -0.0064\n",
      "socialmedia_hours: -0.0174\n",
      "gaming_interest: 0.0015\n",
      "study_room: 0.0038\n",
      "group_study: -0.0341\n",
      "prepandemic_grades: -0.1096\n",
      "online_interaction: 0.1533\n",
      "online_doubts: 0.1368\n",
      "performance_in_online: 0.1633\n",
      "satisfaction_level: -0.0186\n",
      "education_Post Graduate: 0.0219\n",
      "education_School: -0.0002\n",
      "education_Under Graduate: 0.012\n",
      "device_Desktop: -0.0262\n",
      "device_Laptop: 0.0225\n",
      "device_Mobile: 0.004\n",
      "status_Middle Class: 0.0064\n",
      "status_Poor: -0.0139\n",
      "status_Rich: -0.0101\n",
      "interests_Both: -0.0276\n",
      "interests_Practical: 0.0434\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 30 is out of bounds for axis 0 with size 30",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16508/1949795069.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     33\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Beta weights/co-efficients (L2 regularisation)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"-----------------------------------------\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\": \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbetas\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\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[0;32m     36\u001b[0m     \u001b[0mcounter\u001b[0m \u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 30 is out of bounds for axis 0 with size 30"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge_model = Ridge(alpha = 0.5)\n",
    "\n",
    "ridge_model_fit = ridge_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "boston_predict2 = ridge_model_fit.predict(X_train_scaled)\n",
    "\n",
    "rmse = (np.sqrt(mean_squared_error(y_train, boston_predict2)))\n",
    "r2 = r2_score(y_train, boston_predict2)\n",
    "\n",
    "print(\"Training performance (L2 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "boston_predict_test2 = ridge_model_fit.predict(X_test_scaled)\n",
    "\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, boston_predict_test2)))\n",
    "r2 = r2_score(y_test, boston_predict_test2)\n",
    "\n",
    "print(\"Testing performance (L2 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "betas = ridge_model_fit.coef_\n",
    "\n",
    "counter = 0\n",
    "for col in df.columns:\n",
    "    if counter == 0:\n",
    "        print(\"Beta weights/co-efficients (L2 regularisation)\")\n",
    "        print(\"-----------------------------------------\")\n",
    "    print(col + \": \" + str(round(betas[counter], 4)))\n",
    "    counter +=1\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08ed2902",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance (L1 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.6955657547743083\n",
      "R2 score is 0.0\n",
      "\n",
      "\n",
      "Testing performance (L1 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.6620373247124063\n",
      "R2 score is -0.005156698116046021\n",
      "\n",
      "\n",
      "Beta weights/co-efficients (L1 regularisation)\n",
      "-----------------------------------------\n",
      "gender: -0.0\n",
      "home_location: -0.0\n",
      "ageyears: 0.0\n",
      "number_of_subjects: -0.0\n",
      "family_size: -0.0\n",
      "internet_facility: 0.0\n",
      "does_sports: -0.0\n",
      "elderly_monitor: 0.0\n",
      "study_time_hours: 0.0\n",
      "sleep_time_hours: 0.0\n",
      "socialmedia_hours: -0.0\n",
      "gaming_interest: -0.0\n",
      "study_room: 0.0\n",
      "group_study: 0.0\n",
      "prepandemic_grades: -0.0\n",
      "online_interaction: 0.0\n",
      "online_doubts: 0.0\n",
      "performance_in_online: 0.0\n",
      "satisfaction_level: -0.0\n",
      "education_Post Graduate: -0.0\n",
      "education_School: 0.0\n",
      "education_Under Graduate: -0.0\n",
      "device_Desktop: 0.0\n",
      "device_Laptop: -0.0\n",
      "device_Mobile: 0.0\n",
      "status_Middle Class: -0.0\n",
      "status_Poor: -0.0\n",
      "status_Rich: -0.0\n",
      "interests_Both: 0.0\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 29 is out of bounds for axis 0 with size 29",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13584/1833779232.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Beta weights/co-efficients (L1 regularisation)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"-----------------------------------------\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\": \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbetas\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\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[0;32m     47\u001b[0m     \u001b[0mcounter\u001b[0m \u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 29 is out of bounds for axis 0 with size 29"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "# fit a L1 model - alpha is a value between 0 and inf where higher\n",
    "# means more regularisation. Typically we use max = 1\n",
    "lasso_model = Lasso(alpha = 1)\n",
    "\n",
    "# fit the model to the training data\n",
    "lasso_model_fit = lasso_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# predict the training data\n",
    "boston_predict_lasso = lasso_model_fit.predict(X_train_scaled)\n",
    "\n",
    "# calculate RMSE (root mean square error) and R^2 (predictive power)\n",
    "# training set\n",
    "rmse = (np.sqrt(mean_squared_error(y_train, boston_predict_lasso)))\n",
    "r2 = r2_score(y_train, boston_predict_lasso)\n",
    "\n",
    "# print the performance metrics\n",
    "print(\"Training performance (L1 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "# predict the testing data\n",
    "boston_predict_lasso_test = lasso_model_fit.predict(X_test_scaled)\n",
    "\n",
    "# calculate RMSE (root mean square error) and R^2 (predictive power)\n",
    "# testing set\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, boston_predict_lasso_test)))\n",
    "r2 = r2_score(y_test, boston_predict_lasso_test)\n",
    "\n",
    "# print the performance metrics\n",
    "print(\"Testing performance (L1 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "# print the coefficients\n",
    "betas = lasso_model_fit.coef_\n",
    "counter = 0\n",
    "for col in df.columns:\n",
    "    if counter == 0:\n",
    "        print(\"Beta weights/co-efficients (L1 regularisation)\")\n",
    "        print(\"-----------------------------------------\")\n",
    "    print(col + \": \" + str(round(betas[counter], 4)))\n",
    "    counter +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b66e9738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance (L2 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.5494156426462922\n",
      "R2 score is 0.3760846701391073\n",
      "\n",
      "\n",
      "Testing performance (L2 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.5612171972244956\n",
      "R2 score is 0.27767813385999984\n",
      "\n",
      "\n",
      "Beta weights/co-efficients (L2 regularisation)\n",
      "-----------------------------------------\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "type numpy.ndarray doesn't define __round__ method",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13584/2183464113.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     33\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Beta weights/co-efficients (L2 regularisation)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"-----------------------------------------\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\": \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbetas\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\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[0;32m     36\u001b[0m     \u001b[0mcounter\u001b[0m \u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: type numpy.ndarray doesn't define __round__ method"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr_model = SVR(kernel='linear',C=1)\n",
    "\n",
    "svr_model_fit = svr_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "boston_predict3 = svr_model_fit.predict(X_train_scaled)\n",
    "\n",
    "rmse = (np.sqrt(mean_squared_error(y_train, boston_predict3)))\n",
    "r2 = r2_score(y_train, boston_predict3)\n",
    "\n",
    "print(\"Training performance (L2 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "boston_predict_test3 = svr_model_fit.predict(X_test_scaled)\n",
    "\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, boston_predict_test3)))\n",
    "r2 = r2_score(y_test, boston_predict_test3)\n",
    "\n",
    "print(\"Testing performance (L2 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "betas = svr_model_fit.coef_\n",
    "\n",
    "counter = 0\n",
    "for col in df.columns:\n",
    "    if counter == 0:\n",
    "        print(\"Beta weights/co-efficients (L2 regularisation)\")\n",
    "        print(\"-----------------------------------------\")\n",
    "    print(col + \": \" + str(round(betas[counter], 4)))\n",
    "    counter +=1\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6f08a4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ageyears</th>\n",
       "      <th>number_of_subjects</th>\n",
       "      <th>family_size</th>\n",
       "      <th>internet_facility</th>\n",
       "      <th>study_time_hours</th>\n",
       "      <th>sleep_time_hours</th>\n",
       "      <th>socialmedia_hours</th>\n",
       "      <th>prepandemic_grades</th>\n",
       "      <th>online_interaction</th>\n",
       "      <th>online_doubts</th>\n",
       "      <th>performance_in_online</th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>gender__Female</th>\n",
       "      <th>gender__Male</th>\n",
       "      <th>home_location__Rural</th>\n",
       "      <th>...</th>\n",
       "      <th>status_Poor</th>\n",
       "      <th>status_Rich</th>\n",
       "      <th>does_sports_No</th>\n",
       "      <th>does_sports_Yes</th>\n",
       "      <th>elderly_monitored_No</th>\n",
       "      <th>elderly_monitored_Yes</th>\n",
       "      <th>gaming_interest_No</th>\n",
       "      <th>gaming_interest_Yes</th>\n",
       "      <th>study_room_No</th>\n",
       "      <th>study_room_Yes</th>\n",
       "      <th>group_study_No</th>\n",
       "      <th>group_study_yes</th>\n",
       "      <th>interests_Both</th>\n",
       "      <th>interests_Practical</th>\n",
       "      <th>interests_Theory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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>1028</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ageyears  number_of_subjects  family_size  internet_facility  \\\n",
       "0           18                  11            4                  5   \n",
       "1           19                   7            4                  1   \n",
       "2           18                   5            5                  2   \n",
       "3           18                   5            4                  4   \n",
       "4           18                   5            4                  3   \n",
       "...        ...                 ...          ...                ...   \n",
       "1028        19                   3            6                  5   \n",
       "1029        20                   6            3                  1   \n",
       "1030        20                   3            3                  2   \n",
       "1031        19                   6            2                  3   \n",
       "1032        20                   6            4                  3   \n",
       "\n",
       "      study_time_hours  sleep_time_hours  socialmedia_hours  \\\n",
       "0                    3                 6                  1   \n",
       "1                    7                 5                  1   \n",
       "2                    6                 7                  1   \n",
       "3                    3                 6                  2   \n",
       "4                    8                 7                  2   \n",
       "...                ...               ...                ...   \n",
       "1028                 4                 8                  4   \n",
       "1029                 3                 7                  2   \n",
       "1030                 3                 6                  3   \n",
       "1031                 4                 6                  2   \n",
       "1032                 5                 7                  1   \n",
       "\n",
       "      prepandemic_grades  online_interaction  online_doubts  \\\n",
       "0                     10                   1              1   \n",
       "1                     10                   1              1   \n",
       "2                      8                   1              1   \n",
       "3                     10                   1              2   \n",
       "4                      9                   3              3   \n",
       "...                  ...                 ...            ...   \n",
       "1028                  10                   4              4   \n",
       "1029                   8                   5              5   \n",
       "1030                   8                   2              2   \n",
       "1031                   7                   3              3   \n",
       "1032                   9                   5              4   \n",
       "\n",
       "      performance_in_online  satisfaction_level  gender__Female  gender__Male  \\\n",
       "0                         6                   2               0             1   \n",
       "1                         3                   1               0             1   \n",
       "2                         6                   1               0             1   \n",
       "3                         4                   1               0             1   \n",
       "4                         6                   2               0             1   \n",
       "...                     ...                 ...             ...           ...   \n",
       "1028                      8                   2               1             0   \n",
       "1029                     10                   2               1             0   \n",
       "1030                      6                   2               1             0   \n",
       "1031                      6                   3               1             0   \n",
       "1032                     10                   2               1             0   \n",
       "\n",
       "      home_location__Rural  ...  status_Poor  status_Rich  does_sports_No  \\\n",
       "0                        0  ...            0            0               1   \n",
       "1                        0  ...            0            0               0   \n",
       "2                        1  ...            0            0               1   \n",
       "3                        0  ...            0            0               0   \n",
       "4                        1  ...            0            0               1   \n",
       "...                    ...  ...          ...          ...             ...   \n",
       "1028                     1  ...            0            0               0   \n",
       "1029                     1  ...            0            0               1   \n",
       "1030                     1  ...            0            0               1   \n",
       "1031                     1  ...            0            0               0   \n",
       "1032                     0  ...            0            0               1   \n",
       "\n",
       "      does_sports_Yes  elderly_monitored_No  elderly_monitored_Yes  \\\n",
       "0                   0                     0                      1   \n",
       "1                   1                     0                      1   \n",
       "2                   0                     0                      1   \n",
       "3                   1                     0                      1   \n",
       "4                   0                     1                      0   \n",
       "...               ...                   ...                    ...   \n",
       "1028                1                     0                      1   \n",
       "1029                0                     1                      0   \n",
       "1030                0                     1                      0   \n",
       "1031                1                     1                      0   \n",
       "1032                0                     1                      0   \n",
       "\n",
       "      gaming_interest_No  gaming_interest_Yes  study_room_No  study_room_Yes  \\\n",
       "0                      1                    0              1               0   \n",
       "1                      0                    1              0               1   \n",
       "2                      1                    0              0               1   \n",
       "3                      1                    0              1               0   \n",
       "4                      0                    1              0               1   \n",
       "...                  ...                  ...            ...             ...   \n",
       "1028                   0                    1              0               1   \n",
       "1029                   0                    1              0               1   \n",
       "1030                   1                    0              0               1   \n",
       "1031                   0                    1              1               0   \n",
       "1032                   0                    1              0               1   \n",
       "\n",
       "      group_study_No  group_study_yes  interests_Both  interests_Practical  \\\n",
       "0                  1                0               0                    1   \n",
       "1                  1                0               0                    0   \n",
       "2                  1                0               1                    0   \n",
       "3                  0                1               0                    0   \n",
       "4                  0                1               1                    0   \n",
       "...              ...              ...             ...                  ...   \n",
       "1028               0                1               0                    0   \n",
       "1029               1                0               0                    0   \n",
       "1030               0                1               0                    0   \n",
       "1031               1                0               1                    0   \n",
       "1032               0                1               0                    1   \n",
       "\n",
       "      interests_Theory  \n",
       "0                    0  \n",
       "1                    1  \n",
       "2                    0  \n",
       "3                    1  \n",
       "4                    0  \n",
       "...                ...  \n",
       "1028                 1  \n",
       "1029                 1  \n",
       "1030                 1  \n",
       "1031                 0  \n",
       "1032                 0  \n",
       "\n",
       "[1026 rows x 38 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "afd43d71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2302108216704614\n",
      "<bound method RegressorMixin.score of Ridge(alpha=1)>\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import svm\n",
    "svc = Ridge()\n",
    "params = {'alpha':[0.5,0.6,0.7,0.8,0.9,1],'fit_intercept':(True,False)}\n",
    "grid = GridSearchCV(svc,params)\n",
    "grid.fit(X_test_scaled, y_test)\n",
    "print(grid.best_score_)\n",
    "print(grid.best_estimator_.score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "92e32dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>param_fit_intercept</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003393</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "      <td>{'alpha': 0.5, 'fit_intercept': True}</td>\n",
       "      <td>0.236631</td>\n",
       "      <td>0.140770</td>\n",
       "      <td>0.137506</td>\n",
       "      <td>0.364738</td>\n",
       "      <td>0.269929</td>\n",
       "      <td>0.229915</td>\n",
       "      <td>0.085217</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002792</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.001597</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>{'alpha': 0.5, 'fit_intercept': False}</td>\n",
       "      <td>-323.512861</td>\n",
       "      <td>-8.588615</td>\n",
       "      <td>-9.483151</td>\n",
       "      <td>-9.229502</td>\n",
       "      <td>-10.117627</td>\n",
       "      <td>-72.186351</td>\n",
       "      <td>125.664211</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003189</td>\n",
       "      <td>0.000745</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.6</td>\n",
       "      <td>True</td>\n",
       "      <td>{'alpha': 0.6, 'fit_intercept': True}</td>\n",
       "      <td>0.236683</td>\n",
       "      <td>0.140930</td>\n",
       "      <td>0.137748</td>\n",
       "      <td>0.364506</td>\n",
       "      <td>0.270012</td>\n",
       "      <td>0.229976</td>\n",
       "      <td>0.085066</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003192</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>0.001849</td>\n",
       "      <td>0.6</td>\n",
       "      <td>False</td>\n",
       "      <td>{'alpha': 0.6, 'fit_intercept': False}</td>\n",
       "      <td>-311.969254</td>\n",
       "      <td>-8.590889</td>\n",
       "      <td>-9.484649</td>\n",
       "      <td>-9.227706</td>\n",
       "      <td>-10.114686</td>\n",
       "      <td>-69.877437</td>\n",
       "      <td>121.046895</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003390</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.003591</td>\n",
       "      <td>0.001353</td>\n",
       "      <td>0.7</td>\n",
       "      <td>True</td>\n",
       "      <td>{'alpha': 0.7, 'fit_intercept': True}</td>\n",
       "      <td>0.236734</td>\n",
       "      <td>0.141088</td>\n",
       "      <td>0.137989</td>\n",
       "      <td>0.364275</td>\n",
       "      <td>0.270094</td>\n",
       "      <td>0.230036</td>\n",
       "      <td>0.084916</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.004787</td>\n",
       "      <td>0.002035</td>\n",
       "      <td>0.003192</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>0.7</td>\n",
       "      <td>False</td>\n",
       "      <td>{'alpha': 0.7, 'fit_intercept': False}</td>\n",
       "      <td>-301.048893</td>\n",
       "      <td>-8.593158</td>\n",
       "      <td>-9.486142</td>\n",
       "      <td>-9.225919</td>\n",
       "      <td>-10.111772</td>\n",
       "      <td>-67.693177</td>\n",
       "      <td>116.678875</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.002193</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.8</td>\n",
       "      <td>True</td>\n",
       "      <td>{'alpha': 0.8, 'fit_intercept': True}</td>\n",
       "      <td>0.236785</td>\n",
       "      <td>0.141246</td>\n",
       "      <td>0.138229</td>\n",
       "      <td>0.364043</td>\n",
       "      <td>0.270174</td>\n",
       "      <td>0.230095</td>\n",
       "      <td>0.084767</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>{'alpha': 0.8, 'fit_intercept': False}</td>\n",
       "      <td>-290.707670</td>\n",
       "      <td>-8.595421</td>\n",
       "      <td>-9.487632</td>\n",
       "      <td>-9.224138</td>\n",
       "      <td>-10.108884</td>\n",
       "      <td>-65.624749</td>\n",
       "      <td>112.542509</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.002393</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.001596</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.9</td>\n",
       "      <td>True</td>\n",
       "      <td>{'alpha': 0.9, 'fit_intercept': True}</td>\n",
       "      <td>0.236835</td>\n",
       "      <td>0.141402</td>\n",
       "      <td>0.138466</td>\n",
       "      <td>0.363812</td>\n",
       "      <td>0.270251</td>\n",
       "      <td>0.230153</td>\n",
       "      <td>0.084617</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.001595</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.9</td>\n",
       "      <td>False</td>\n",
       "      <td>{'alpha': 0.9, 'fit_intercept': False}</td>\n",
       "      <td>-280.905313</td>\n",
       "      <td>-8.597678</td>\n",
       "      <td>-9.489118</td>\n",
       "      <td>-9.222364</td>\n",
       "      <td>-10.106022</td>\n",
       "      <td>-63.664099</td>\n",
       "      <td>108.621687</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.001596</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>{'alpha': 1, 'fit_intercept': True}</td>\n",
       "      <td>0.236885</td>\n",
       "      <td>0.141557</td>\n",
       "      <td>0.138703</td>\n",
       "      <td>0.363582</td>\n",
       "      <td>0.270328</td>\n",
       "      <td>0.230211</td>\n",
       "      <td>0.084469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.001796</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.001595</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>{'alpha': 1, 'fit_intercept': False}</td>\n",
       "      <td>-271.604993</td>\n",
       "      <td>-8.599930</td>\n",
       "      <td>-9.490600</td>\n",
       "      <td>-9.220598</td>\n",
       "      <td>-10.103186</td>\n",
       "      <td>-61.803861</td>\n",
       "      <td>104.901678</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0        0.003393      0.001005         0.002206        0.000757         0.5   \n",
       "1        0.002792      0.000747         0.001597        0.000489         0.5   \n",
       "2        0.003189      0.000745         0.001995        0.000631         0.6   \n",
       "3        0.003192      0.001163         0.003392        0.001849         0.6   \n",
       "4        0.003390      0.001197         0.003591        0.001353         0.7   \n",
       "5        0.004787      0.002035         0.003192        0.001466         0.7   \n",
       "6        0.002193      0.000746         0.001995        0.001092         0.8   \n",
       "7        0.001995      0.000002         0.001397        0.000489         0.8   \n",
       "8        0.002393      0.000488         0.001596        0.000488         0.9   \n",
       "9        0.001795      0.000399         0.001595        0.000488         0.9   \n",
       "10       0.001795      0.000399         0.001596        0.000489           1   \n",
       "11       0.001796      0.000399         0.001595        0.000488           1   \n",
       "\n",
       "   param_fit_intercept                                  params  \\\n",
       "0                 True   {'alpha': 0.5, 'fit_intercept': True}   \n",
       "1                False  {'alpha': 0.5, 'fit_intercept': False}   \n",
       "2                 True   {'alpha': 0.6, 'fit_intercept': True}   \n",
       "3                False  {'alpha': 0.6, 'fit_intercept': False}   \n",
       "4                 True   {'alpha': 0.7, 'fit_intercept': True}   \n",
       "5                False  {'alpha': 0.7, 'fit_intercept': False}   \n",
       "6                 True   {'alpha': 0.8, 'fit_intercept': True}   \n",
       "7                False  {'alpha': 0.8, 'fit_intercept': False}   \n",
       "8                 True   {'alpha': 0.9, 'fit_intercept': True}   \n",
       "9                False  {'alpha': 0.9, 'fit_intercept': False}   \n",
       "10                True     {'alpha': 1, 'fit_intercept': True}   \n",
       "11               False    {'alpha': 1, 'fit_intercept': False}   \n",
       "\n",
       "    split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0            0.236631           0.140770           0.137506   \n",
       "1         -323.512861          -8.588615          -9.483151   \n",
       "2            0.236683           0.140930           0.137748   \n",
       "3         -311.969254          -8.590889          -9.484649   \n",
       "4            0.236734           0.141088           0.137989   \n",
       "5         -301.048893          -8.593158          -9.486142   \n",
       "6            0.236785           0.141246           0.138229   \n",
       "7         -290.707670          -8.595421          -9.487632   \n",
       "8            0.236835           0.141402           0.138466   \n",
       "9         -280.905313          -8.597678          -9.489118   \n",
       "10           0.236885           0.141557           0.138703   \n",
       "11        -271.604993          -8.599930          -9.490600   \n",
       "\n",
       "    split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
       "0            0.364738           0.269929         0.229915        0.085217   \n",
       "1           -9.229502         -10.117627       -72.186351      125.664211   \n",
       "2            0.364506           0.270012         0.229976        0.085066   \n",
       "3           -9.227706         -10.114686       -69.877437      121.046895   \n",
       "4            0.364275           0.270094         0.230036        0.084916   \n",
       "5           -9.225919         -10.111772       -67.693177      116.678875   \n",
       "6            0.364043           0.270174         0.230095        0.084767   \n",
       "7           -9.224138         -10.108884       -65.624749      112.542509   \n",
       "8            0.363812           0.270251         0.230153        0.084617   \n",
       "9           -9.222364         -10.106022       -63.664099      108.621687   \n",
       "10           0.363582           0.270328         0.230211        0.084469   \n",
       "11          -9.220598         -10.103186       -61.803861      104.901678   \n",
       "\n",
       "    rank_test_score  \n",
       "0                 6  \n",
       "1                12  \n",
       "2                 5  \n",
       "3                11  \n",
       "4                 4  \n",
       "5                10  \n",
       "6                 3  \n",
       "7                 9  \n",
       "8                 2  \n",
       "9                 8  \n",
       "10                1  \n",
       "11                7  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvresults= pd.DataFrame(grid.cv_results_)\n",
    "cvresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1bfe7670",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ageyears</th>\n",
       "      <th>number_of_subjects</th>\n",
       "      <th>family_size</th>\n",
       "      <th>internet_facility</th>\n",
       "      <th>study_time_hours</th>\n",
       "      <th>sleep_time_hours</th>\n",
       "      <th>socialmedia_hours</th>\n",
       "      <th>prepandemic_grades</th>\n",
       "      <th>online_interaction</th>\n",
       "      <th>online_doubts</th>\n",
       "      <th>performance_in_online</th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>gender__Female</th>\n",
       "      <th>gender__Male</th>\n",
       "      <th>home_location__Rural</th>\n",
       "      <th>...</th>\n",
       "      <th>status_Poor</th>\n",
       "      <th>status_Rich</th>\n",
       "      <th>does_sports_No</th>\n",
       "      <th>does_sports_Yes</th>\n",
       "      <th>elderly_monitored_No</th>\n",
       "      <th>elderly_monitored_Yes</th>\n",
       "      <th>gaming_interest_No</th>\n",
       "      <th>gaming_interest_Yes</th>\n",
       "      <th>study_room_No</th>\n",
       "      <th>study_room_Yes</th>\n",
       "      <th>group_study_No</th>\n",
       "      <th>group_study_yes</th>\n",
       "      <th>interests_Both</th>\n",
       "      <th>interests_Practical</th>\n",
       "      <th>interests_Theory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ageyears</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018407</td>\n",
       "      <td>0.019206</td>\n",
       "      <td>0.068040</td>\n",
       "      <td>0.009059</td>\n",
       "      <td>-0.067695</td>\n",
       "      <td>0.011068</td>\n",
       "      <td>-0.192750</td>\n",
       "      <td>0.163854</td>\n",
       "      <td>0.122492</td>\n",
       "      <td>0.109907</td>\n",
       "      <td>0.126454</td>\n",
       "      <td>0.034759</td>\n",
       "      <td>-0.034759</td>\n",
       "      <td>0.003205</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047326</td>\n",
       "      <td>0.023414</td>\n",
       "      <td>-0.028113</td>\n",
       "      <td>0.028113</td>\n",
       "      <td>0.037569</td>\n",
       "      <td>-0.037569</td>\n",
       "      <td>0.002852</td>\n",
       "      <td>-0.002852</td>\n",
       "      <td>-0.033317</td>\n",
       "      <td>0.033317</td>\n",
       "      <td>-0.105300</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.090857</td>\n",
       "      <td>0.021785</td>\n",
       "      <td>-0.126185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_subjects</th>\n",
       "      <td>-0.018407</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025308</td>\n",
       "      <td>-0.018954</td>\n",
       "      <td>0.029771</td>\n",
       "      <td>0.051395</td>\n",
       "      <td>0.007021</td>\n",
       "      <td>0.010550</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>-0.003861</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>-0.017408</td>\n",
       "      <td>-0.130298</td>\n",
       "      <td>0.130298</td>\n",
       "      <td>0.029581</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>-0.032776</td>\n",
       "      <td>0.009735</td>\n",
       "      <td>-0.009735</td>\n",
       "      <td>-0.002994</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>-0.011908</td>\n",
       "      <td>0.011908</td>\n",
       "      <td>-0.006871</td>\n",
       "      <td>0.006871</td>\n",
       "      <td>0.059633</td>\n",
       "      <td>-0.059633</td>\n",
       "      <td>0.026852</td>\n",
       "      <td>-0.001452</td>\n",
       "      <td>-0.028132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_size</th>\n",
       "      <td>0.019206</td>\n",
       "      <td>0.025308</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016523</td>\n",
       "      <td>0.062488</td>\n",
       "      <td>-0.015139</td>\n",
       "      <td>0.055168</td>\n",
       "      <td>-0.045161</td>\n",
       "      <td>-0.021616</td>\n",
       "      <td>-0.017465</td>\n",
       "      <td>-0.002605</td>\n",
       "      <td>0.014073</td>\n",
       "      <td>0.018163</td>\n",
       "      <td>-0.018163</td>\n",
       "      <td>0.075088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064421</td>\n",
       "      <td>0.049882</td>\n",
       "      <td>-0.035567</td>\n",
       "      <td>0.035567</td>\n",
       "      <td>-0.110653</td>\n",
       "      <td>0.110653</td>\n",
       "      <td>0.003599</td>\n",
       "      <td>-0.003599</td>\n",
       "      <td>0.046895</td>\n",
       "      <td>-0.046895</td>\n",
       "      <td>-0.032359</td>\n",
       "      <td>0.032359</td>\n",
       "      <td>-0.085559</td>\n",
       "      <td>0.083992</td>\n",
       "      <td>-0.002516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet_facility</th>\n",
       "      <td>0.068040</td>\n",
       "      <td>-0.018954</td>\n",
       "      <td>-0.016523</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013898</td>\n",
       "      <td>0.049981</td>\n",
       "      <td>0.083749</td>\n",
       "      <td>0.113827</td>\n",
       "      <td>0.202672</td>\n",
       "      <td>0.157419</td>\n",
       "      <td>0.262896</td>\n",
       "      <td>0.114446</td>\n",
       "      <td>0.065444</td>\n",
       "      <td>-0.065444</td>\n",
       "      <td>-0.289611</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.150137</td>\n",
       "      <td>0.177332</td>\n",
       "      <td>-0.116004</td>\n",
       "      <td>0.116004</td>\n",
       "      <td>-0.036147</td>\n",
       "      <td>0.036147</td>\n",
       "      <td>0.056866</td>\n",
       "      <td>-0.056866</td>\n",
       "      <td>-0.221508</td>\n",
       "      <td>0.221508</td>\n",
       "      <td>-0.042513</td>\n",
       "      <td>0.042513</td>\n",
       "      <td>0.095404</td>\n",
       "      <td>-0.086147</td>\n",
       "      <td>-0.005914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>study_time_hours</th>\n",
       "      <td>0.009059</td>\n",
       "      <td>0.029771</td>\n",
       "      <td>0.062488</td>\n",
       "      <td>-0.013898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.127854</td>\n",
       "      <td>0.110686</td>\n",
       "      <td>0.145378</td>\n",
       "      <td>0.170712</td>\n",
       "      <td>0.131312</td>\n",
       "      <td>0.082885</td>\n",
       "      <td>0.083275</td>\n",
       "      <td>-0.083275</td>\n",
       "      <td>0.061245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023496</td>\n",
       "      <td>-0.038204</td>\n",
       "      <td>0.052150</td>\n",
       "      <td>-0.052150</td>\n",
       "      <td>-0.083058</td>\n",
       "      <td>0.083058</td>\n",
       "      <td>0.036329</td>\n",
       "      <td>-0.036329</td>\n",
       "      <td>-0.081980</td>\n",
       "      <td>0.081980</td>\n",
       "      <td>-0.014092</td>\n",
       "      <td>0.014092</td>\n",
       "      <td>0.028659</td>\n",
       "      <td>-0.031987</td>\n",
       "      <td>0.005316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sleep_time_hours</th>\n",
       "      <td>-0.067695</td>\n",
       "      <td>0.051395</td>\n",
       "      <td>-0.015139</td>\n",
       "      <td>0.049981</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142652</td>\n",
       "      <td>0.008884</td>\n",
       "      <td>0.036467</td>\n",
       "      <td>0.046848</td>\n",
       "      <td>0.070427</td>\n",
       "      <td>0.021834</td>\n",
       "      <td>-0.004162</td>\n",
       "      <td>0.004162</td>\n",
       "      <td>0.059146</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025577</td>\n",
       "      <td>0.015123</td>\n",
       "      <td>-0.006036</td>\n",
       "      <td>0.006036</td>\n",
       "      <td>0.055412</td>\n",
       "      <td>-0.055412</td>\n",
       "      <td>-0.053235</td>\n",
       "      <td>0.053235</td>\n",
       "      <td>-0.069644</td>\n",
       "      <td>0.069644</td>\n",
       "      <td>0.043329</td>\n",
       "      <td>-0.043329</td>\n",
       "      <td>-0.025122</td>\n",
       "      <td>0.018234</td>\n",
       "      <td>0.006726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>socialmedia_hours</th>\n",
       "      <td>0.011068</td>\n",
       "      <td>0.007021</td>\n",
       "      <td>0.055168</td>\n",
       "      <td>0.083749</td>\n",
       "      <td>-0.127854</td>\n",
       "      <td>0.142652</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.176533</td>\n",
       "      <td>-0.083168</td>\n",
       "      <td>-0.101189</td>\n",
       "      <td>-0.084408</td>\n",
       "      <td>-0.048437</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>-0.069137</td>\n",
       "      <td>-0.073566</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.063885</td>\n",
       "      <td>0.064475</td>\n",
       "      <td>-0.007628</td>\n",
       "      <td>0.007628</td>\n",
       "      <td>0.045698</td>\n",
       "      <td>-0.045698</td>\n",
       "      <td>-0.074618</td>\n",
       "      <td>0.074618</td>\n",
       "      <td>-0.002508</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>-0.056690</td>\n",
       "      <td>0.056690</td>\n",
       "      <td>-0.019810</td>\n",
       "      <td>0.005697</td>\n",
       "      <td>0.015383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prepandemic_grades</th>\n",
       "      <td>-0.192750</td>\n",
       "      <td>0.010550</td>\n",
       "      <td>-0.045161</td>\n",
       "      <td>0.113827</td>\n",
       "      <td>0.110686</td>\n",
       "      <td>0.008884</td>\n",
       "      <td>-0.176533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059298</td>\n",
       "      <td>-0.070405</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>-0.206302</td>\n",
       "      <td>0.023041</td>\n",
       "      <td>-0.023041</td>\n",
       "      <td>-0.127705</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001329</td>\n",
       "      <td>0.077286</td>\n",
       "      <td>0.003973</td>\n",
       "      <td>-0.003973</td>\n",
       "      <td>-0.004313</td>\n",
       "      <td>0.004313</td>\n",
       "      <td>0.100308</td>\n",
       "      <td>-0.100308</td>\n",
       "      <td>-0.082691</td>\n",
       "      <td>0.082691</td>\n",
       "      <td>0.114598</td>\n",
       "      <td>-0.114598</td>\n",
       "      <td>0.133596</td>\n",
       "      <td>-0.024696</td>\n",
       "      <td>-0.119676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_interaction</th>\n",
       "      <td>0.163854</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>-0.021616</td>\n",
       "      <td>0.202672</td>\n",
       "      <td>0.145378</td>\n",
       "      <td>0.036467</td>\n",
       "      <td>-0.083168</td>\n",
       "      <td>-0.059298</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722108</td>\n",
       "      <td>0.557974</td>\n",
       "      <td>0.514485</td>\n",
       "      <td>0.137565</td>\n",
       "      <td>-0.137565</td>\n",
       "      <td>0.074572</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017909</td>\n",
       "      <td>0.037004</td>\n",
       "      <td>-0.035246</td>\n",
       "      <td>0.035246</td>\n",
       "      <td>-0.104525</td>\n",
       "      <td>0.104525</td>\n",
       "      <td>-0.024617</td>\n",
       "      <td>0.024617</td>\n",
       "      <td>-0.074390</td>\n",
       "      <td>0.074390</td>\n",
       "      <td>-0.103150</td>\n",
       "      <td>0.103150</td>\n",
       "      <td>-0.015708</td>\n",
       "      <td>-0.068462</td>\n",
       "      <td>0.096934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_doubts</th>\n",
       "      <td>0.122492</td>\n",
       "      <td>-0.003861</td>\n",
       "      <td>-0.017465</td>\n",
       "      <td>0.157419</td>\n",
       "      <td>0.170712</td>\n",
       "      <td>0.046848</td>\n",
       "      <td>-0.101189</td>\n",
       "      <td>-0.070405</td>\n",
       "      <td>0.722108</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.559356</td>\n",
       "      <td>0.519651</td>\n",
       "      <td>0.121685</td>\n",
       "      <td>-0.121685</td>\n",
       "      <td>0.093630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026896</td>\n",
       "      <td>0.029167</td>\n",
       "      <td>0.027059</td>\n",
       "      <td>-0.027059</td>\n",
       "      <td>-0.112148</td>\n",
       "      <td>0.112148</td>\n",
       "      <td>0.012137</td>\n",
       "      <td>-0.012137</td>\n",
       "      <td>-0.064953</td>\n",
       "      <td>0.064953</td>\n",
       "      <td>-0.112667</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>-0.007137</td>\n",
       "      <td>-0.068703</td>\n",
       "      <td>0.087697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>performance_in_online</th>\n",
       "      <td>0.109907</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>-0.002605</td>\n",
       "      <td>0.262896</td>\n",
       "      <td>0.131312</td>\n",
       "      <td>0.070427</td>\n",
       "      <td>-0.084408</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>0.557974</td>\n",
       "      <td>0.559356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.471088</td>\n",
       "      <td>0.108612</td>\n",
       "      <td>-0.108612</td>\n",
       "      <td>0.013602</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035217</td>\n",
       "      <td>0.054474</td>\n",
       "      <td>-0.003690</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>-0.092615</td>\n",
       "      <td>0.092615</td>\n",
       "      <td>-0.022102</td>\n",
       "      <td>0.022102</td>\n",
       "      <td>-0.075586</td>\n",
       "      <td>0.075586</td>\n",
       "      <td>-0.104955</td>\n",
       "      <td>0.104955</td>\n",
       "      <td>0.028967</td>\n",
       "      <td>-0.073326</td>\n",
       "      <td>0.052974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satisfaction_level</th>\n",
       "      <td>0.126454</td>\n",
       "      <td>-0.017408</td>\n",
       "      <td>0.014073</td>\n",
       "      <td>0.114446</td>\n",
       "      <td>0.082885</td>\n",
       "      <td>0.021834</td>\n",
       "      <td>-0.048437</td>\n",
       "      <td>-0.206302</td>\n",
       "      <td>0.514485</td>\n",
       "      <td>0.519651</td>\n",
       "      <td>0.471088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110579</td>\n",
       "      <td>-0.110579</td>\n",
       "      <td>0.107025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010578</td>\n",
       "      <td>-0.010607</td>\n",
       "      <td>0.056721</td>\n",
       "      <td>-0.056721</td>\n",
       "      <td>-0.057379</td>\n",
       "      <td>0.057379</td>\n",
       "      <td>-0.006052</td>\n",
       "      <td>0.006052</td>\n",
       "      <td>-0.019232</td>\n",
       "      <td>0.019232</td>\n",
       "      <td>-0.053017</td>\n",
       "      <td>0.053017</td>\n",
       "      <td>-0.053972</td>\n",
       "      <td>-0.096807</td>\n",
       "      <td>0.172336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender__Female</th>\n",
       "      <td>0.034759</td>\n",
       "      <td>-0.130298</td>\n",
       "      <td>0.018163</td>\n",
       "      <td>0.065444</td>\n",
       "      <td>0.083275</td>\n",
       "      <td>-0.004162</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>0.023041</td>\n",
       "      <td>0.137565</td>\n",
       "      <td>0.121685</td>\n",
       "      <td>0.108612</td>\n",
       "      <td>0.110579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.068512</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003905</td>\n",
       "      <td>0.021989</td>\n",
       "      <td>0.210550</td>\n",
       "      <td>-0.210550</td>\n",
       "      <td>-0.026932</td>\n",
       "      <td>0.026932</td>\n",
       "      <td>0.221130</td>\n",
       "      <td>-0.221130</td>\n",
       "      <td>0.012588</td>\n",
       "      <td>-0.012588</td>\n",
       "      <td>-0.056483</td>\n",
       "      <td>0.056483</td>\n",
       "      <td>-0.096409</td>\n",
       "      <td>-0.021512</td>\n",
       "      <td>0.132033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender__Male</th>\n",
       "      <td>-0.034759</td>\n",
       "      <td>0.130298</td>\n",
       "      <td>-0.018163</td>\n",
       "      <td>-0.065444</td>\n",
       "      <td>-0.083275</td>\n",
       "      <td>0.004162</td>\n",
       "      <td>-0.069137</td>\n",
       "      <td>-0.023041</td>\n",
       "      <td>-0.137565</td>\n",
       "      <td>-0.121685</td>\n",
       "      <td>-0.108612</td>\n",
       "      <td>-0.110579</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068512</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003905</td>\n",
       "      <td>-0.021989</td>\n",
       "      <td>-0.210550</td>\n",
       "      <td>0.210550</td>\n",
       "      <td>0.026932</td>\n",
       "      <td>-0.026932</td>\n",
       "      <td>-0.221130</td>\n",
       "      <td>0.221130</td>\n",
       "      <td>-0.012588</td>\n",
       "      <td>0.012588</td>\n",
       "      <td>0.056483</td>\n",
       "      <td>-0.056483</td>\n",
       "      <td>0.096409</td>\n",
       "      <td>0.021512</td>\n",
       "      <td>-0.132033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_location__Rural</th>\n",
       "      <td>0.003205</td>\n",
       "      <td>0.029581</td>\n",
       "      <td>0.075088</td>\n",
       "      <td>-0.289611</td>\n",
       "      <td>0.061245</td>\n",
       "      <td>0.059146</td>\n",
       "      <td>-0.073566</td>\n",
       "      <td>-0.127705</td>\n",
       "      <td>0.074572</td>\n",
       "      <td>0.093630</td>\n",
       "      <td>0.013602</td>\n",
       "      <td>0.107025</td>\n",
       "      <td>-0.068512</td>\n",
       "      <td>0.068512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170448</td>\n",
       "      <td>-0.088864</td>\n",
       "      <td>0.082417</td>\n",
       "      <td>-0.082417</td>\n",
       "      <td>0.020355</td>\n",
       "      <td>-0.020355</td>\n",
       "      <td>-0.076592</td>\n",
       "      <td>0.076592</td>\n",
       "      <td>0.091465</td>\n",
       "      <td>-0.091465</td>\n",
       "      <td>0.032202</td>\n",
       "      <td>-0.032202</td>\n",
       "      <td>-0.137686</td>\n",
       "      <td>0.064030</td>\n",
       "      <td>0.078547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_location__Urban</th>\n",
       "      <td>-0.003205</td>\n",
       "      <td>-0.029581</td>\n",
       "      <td>-0.075088</td>\n",
       "      <td>0.289611</td>\n",
       "      <td>-0.061245</td>\n",
       "      <td>-0.059146</td>\n",
       "      <td>0.073566</td>\n",
       "      <td>0.127705</td>\n",
       "      <td>-0.074572</td>\n",
       "      <td>-0.093630</td>\n",
       "      <td>-0.013602</td>\n",
       "      <td>-0.107025</td>\n",
       "      <td>0.068512</td>\n",
       "      <td>-0.068512</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.170448</td>\n",
       "      <td>0.088864</td>\n",
       "      <td>-0.082417</td>\n",
       "      <td>0.082417</td>\n",
       "      <td>-0.020355</td>\n",
       "      <td>0.020355</td>\n",
       "      <td>0.076592</td>\n",
       "      <td>-0.076592</td>\n",
       "      <td>-0.091465</td>\n",
       "      <td>0.091465</td>\n",
       "      <td>-0.032202</td>\n",
       "      <td>0.032202</td>\n",
       "      <td>0.137686</td>\n",
       "      <td>-0.064030</td>\n",
       "      <td>-0.078547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_Post Graduate</th>\n",
       "      <td>0.410842</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.035340</td>\n",
       "      <td>-0.065896</td>\n",
       "      <td>-0.030112</td>\n",
       "      <td>-0.008516</td>\n",
       "      <td>0.018353</td>\n",
       "      <td>-0.177993</td>\n",
       "      <td>0.079691</td>\n",
       "      <td>0.067668</td>\n",
       "      <td>0.017218</td>\n",
       "      <td>0.051062</td>\n",
       "      <td>0.003803</td>\n",
       "      <td>-0.003803</td>\n",
       "      <td>0.040077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014834</td>\n",
       "      <td>-0.047665</td>\n",
       "      <td>0.028087</td>\n",
       "      <td>-0.028087</td>\n",
       "      <td>0.012389</td>\n",
       "      <td>-0.012389</td>\n",
       "      <td>-0.031483</td>\n",
       "      <td>0.031483</td>\n",
       "      <td>0.042201</td>\n",
       "      <td>-0.042201</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>0.071682</td>\n",
       "      <td>-0.067786</td>\n",
       "      <td>0.078015</td>\n",
       "      <td>-0.015312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_School</th>\n",
       "      <td>-0.146328</td>\n",
       "      <td>-0.035474</td>\n",
       "      <td>-0.002217</td>\n",
       "      <td>0.012948</td>\n",
       "      <td>-0.061101</td>\n",
       "      <td>0.058753</td>\n",
       "      <td>0.032315</td>\n",
       "      <td>0.057636</td>\n",
       "      <td>-0.066897</td>\n",
       "      <td>-0.047984</td>\n",
       "      <td>-0.038209</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>-0.022741</td>\n",
       "      <td>0.022741</td>\n",
       "      <td>-0.050468</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015405</td>\n",
       "      <td>-0.011294</td>\n",
       "      <td>0.016083</td>\n",
       "      <td>-0.016083</td>\n",
       "      <td>-0.014304</td>\n",
       "      <td>0.014304</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>-0.000431</td>\n",
       "      <td>-0.061292</td>\n",
       "      <td>0.061292</td>\n",
       "      <td>0.007280</td>\n",
       "      <td>-0.007280</td>\n",
       "      <td>-0.002510</td>\n",
       "      <td>-0.006871</td>\n",
       "      <td>0.010765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_Under Graduate</th>\n",
       "      <td>-0.232690</td>\n",
       "      <td>-0.089322</td>\n",
       "      <td>-0.027126</td>\n",
       "      <td>0.044536</td>\n",
       "      <td>0.066302</td>\n",
       "      <td>-0.033381</td>\n",
       "      <td>-0.037034</td>\n",
       "      <td>0.104760</td>\n",
       "      <td>-0.018728</td>\n",
       "      <td>-0.021950</td>\n",
       "      <td>0.012242</td>\n",
       "      <td>-0.045697</td>\n",
       "      <td>0.012510</td>\n",
       "      <td>-0.012510</td>\n",
       "      <td>0.002118</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022587</td>\n",
       "      <td>0.046380</td>\n",
       "      <td>-0.033794</td>\n",
       "      <td>0.033794</td>\n",
       "      <td>-0.000234</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.025224</td>\n",
       "      <td>-0.025224</td>\n",
       "      <td>0.007817</td>\n",
       "      <td>-0.007817</td>\n",
       "      <td>0.053112</td>\n",
       "      <td>-0.053112</td>\n",
       "      <td>0.056667</td>\n",
       "      <td>-0.058527</td>\n",
       "      <td>0.005031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_Desktop</th>\n",
       "      <td>0.021359</td>\n",
       "      <td>-0.054975</td>\n",
       "      <td>-0.063672</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>-0.010881</td>\n",
       "      <td>-0.008115</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>-0.015256</td>\n",
       "      <td>0.004356</td>\n",
       "      <td>-0.004238</td>\n",
       "      <td>0.015584</td>\n",
       "      <td>0.015952</td>\n",
       "      <td>-0.044436</td>\n",
       "      <td>0.044436</td>\n",
       "      <td>0.014108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035722</td>\n",
       "      <td>-0.027985</td>\n",
       "      <td>-0.021652</td>\n",
       "      <td>0.021652</td>\n",
       "      <td>-0.040564</td>\n",
       "      <td>0.040564</td>\n",
       "      <td>-0.048980</td>\n",
       "      <td>0.048980</td>\n",
       "      <td>-0.071176</td>\n",
       "      <td>0.071176</td>\n",
       "      <td>-0.019400</td>\n",
       "      <td>0.019400</td>\n",
       "      <td>0.042397</td>\n",
       "      <td>-0.025703</td>\n",
       "      <td>-0.017236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_Laptop</th>\n",
       "      <td>0.018520</td>\n",
       "      <td>0.057449</td>\n",
       "      <td>-0.003292</td>\n",
       "      <td>0.246474</td>\n",
       "      <td>0.036855</td>\n",
       "      <td>-0.102572</td>\n",
       "      <td>-0.030061</td>\n",
       "      <td>0.229624</td>\n",
       "      <td>-0.113860</td>\n",
       "      <td>-0.136651</td>\n",
       "      <td>-0.061686</td>\n",
       "      <td>-0.181087</td>\n",
       "      <td>-0.026753</td>\n",
       "      <td>0.026753</td>\n",
       "      <td>-0.294372</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129934</td>\n",
       "      <td>0.101913</td>\n",
       "      <td>-0.061216</td>\n",
       "      <td>0.061216</td>\n",
       "      <td>-0.022714</td>\n",
       "      <td>0.022714</td>\n",
       "      <td>0.098547</td>\n",
       "      <td>-0.098547</td>\n",
       "      <td>-0.169502</td>\n",
       "      <td>0.169502</td>\n",
       "      <td>0.004597</td>\n",
       "      <td>-0.004597</td>\n",
       "      <td>0.102789</td>\n",
       "      <td>-0.000590</td>\n",
       "      <td>-0.113456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>device_Mobile</th>\n",
       "      <td>-0.026068</td>\n",
       "      <td>-0.040040</td>\n",
       "      <td>0.024795</td>\n",
       "      <td>-0.252625</td>\n",
       "      <td>-0.033899</td>\n",
       "      <td>0.107274</td>\n",
       "      <td>0.030273</td>\n",
       "      <td>-0.228897</td>\n",
       "      <td>0.114579</td>\n",
       "      <td>0.140702</td>\n",
       "      <td>0.057623</td>\n",
       "      <td>0.179193</td>\n",
       "      <td>0.042230</td>\n",
       "      <td>-0.042230</td>\n",
       "      <td>0.295275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144457</td>\n",
       "      <td>-0.094448</td>\n",
       "      <td>0.069682</td>\n",
       "      <td>-0.069682</td>\n",
       "      <td>0.036809</td>\n",
       "      <td>-0.036809</td>\n",
       "      <td>-0.083947</td>\n",
       "      <td>0.083947</td>\n",
       "      <td>0.196723</td>\n",
       "      <td>-0.196723</td>\n",
       "      <td>0.001847</td>\n",
       "      <td>-0.001847</td>\n",
       "      <td>-0.119039</td>\n",
       "      <td>0.009256</td>\n",
       "      <td>0.121438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_Middle Class</th>\n",
       "      <td>0.022825</td>\n",
       "      <td>0.015833</td>\n",
       "      <td>-0.083038</td>\n",
       "      <td>0.006899</td>\n",
       "      <td>0.005565</td>\n",
       "      <td>-0.029992</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>-0.048070</td>\n",
       "      <td>-0.037793</td>\n",
       "      <td>-0.039971</td>\n",
       "      <td>-0.006568</td>\n",
       "      <td>-0.001686</td>\n",
       "      <td>-0.010867</td>\n",
       "      <td>0.010867</td>\n",
       "      <td>-0.079320</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.772339</td>\n",
       "      <td>-0.605045</td>\n",
       "      <td>0.023182</td>\n",
       "      <td>-0.023182</td>\n",
       "      <td>-0.045708</td>\n",
       "      <td>0.045708</td>\n",
       "      <td>-0.038323</td>\n",
       "      <td>0.038323</td>\n",
       "      <td>-0.097750</td>\n",
       "      <td>0.097750</td>\n",
       "      <td>-0.010481</td>\n",
       "      <td>0.010481</td>\n",
       "      <td>0.002365</td>\n",
       "      <td>0.008527</td>\n",
       "      <td>-0.012527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_Poor</th>\n",
       "      <td>-0.047326</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.064421</td>\n",
       "      <td>-0.150137</td>\n",
       "      <td>0.023496</td>\n",
       "      <td>0.025577</td>\n",
       "      <td>-0.063885</td>\n",
       "      <td>-0.001329</td>\n",
       "      <td>0.017909</td>\n",
       "      <td>0.026896</td>\n",
       "      <td>-0.035217</td>\n",
       "      <td>0.010578</td>\n",
       "      <td>-0.003905</td>\n",
       "      <td>0.003905</td>\n",
       "      <td>0.170448</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038449</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>-0.069445</td>\n",
       "      <td>0.049526</td>\n",
       "      <td>-0.049526</td>\n",
       "      <td>-0.018849</td>\n",
       "      <td>0.018849</td>\n",
       "      <td>0.134702</td>\n",
       "      <td>-0.134702</td>\n",
       "      <td>0.021850</td>\n",
       "      <td>-0.021850</td>\n",
       "      <td>-0.031115</td>\n",
       "      <td>0.005624</td>\n",
       "      <td>0.028021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_Rich</th>\n",
       "      <td>0.023414</td>\n",
       "      <td>-0.032776</td>\n",
       "      <td>0.049882</td>\n",
       "      <td>0.177332</td>\n",
       "      <td>-0.038204</td>\n",
       "      <td>0.015123</td>\n",
       "      <td>0.064475</td>\n",
       "      <td>0.077286</td>\n",
       "      <td>0.037004</td>\n",
       "      <td>0.029167</td>\n",
       "      <td>0.054474</td>\n",
       "      <td>-0.010607</td>\n",
       "      <td>0.021989</td>\n",
       "      <td>-0.021989</td>\n",
       "      <td>-0.088864</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038449</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.123512</td>\n",
       "      <td>0.123512</td>\n",
       "      <td>0.009827</td>\n",
       "      <td>-0.009827</td>\n",
       "      <td>0.083912</td>\n",
       "      <td>-0.083912</td>\n",
       "      <td>-0.015068</td>\n",
       "      <td>0.015068</td>\n",
       "      <td>-0.010900</td>\n",
       "      <td>0.010900</td>\n",
       "      <td>0.035279</td>\n",
       "      <td>-0.020463</td>\n",
       "      <td>-0.015416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>does_sports_No</th>\n",
       "      <td>-0.028113</td>\n",
       "      <td>0.009735</td>\n",
       "      <td>-0.035567</td>\n",
       "      <td>-0.116004</td>\n",
       "      <td>0.052150</td>\n",
       "      <td>-0.006036</td>\n",
       "      <td>-0.007628</td>\n",
       "      <td>0.003973</td>\n",
       "      <td>-0.035246</td>\n",
       "      <td>0.027059</td>\n",
       "      <td>-0.003690</td>\n",
       "      <td>0.056721</td>\n",
       "      <td>0.210550</td>\n",
       "      <td>-0.210550</td>\n",
       "      <td>0.082417</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>-0.123512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.071641</td>\n",
       "      <td>-0.071641</td>\n",
       "      <td>0.126158</td>\n",
       "      <td>-0.126158</td>\n",
       "      <td>0.077050</td>\n",
       "      <td>-0.077050</td>\n",
       "      <td>0.094760</td>\n",
       "      <td>-0.094760</td>\n",
       "      <td>-0.016758</td>\n",
       "      <td>-0.060093</td>\n",
       "      <td>0.088384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>does_sports_Yes</th>\n",
       "      <td>0.028113</td>\n",
       "      <td>-0.009735</td>\n",
       "      <td>0.035567</td>\n",
       "      <td>0.116004</td>\n",
       "      <td>-0.052150</td>\n",
       "      <td>0.006036</td>\n",
       "      <td>0.007628</td>\n",
       "      <td>-0.003973</td>\n",
       "      <td>0.035246</td>\n",
       "      <td>-0.027059</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>-0.056721</td>\n",
       "      <td>-0.210550</td>\n",
       "      <td>0.210550</td>\n",
       "      <td>-0.082417</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069445</td>\n",
       "      <td>0.123512</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.071641</td>\n",
       "      <td>0.071641</td>\n",
       "      <td>-0.126158</td>\n",
       "      <td>0.126158</td>\n",
       "      <td>-0.077050</td>\n",
       "      <td>0.077050</td>\n",
       "      <td>-0.094760</td>\n",
       "      <td>0.094760</td>\n",
       "      <td>0.016758</td>\n",
       "      <td>0.060093</td>\n",
       "      <td>-0.088384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elderly_monitored_No</th>\n",
       "      <td>0.037569</td>\n",
       "      <td>-0.002994</td>\n",
       "      <td>-0.110653</td>\n",
       "      <td>-0.036147</td>\n",
       "      <td>-0.083058</td>\n",
       "      <td>0.055412</td>\n",
       "      <td>0.045698</td>\n",
       "      <td>-0.004313</td>\n",
       "      <td>-0.104525</td>\n",
       "      <td>-0.112148</td>\n",
       "      <td>-0.092615</td>\n",
       "      <td>-0.057379</td>\n",
       "      <td>-0.026932</td>\n",
       "      <td>0.026932</td>\n",
       "      <td>0.020355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049526</td>\n",
       "      <td>0.009827</td>\n",
       "      <td>0.071641</td>\n",
       "      <td>-0.071641</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.034529</td>\n",
       "      <td>-0.034529</td>\n",
       "      <td>0.047137</td>\n",
       "      <td>-0.047137</td>\n",
       "      <td>0.082916</td>\n",
       "      <td>-0.082916</td>\n",
       "      <td>0.048648</td>\n",
       "      <td>-0.050067</td>\n",
       "      <td>0.004112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elderly_monitored_Yes</th>\n",
       "      <td>-0.037569</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>0.110653</td>\n",
       "      <td>0.036147</td>\n",
       "      <td>0.083058</td>\n",
       "      <td>-0.055412</td>\n",
       "      <td>-0.045698</td>\n",
       "      <td>0.004313</td>\n",
       "      <td>0.104525</td>\n",
       "      <td>0.112148</td>\n",
       "      <td>0.092615</td>\n",
       "      <td>0.057379</td>\n",
       "      <td>0.026932</td>\n",
       "      <td>-0.026932</td>\n",
       "      <td>-0.020355</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.049526</td>\n",
       "      <td>-0.009827</td>\n",
       "      <td>-0.071641</td>\n",
       "      <td>0.071641</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034529</td>\n",
       "      <td>0.034529</td>\n",
       "      <td>-0.047137</td>\n",
       "      <td>0.047137</td>\n",
       "      <td>-0.082916</td>\n",
       "      <td>0.082916</td>\n",
       "      <td>-0.048648</td>\n",
       "      <td>0.050067</td>\n",
       "      <td>-0.004112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaming_interest_No</th>\n",
       "      <td>0.002852</td>\n",
       "      <td>-0.011908</td>\n",
       "      <td>0.003599</td>\n",
       "      <td>0.056866</td>\n",
       "      <td>0.036329</td>\n",
       "      <td>-0.053235</td>\n",
       "      <td>-0.074618</td>\n",
       "      <td>0.100308</td>\n",
       "      <td>-0.024617</td>\n",
       "      <td>0.012137</td>\n",
       "      <td>-0.022102</td>\n",
       "      <td>-0.006052</td>\n",
       "      <td>0.221130</td>\n",
       "      <td>-0.221130</td>\n",
       "      <td>-0.076592</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018849</td>\n",
       "      <td>0.083912</td>\n",
       "      <td>0.126158</td>\n",
       "      <td>-0.126158</td>\n",
       "      <td>0.034529</td>\n",
       "      <td>-0.034529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.011165</td>\n",
       "      <td>0.011165</td>\n",
       "      <td>0.066180</td>\n",
       "      <td>-0.066180</td>\n",
       "      <td>0.008952</td>\n",
       "      <td>-0.083690</td>\n",
       "      <td>0.087232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaming_interest_Yes</th>\n",
       "      <td>-0.002852</td>\n",
       "      <td>0.011908</td>\n",
       "      <td>-0.003599</td>\n",
       "      <td>-0.056866</td>\n",
       "      <td>-0.036329</td>\n",
       "      <td>0.053235</td>\n",
       "      <td>0.074618</td>\n",
       "      <td>-0.100308</td>\n",
       "      <td>0.024617</td>\n",
       "      <td>-0.012137</td>\n",
       "      <td>0.022102</td>\n",
       "      <td>0.006052</td>\n",
       "      <td>-0.221130</td>\n",
       "      <td>0.221130</td>\n",
       "      <td>0.076592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018849</td>\n",
       "      <td>-0.083912</td>\n",
       "      <td>-0.126158</td>\n",
       "      <td>0.126158</td>\n",
       "      <td>-0.034529</td>\n",
       "      <td>0.034529</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011165</td>\n",
       "      <td>-0.011165</td>\n",
       "      <td>-0.066180</td>\n",
       "      <td>0.066180</td>\n",
       "      <td>-0.008952</td>\n",
       "      <td>0.083690</td>\n",
       "      <td>-0.087232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>study_room_No</th>\n",
       "      <td>-0.033317</td>\n",
       "      <td>-0.006871</td>\n",
       "      <td>0.046895</td>\n",
       "      <td>-0.221508</td>\n",
       "      <td>-0.081980</td>\n",
       "      <td>-0.069644</td>\n",
       "      <td>-0.002508</td>\n",
       "      <td>-0.082691</td>\n",
       "      <td>-0.074390</td>\n",
       "      <td>-0.064953</td>\n",
       "      <td>-0.075586</td>\n",
       "      <td>-0.019232</td>\n",
       "      <td>0.012588</td>\n",
       "      <td>-0.012588</td>\n",
       "      <td>0.091465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134702</td>\n",
       "      <td>-0.015068</td>\n",
       "      <td>0.077050</td>\n",
       "      <td>-0.077050</td>\n",
       "      <td>0.047137</td>\n",
       "      <td>-0.047137</td>\n",
       "      <td>-0.011165</td>\n",
       "      <td>0.011165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.077055</td>\n",
       "      <td>-0.077055</td>\n",
       "      <td>-0.069756</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>0.014512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>study_room_Yes</th>\n",
       "      <td>0.033317</td>\n",
       "      <td>0.006871</td>\n",
       "      <td>-0.046895</td>\n",
       "      <td>0.221508</td>\n",
       "      <td>0.081980</td>\n",
       "      <td>0.069644</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>0.082691</td>\n",
       "      <td>0.074390</td>\n",
       "      <td>0.064953</td>\n",
       "      <td>0.075586</td>\n",
       "      <td>0.019232</td>\n",
       "      <td>-0.012588</td>\n",
       "      <td>0.012588</td>\n",
       "      <td>-0.091465</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.134702</td>\n",
       "      <td>0.015068</td>\n",
       "      <td>-0.077050</td>\n",
       "      <td>0.077050</td>\n",
       "      <td>-0.047137</td>\n",
       "      <td>0.047137</td>\n",
       "      <td>0.011165</td>\n",
       "      <td>-0.011165</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077055</td>\n",
       "      <td>0.077055</td>\n",
       "      <td>0.069756</td>\n",
       "      <td>-0.054214</td>\n",
       "      <td>-0.014512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_study_No</th>\n",
       "      <td>-0.105300</td>\n",
       "      <td>0.059633</td>\n",
       "      <td>-0.032359</td>\n",
       "      <td>-0.042513</td>\n",
       "      <td>-0.014092</td>\n",
       "      <td>0.043329</td>\n",
       "      <td>-0.056690</td>\n",
       "      <td>0.114598</td>\n",
       "      <td>-0.103150</td>\n",
       "      <td>-0.112667</td>\n",
       "      <td>-0.104955</td>\n",
       "      <td>-0.053017</td>\n",
       "      <td>-0.056483</td>\n",
       "      <td>0.056483</td>\n",
       "      <td>0.032202</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021850</td>\n",
       "      <td>-0.010900</td>\n",
       "      <td>0.094760</td>\n",
       "      <td>-0.094760</td>\n",
       "      <td>0.082916</td>\n",
       "      <td>-0.082916</td>\n",
       "      <td>0.066180</td>\n",
       "      <td>-0.066180</td>\n",
       "      <td>0.077055</td>\n",
       "      <td>-0.077055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.024060</td>\n",
       "      <td>0.010071</td>\n",
       "      <td>0.015024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_study_yes</th>\n",
       "      <td>0.105300</td>\n",
       "      <td>-0.059633</td>\n",
       "      <td>0.032359</td>\n",
       "      <td>0.042513</td>\n",
       "      <td>0.014092</td>\n",
       "      <td>-0.043329</td>\n",
       "      <td>0.056690</td>\n",
       "      <td>-0.114598</td>\n",
       "      <td>0.103150</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.104955</td>\n",
       "      <td>0.053017</td>\n",
       "      <td>0.056483</td>\n",
       "      <td>-0.056483</td>\n",
       "      <td>-0.032202</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021850</td>\n",
       "      <td>0.010900</td>\n",
       "      <td>-0.094760</td>\n",
       "      <td>0.094760</td>\n",
       "      <td>-0.082916</td>\n",
       "      <td>0.082916</td>\n",
       "      <td>-0.066180</td>\n",
       "      <td>0.066180</td>\n",
       "      <td>-0.077055</td>\n",
       "      <td>0.077055</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024060</td>\n",
       "      <td>-0.010071</td>\n",
       "      <td>-0.015024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interests_Both</th>\n",
       "      <td>0.090857</td>\n",
       "      <td>0.026852</td>\n",
       "      <td>-0.085559</td>\n",
       "      <td>0.095404</td>\n",
       "      <td>0.028659</td>\n",
       "      <td>-0.025122</td>\n",
       "      <td>-0.019810</td>\n",
       "      <td>0.133596</td>\n",
       "      <td>-0.015708</td>\n",
       "      <td>-0.007137</td>\n",
       "      <td>0.028967</td>\n",
       "      <td>-0.053972</td>\n",
       "      <td>-0.096409</td>\n",
       "      <td>0.096409</td>\n",
       "      <td>-0.137686</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031115</td>\n",
       "      <td>0.035279</td>\n",
       "      <td>-0.016758</td>\n",
       "      <td>0.016758</td>\n",
       "      <td>0.048648</td>\n",
       "      <td>-0.048648</td>\n",
       "      <td>0.008952</td>\n",
       "      <td>-0.008952</td>\n",
       "      <td>-0.069756</td>\n",
       "      <td>0.069756</td>\n",
       "      <td>-0.024060</td>\n",
       "      <td>0.024060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.613198</td>\n",
       "      <td>-0.398451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interests_Practical</th>\n",
       "      <td>0.021785</td>\n",
       "      <td>-0.001452</td>\n",
       "      <td>0.083992</td>\n",
       "      <td>-0.086147</td>\n",
       "      <td>-0.031987</td>\n",
       "      <td>0.018234</td>\n",
       "      <td>0.005697</td>\n",
       "      <td>-0.024696</td>\n",
       "      <td>-0.068462</td>\n",
       "      <td>-0.068703</td>\n",
       "      <td>-0.073326</td>\n",
       "      <td>-0.096807</td>\n",
       "      <td>-0.021512</td>\n",
       "      <td>0.021512</td>\n",
       "      <td>0.064030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005624</td>\n",
       "      <td>-0.020463</td>\n",
       "      <td>-0.060093</td>\n",
       "      <td>0.060093</td>\n",
       "      <td>-0.050067</td>\n",
       "      <td>0.050067</td>\n",
       "      <td>-0.083690</td>\n",
       "      <td>0.083690</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.054214</td>\n",
       "      <td>0.010071</td>\n",
       "      <td>-0.010071</td>\n",
       "      <td>-0.613198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.480185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interests_Theory</th>\n",
       "      <td>-0.126185</td>\n",
       "      <td>-0.028132</td>\n",
       "      <td>-0.002516</td>\n",
       "      <td>-0.005914</td>\n",
       "      <td>0.005316</td>\n",
       "      <td>0.006726</td>\n",
       "      <td>0.015383</td>\n",
       "      <td>-0.119676</td>\n",
       "      <td>0.096934</td>\n",
       "      <td>0.087697</td>\n",
       "      <td>0.052974</td>\n",
       "      <td>0.172336</td>\n",
       "      <td>0.132033</td>\n",
       "      <td>-0.132033</td>\n",
       "      <td>0.078547</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028021</td>\n",
       "      <td>-0.015416</td>\n",
       "      <td>0.088384</td>\n",
       "      <td>-0.088384</td>\n",
       "      <td>0.004112</td>\n",
       "      <td>-0.004112</td>\n",
       "      <td>0.087232</td>\n",
       "      <td>-0.087232</td>\n",
       "      <td>0.014512</td>\n",
       "      <td>-0.014512</td>\n",
       "      <td>0.015024</td>\n",
       "      <td>-0.015024</td>\n",
       "      <td>-0.398451</td>\n",
       "      <td>-0.480185</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ageyears  number_of_subjects  family_size  \\\n",
       "ageyears                  1.000000           -0.018407     0.019206   \n",
       "number_of_subjects       -0.018407            1.000000     0.025308   \n",
       "family_size               0.019206            0.025308     1.000000   \n",
       "internet_facility         0.068040           -0.018954    -0.016523   \n",
       "study_time_hours          0.009059            0.029771     0.062488   \n",
       "sleep_time_hours         -0.067695            0.051395    -0.015139   \n",
       "socialmedia_hours         0.011068            0.007021     0.055168   \n",
       "prepandemic_grades       -0.192750            0.010550    -0.045161   \n",
       "online_interaction        0.163854           -0.000708    -0.021616   \n",
       "online_doubts             0.122492           -0.003861    -0.017465   \n",
       "performance_in_online     0.109907            0.005644    -0.002605   \n",
       "satisfaction_level        0.126454           -0.017408     0.014073   \n",
       "gender__Female            0.034759           -0.130298     0.018163   \n",
       "gender__Male             -0.034759            0.130298    -0.018163   \n",
       "home_location__Rural      0.003205            0.029581     0.075088   \n",
       "home_location__Urban     -0.003205           -0.029581    -0.075088   \n",
       "education_Post Graduate   0.410842            0.140202     0.035340   \n",
       "education_School         -0.146328           -0.035474    -0.002217   \n",
       "education_Under Graduate -0.232690           -0.089322    -0.027126   \n",
       "device_Desktop            0.021359           -0.054975    -0.063672   \n",
       "device_Laptop             0.018520            0.057449    -0.003292   \n",
       "device_Mobile            -0.026068           -0.040040     0.024795   \n",
       "status_Middle Class       0.022825            0.015833    -0.083038   \n",
       "status_Poor              -0.047326            0.006278     0.064421   \n",
       "status_Rich               0.023414           -0.032776     0.049882   \n",
       "does_sports_No           -0.028113            0.009735    -0.035567   \n",
       "does_sports_Yes           0.028113           -0.009735     0.035567   \n",
       "elderly_monitored_No      0.037569           -0.002994    -0.110653   \n",
       "elderly_monitored_Yes    -0.037569            0.002994     0.110653   \n",
       "gaming_interest_No        0.002852           -0.011908     0.003599   \n",
       "gaming_interest_Yes      -0.002852            0.011908    -0.003599   \n",
       "study_room_No            -0.033317           -0.006871     0.046895   \n",
       "study_room_Yes            0.033317            0.006871    -0.046895   \n",
       "group_study_No           -0.105300            0.059633    -0.032359   \n",
       "group_study_yes           0.105300           -0.059633     0.032359   \n",
       "interests_Both            0.090857            0.026852    -0.085559   \n",
       "interests_Practical       0.021785           -0.001452     0.083992   \n",
       "interests_Theory         -0.126185           -0.028132    -0.002516   \n",
       "\n",
       "                          internet_facility  study_time_hours  \\\n",
       "ageyears                           0.068040          0.009059   \n",
       "number_of_subjects                -0.018954          0.029771   \n",
       "family_size                       -0.016523          0.062488   \n",
       "internet_facility                  1.000000         -0.013898   \n",
       "study_time_hours                  -0.013898          1.000000   \n",
       "sleep_time_hours                   0.049981         -0.044223   \n",
       "socialmedia_hours                  0.083749         -0.127854   \n",
       "prepandemic_grades                 0.113827          0.110686   \n",
       "online_interaction                 0.202672          0.145378   \n",
       "online_doubts                      0.157419          0.170712   \n",
       "performance_in_online              0.262896          0.131312   \n",
       "satisfaction_level                 0.114446          0.082885   \n",
       "gender__Female                     0.065444          0.083275   \n",
       "gender__Male                      -0.065444         -0.083275   \n",
       "home_location__Rural              -0.289611          0.061245   \n",
       "home_location__Urban               0.289611         -0.061245   \n",
       "education_Post Graduate           -0.065896         -0.030112   \n",
       "education_School                   0.012948         -0.061101   \n",
       "education_Under Graduate           0.044536          0.066302   \n",
       "device_Desktop                     0.004210         -0.010881   \n",
       "device_Laptop                      0.246474          0.036855   \n",
       "device_Mobile                     -0.252625         -0.033899   \n",
       "status_Middle Class                0.006899          0.005565   \n",
       "status_Poor                       -0.150137          0.023496   \n",
       "status_Rich                        0.177332         -0.038204   \n",
       "does_sports_No                    -0.116004          0.052150   \n",
       "does_sports_Yes                    0.116004         -0.052150   \n",
       "elderly_monitored_No              -0.036147         -0.083058   \n",
       "elderly_monitored_Yes              0.036147          0.083058   \n",
       "gaming_interest_No                 0.056866          0.036329   \n",
       "gaming_interest_Yes               -0.056866         -0.036329   \n",
       "study_room_No                     -0.221508         -0.081980   \n",
       "study_room_Yes                     0.221508          0.081980   \n",
       "group_study_No                    -0.042513         -0.014092   \n",
       "group_study_yes                    0.042513          0.014092   \n",
       "interests_Both                     0.095404          0.028659   \n",
       "interests_Practical               -0.086147         -0.031987   \n",
       "interests_Theory                  -0.005914          0.005316   \n",
       "\n",
       "                          sleep_time_hours  socialmedia_hours  \\\n",
       "ageyears                         -0.067695           0.011068   \n",
       "number_of_subjects                0.051395           0.007021   \n",
       "family_size                      -0.015139           0.055168   \n",
       "internet_facility                 0.049981           0.083749   \n",
       "study_time_hours                 -0.044223          -0.127854   \n",
       "sleep_time_hours                  1.000000           0.142652   \n",
       "socialmedia_hours                 0.142652           1.000000   \n",
       "prepandemic_grades                0.008884          -0.176533   \n",
       "online_interaction                0.036467          -0.083168   \n",
       "online_doubts                     0.046848          -0.101189   \n",
       "performance_in_online             0.070427          -0.084408   \n",
       "satisfaction_level                0.021834          -0.048437   \n",
       "gender__Female                   -0.004162           0.069137   \n",
       "gender__Male                      0.004162          -0.069137   \n",
       "home_location__Rural              0.059146          -0.073566   \n",
       "home_location__Urban             -0.059146           0.073566   \n",
       "education_Post Graduate          -0.008516           0.018353   \n",
       "education_School                  0.058753           0.032315   \n",
       "education_Under Graduate         -0.033381          -0.037034   \n",
       "device_Desktop                   -0.008115           0.001084   \n",
       "device_Laptop                    -0.102572          -0.030061   \n",
       "device_Mobile                     0.107274           0.030273   \n",
       "status_Middle Class              -0.029992           0.009917   \n",
       "status_Poor                       0.025577          -0.063885   \n",
       "status_Rich                       0.015123           0.064475   \n",
       "does_sports_No                   -0.006036          -0.007628   \n",
       "does_sports_Yes                   0.006036           0.007628   \n",
       "elderly_monitored_No              0.055412           0.045698   \n",
       "elderly_monitored_Yes            -0.055412          -0.045698   \n",
       "gaming_interest_No               -0.053235          -0.074618   \n",
       "gaming_interest_Yes               0.053235           0.074618   \n",
       "study_room_No                    -0.069644          -0.002508   \n",
       "study_room_Yes                    0.069644           0.002508   \n",
       "group_study_No                    0.043329          -0.056690   \n",
       "group_study_yes                  -0.043329           0.056690   \n",
       "interests_Both                   -0.025122          -0.019810   \n",
       "interests_Practical               0.018234           0.005697   \n",
       "interests_Theory                  0.006726           0.015383   \n",
       "\n",
       "                          prepandemic_grades  online_interaction  \\\n",
       "ageyears                           -0.192750            0.163854   \n",
       "number_of_subjects                  0.010550           -0.000708   \n",
       "family_size                        -0.045161           -0.021616   \n",
       "internet_facility                   0.113827            0.202672   \n",
       "study_time_hours                    0.110686            0.145378   \n",
       "sleep_time_hours                    0.008884            0.036467   \n",
       "socialmedia_hours                  -0.176533           -0.083168   \n",
       "prepandemic_grades                  1.000000           -0.059298   \n",
       "online_interaction                 -0.059298            1.000000   \n",
       "online_doubts                      -0.070405            0.722108   \n",
       "performance_in_online               0.003419            0.557974   \n",
       "satisfaction_level                 -0.206302            0.514485   \n",
       "gender__Female                      0.023041            0.137565   \n",
       "gender__Male                       -0.023041           -0.137565   \n",
       "home_location__Rural               -0.127705            0.074572   \n",
       "home_location__Urban                0.127705           -0.074572   \n",
       "education_Post Graduate            -0.177993            0.079691   \n",
       "education_School                    0.057636           -0.066897   \n",
       "education_Under Graduate            0.104760           -0.018728   \n",
       "device_Desktop                     -0.015256            0.004356   \n",
       "device_Laptop                       0.229624           -0.113860   \n",
       "device_Mobile                      -0.228897            0.114579   \n",
       "status_Middle Class                -0.048070           -0.037793   \n",
       "status_Poor                        -0.001329            0.017909   \n",
       "status_Rich                         0.077286            0.037004   \n",
       "does_sports_No                      0.003973           -0.035246   \n",
       "does_sports_Yes                    -0.003973            0.035246   \n",
       "elderly_monitored_No               -0.004313           -0.104525   \n",
       "elderly_monitored_Yes               0.004313            0.104525   \n",
       "gaming_interest_No                  0.100308           -0.024617   \n",
       "gaming_interest_Yes                -0.100308            0.024617   \n",
       "study_room_No                      -0.082691           -0.074390   \n",
       "study_room_Yes                      0.082691            0.074390   \n",
       "group_study_No                      0.114598           -0.103150   \n",
       "group_study_yes                    -0.114598            0.103150   \n",
       "interests_Both                      0.133596           -0.015708   \n",
       "interests_Practical                -0.024696           -0.068462   \n",
       "interests_Theory                   -0.119676            0.096934   \n",
       "\n",
       "                          online_doubts  performance_in_online  \\\n",
       "ageyears                       0.122492               0.109907   \n",
       "number_of_subjects            -0.003861               0.005644   \n",
       "family_size                   -0.017465              -0.002605   \n",
       "internet_facility              0.157419               0.262896   \n",
       "study_time_hours               0.170712               0.131312   \n",
       "sleep_time_hours               0.046848               0.070427   \n",
       "socialmedia_hours             -0.101189              -0.084408   \n",
       "prepandemic_grades            -0.070405               0.003419   \n",
       "online_interaction             0.722108               0.557974   \n",
       "online_doubts                  1.000000               0.559356   \n",
       "performance_in_online          0.559356               1.000000   \n",
       "satisfaction_level             0.519651               0.471088   \n",
       "gender__Female                 0.121685               0.108612   \n",
       "gender__Male                  -0.121685              -0.108612   \n",
       "home_location__Rural           0.093630               0.013602   \n",
       "home_location__Urban          -0.093630              -0.013602   \n",
       "education_Post Graduate        0.067668               0.017218   \n",
       "education_School              -0.047984              -0.038209   \n",
       "education_Under Graduate      -0.021950               0.012242   \n",
       "device_Desktop                -0.004238               0.015584   \n",
       "device_Laptop                 -0.136651              -0.061686   \n",
       "device_Mobile                  0.140702               0.057623   \n",
       "status_Middle Class           -0.039971              -0.006568   \n",
       "status_Poor                    0.026896              -0.035217   \n",
       "status_Rich                    0.029167               0.054474   \n",
       "does_sports_No                 0.027059              -0.003690   \n",
       "does_sports_Yes               -0.027059               0.003690   \n",
       "elderly_monitored_No          -0.112148              -0.092615   \n",
       "elderly_monitored_Yes          0.112148               0.092615   \n",
       "gaming_interest_No             0.012137              -0.022102   \n",
       "gaming_interest_Yes           -0.012137               0.022102   \n",
       "study_room_No                 -0.064953              -0.075586   \n",
       "study_room_Yes                 0.064953               0.075586   \n",
       "group_study_No                -0.112667              -0.104955   \n",
       "group_study_yes                0.112667               0.104955   \n",
       "interests_Both                -0.007137               0.028967   \n",
       "interests_Practical           -0.068703              -0.073326   \n",
       "interests_Theory               0.087697               0.052974   \n",
       "\n",
       "                          satisfaction_level  gender__Female  gender__Male  \\\n",
       "ageyears                            0.126454        0.034759     -0.034759   \n",
       "number_of_subjects                 -0.017408       -0.130298      0.130298   \n",
       "family_size                         0.014073        0.018163     -0.018163   \n",
       "internet_facility                   0.114446        0.065444     -0.065444   \n",
       "study_time_hours                    0.082885        0.083275     -0.083275   \n",
       "sleep_time_hours                    0.021834       -0.004162      0.004162   \n",
       "socialmedia_hours                  -0.048437        0.069137     -0.069137   \n",
       "prepandemic_grades                 -0.206302        0.023041     -0.023041   \n",
       "online_interaction                  0.514485        0.137565     -0.137565   \n",
       "online_doubts                       0.519651        0.121685     -0.121685   \n",
       "performance_in_online               0.471088        0.108612     -0.108612   \n",
       "satisfaction_level                  1.000000        0.110579     -0.110579   \n",
       "gender__Female                      0.110579        1.000000     -1.000000   \n",
       "gender__Male                       -0.110579       -1.000000      1.000000   \n",
       "home_location__Rural                0.107025       -0.068512      0.068512   \n",
       "home_location__Urban               -0.107025        0.068512     -0.068512   \n",
       "education_Post Graduate             0.051062        0.003803     -0.003803   \n",
       "education_School                    0.006282       -0.022741      0.022741   \n",
       "education_Under Graduate           -0.045697        0.012510     -0.012510   \n",
       "device_Desktop                      0.015952       -0.044436      0.044436   \n",
       "device_Laptop                      -0.181087       -0.026753      0.026753   \n",
       "device_Mobile                       0.179193        0.042230     -0.042230   \n",
       "status_Middle Class                -0.001686       -0.010867      0.010867   \n",
       "status_Poor                         0.010578       -0.003905      0.003905   \n",
       "status_Rich                        -0.010607        0.021989     -0.021989   \n",
       "does_sports_No                      0.056721        0.210550     -0.210550   \n",
       "does_sports_Yes                    -0.056721       -0.210550      0.210550   \n",
       "elderly_monitored_No               -0.057379       -0.026932      0.026932   \n",
       "elderly_monitored_Yes               0.057379        0.026932     -0.026932   \n",
       "gaming_interest_No                 -0.006052        0.221130     -0.221130   \n",
       "gaming_interest_Yes                 0.006052       -0.221130      0.221130   \n",
       "study_room_No                      -0.019232        0.012588     -0.012588   \n",
       "study_room_Yes                      0.019232       -0.012588      0.012588   \n",
       "group_study_No                     -0.053017       -0.056483      0.056483   \n",
       "group_study_yes                     0.053017        0.056483     -0.056483   \n",
       "interests_Both                     -0.053972       -0.096409      0.096409   \n",
       "interests_Practical                -0.096807       -0.021512      0.021512   \n",
       "interests_Theory                    0.172336        0.132033     -0.132033   \n",
       "\n",
       "                          home_location__Rural  ...  status_Poor  status_Rich  \\\n",
       "ageyears                              0.003205  ...    -0.047326     0.023414   \n",
       "number_of_subjects                    0.029581  ...     0.006278    -0.032776   \n",
       "family_size                           0.075088  ...     0.064421     0.049882   \n",
       "internet_facility                    -0.289611  ...    -0.150137     0.177332   \n",
       "study_time_hours                      0.061245  ...     0.023496    -0.038204   \n",
       "sleep_time_hours                      0.059146  ...     0.025577     0.015123   \n",
       "socialmedia_hours                    -0.073566  ...    -0.063885     0.064475   \n",
       "prepandemic_grades                   -0.127705  ...    -0.001329     0.077286   \n",
       "online_interaction                    0.074572  ...     0.017909     0.037004   \n",
       "online_doubts                         0.093630  ...     0.026896     0.029167   \n",
       "performance_in_online                 0.013602  ...    -0.035217     0.054474   \n",
       "satisfaction_level                    0.107025  ...     0.010578    -0.010607   \n",
       "gender__Female                       -0.068512  ...    -0.003905     0.021989   \n",
       "gender__Male                          0.068512  ...     0.003905    -0.021989   \n",
       "home_location__Rural                  1.000000  ...     0.170448    -0.088864   \n",
       "home_location__Urban                 -1.000000  ...    -0.170448     0.088864   \n",
       "education_Post Graduate               0.040077  ...     0.014834    -0.047665   \n",
       "education_School                     -0.050468  ...     0.015405    -0.011294   \n",
       "education_Under Graduate              0.002118  ...    -0.022587     0.046380   \n",
       "device_Desktop                        0.014108  ...    -0.035722    -0.027985   \n",
       "device_Laptop                        -0.294372  ...    -0.129934     0.101913   \n",
       "device_Mobile                         0.295275  ...     0.144457    -0.094448   \n",
       "status_Middle Class                  -0.079320  ...    -0.772339    -0.605045   \n",
       "status_Poor                           0.170448  ...     1.000000    -0.038449   \n",
       "status_Rich                          -0.088864  ...    -0.038449     1.000000   \n",
       "does_sports_No                        0.082417  ...     0.069445    -0.123512   \n",
       "does_sports_Yes                      -0.082417  ...    -0.069445     0.123512   \n",
       "elderly_monitored_No                  0.020355  ...     0.049526     0.009827   \n",
       "elderly_monitored_Yes                -0.020355  ...    -0.049526    -0.009827   \n",
       "gaming_interest_No                   -0.076592  ...    -0.018849     0.083912   \n",
       "gaming_interest_Yes                   0.076592  ...     0.018849    -0.083912   \n",
       "study_room_No                         0.091465  ...     0.134702    -0.015068   \n",
       "study_room_Yes                       -0.091465  ...    -0.134702     0.015068   \n",
       "group_study_No                        0.032202  ...     0.021850    -0.010900   \n",
       "group_study_yes                      -0.032202  ...    -0.021850     0.010900   \n",
       "interests_Both                       -0.137686  ...    -0.031115     0.035279   \n",
       "interests_Practical                   0.064030  ...     0.005624    -0.020463   \n",
       "interests_Theory                      0.078547  ...     0.028021    -0.015416   \n",
       "\n",
       "                          does_sports_No  does_sports_Yes  \\\n",
       "ageyears                       -0.028113         0.028113   \n",
       "number_of_subjects              0.009735        -0.009735   \n",
       "family_size                    -0.035567         0.035567   \n",
       "internet_facility              -0.116004         0.116004   \n",
       "study_time_hours                0.052150        -0.052150   \n",
       "sleep_time_hours               -0.006036         0.006036   \n",
       "socialmedia_hours              -0.007628         0.007628   \n",
       "prepandemic_grades              0.003973        -0.003973   \n",
       "online_interaction             -0.035246         0.035246   \n",
       "online_doubts                   0.027059        -0.027059   \n",
       "performance_in_online          -0.003690         0.003690   \n",
       "satisfaction_level              0.056721        -0.056721   \n",
       "gender__Female                  0.210550        -0.210550   \n",
       "gender__Male                   -0.210550         0.210550   \n",
       "home_location__Rural            0.082417        -0.082417   \n",
       "home_location__Urban           -0.082417         0.082417   \n",
       "education_Post Graduate         0.028087        -0.028087   \n",
       "education_School                0.016083        -0.016083   \n",
       "education_Under Graduate       -0.033794         0.033794   \n",
       "device_Desktop                 -0.021652         0.021652   \n",
       "device_Laptop                  -0.061216         0.061216   \n",
       "device_Mobile                   0.069682        -0.069682   \n",
       "status_Middle Class             0.023182        -0.023182   \n",
       "status_Poor                     0.069445        -0.069445   \n",
       "status_Rich                    -0.123512         0.123512   \n",
       "does_sports_No                  1.000000        -1.000000   \n",
       "does_sports_Yes                -1.000000         1.000000   \n",
       "elderly_monitored_No            0.071641        -0.071641   \n",
       "elderly_monitored_Yes          -0.071641         0.071641   \n",
       "gaming_interest_No              0.126158        -0.126158   \n",
       "gaming_interest_Yes            -0.126158         0.126158   \n",
       "study_room_No                   0.077050        -0.077050   \n",
       "study_room_Yes                 -0.077050         0.077050   \n",
       "group_study_No                  0.094760        -0.094760   \n",
       "group_study_yes                -0.094760         0.094760   \n",
       "interests_Both                 -0.016758         0.016758   \n",
       "interests_Practical            -0.060093         0.060093   \n",
       "interests_Theory                0.088384        -0.088384   \n",
       "\n",
       "                          elderly_monitored_No  elderly_monitored_Yes  \\\n",
       "ageyears                              0.037569              -0.037569   \n",
       "number_of_subjects                   -0.002994               0.002994   \n",
       "family_size                          -0.110653               0.110653   \n",
       "internet_facility                    -0.036147               0.036147   \n",
       "study_time_hours                     -0.083058               0.083058   \n",
       "sleep_time_hours                      0.055412              -0.055412   \n",
       "socialmedia_hours                     0.045698              -0.045698   \n",
       "prepandemic_grades                   -0.004313               0.004313   \n",
       "online_interaction                   -0.104525               0.104525   \n",
       "online_doubts                        -0.112148               0.112148   \n",
       "performance_in_online                -0.092615               0.092615   \n",
       "satisfaction_level                   -0.057379               0.057379   \n",
       "gender__Female                       -0.026932               0.026932   \n",
       "gender__Male                          0.026932              -0.026932   \n",
       "home_location__Rural                  0.020355              -0.020355   \n",
       "home_location__Urban                 -0.020355               0.020355   \n",
       "education_Post Graduate               0.012389              -0.012389   \n",
       "education_School                     -0.014304               0.014304   \n",
       "education_Under Graduate             -0.000234               0.000234   \n",
       "device_Desktop                       -0.040564               0.040564   \n",
       "device_Laptop                        -0.022714               0.022714   \n",
       "device_Mobile                         0.036809              -0.036809   \n",
       "status_Middle Class                  -0.045708               0.045708   \n",
       "status_Poor                           0.049526              -0.049526   \n",
       "status_Rich                           0.009827              -0.009827   \n",
       "does_sports_No                        0.071641              -0.071641   \n",
       "does_sports_Yes                      -0.071641               0.071641   \n",
       "elderly_monitored_No                  1.000000              -1.000000   \n",
       "elderly_monitored_Yes                -1.000000               1.000000   \n",
       "gaming_interest_No                    0.034529              -0.034529   \n",
       "gaming_interest_Yes                  -0.034529               0.034529   \n",
       "study_room_No                         0.047137              -0.047137   \n",
       "study_room_Yes                       -0.047137               0.047137   \n",
       "group_study_No                        0.082916              -0.082916   \n",
       "group_study_yes                      -0.082916               0.082916   \n",
       "interests_Both                        0.048648              -0.048648   \n",
       "interests_Practical                  -0.050067               0.050067   \n",
       "interests_Theory                      0.004112              -0.004112   \n",
       "\n",
       "                          gaming_interest_No  gaming_interest_Yes  \\\n",
       "ageyears                            0.002852            -0.002852   \n",
       "number_of_subjects                 -0.011908             0.011908   \n",
       "family_size                         0.003599            -0.003599   \n",
       "internet_facility                   0.056866            -0.056866   \n",
       "study_time_hours                    0.036329            -0.036329   \n",
       "sleep_time_hours                   -0.053235             0.053235   \n",
       "socialmedia_hours                  -0.074618             0.074618   \n",
       "prepandemic_grades                  0.100308            -0.100308   \n",
       "online_interaction                 -0.024617             0.024617   \n",
       "online_doubts                       0.012137            -0.012137   \n",
       "performance_in_online              -0.022102             0.022102   \n",
       "satisfaction_level                 -0.006052             0.006052   \n",
       "gender__Female                      0.221130            -0.221130   \n",
       "gender__Male                       -0.221130             0.221130   \n",
       "home_location__Rural               -0.076592             0.076592   \n",
       "home_location__Urban                0.076592            -0.076592   \n",
       "education_Post Graduate            -0.031483             0.031483   \n",
       "education_School                    0.000431            -0.000431   \n",
       "education_Under Graduate            0.025224            -0.025224   \n",
       "device_Desktop                     -0.048980             0.048980   \n",
       "device_Laptop                       0.098547            -0.098547   \n",
       "device_Mobile                      -0.083947             0.083947   \n",
       "status_Middle Class                -0.038323             0.038323   \n",
       "status_Poor                        -0.018849             0.018849   \n",
       "status_Rich                         0.083912            -0.083912   \n",
       "does_sports_No                      0.126158            -0.126158   \n",
       "does_sports_Yes                    -0.126158             0.126158   \n",
       "elderly_monitored_No                0.034529            -0.034529   \n",
       "elderly_monitored_Yes              -0.034529             0.034529   \n",
       "gaming_interest_No                  1.000000            -1.000000   \n",
       "gaming_interest_Yes                -1.000000             1.000000   \n",
       "study_room_No                      -0.011165             0.011165   \n",
       "study_room_Yes                      0.011165            -0.011165   \n",
       "group_study_No                      0.066180            -0.066180   \n",
       "group_study_yes                    -0.066180             0.066180   \n",
       "interests_Both                      0.008952            -0.008952   \n",
       "interests_Practical                -0.083690             0.083690   \n",
       "interests_Theory                    0.087232            -0.087232   \n",
       "\n",
       "                          study_room_No  study_room_Yes  group_study_No  \\\n",
       "ageyears                      -0.033317        0.033317       -0.105300   \n",
       "number_of_subjects            -0.006871        0.006871        0.059633   \n",
       "family_size                    0.046895       -0.046895       -0.032359   \n",
       "internet_facility             -0.221508        0.221508       -0.042513   \n",
       "study_time_hours              -0.081980        0.081980       -0.014092   \n",
       "sleep_time_hours              -0.069644        0.069644        0.043329   \n",
       "socialmedia_hours             -0.002508        0.002508       -0.056690   \n",
       "prepandemic_grades            -0.082691        0.082691        0.114598   \n",
       "online_interaction            -0.074390        0.074390       -0.103150   \n",
       "online_doubts                 -0.064953        0.064953       -0.112667   \n",
       "performance_in_online         -0.075586        0.075586       -0.104955   \n",
       "satisfaction_level            -0.019232        0.019232       -0.053017   \n",
       "gender__Female                 0.012588       -0.012588       -0.056483   \n",
       "gender__Male                  -0.012588        0.012588        0.056483   \n",
       "home_location__Rural           0.091465       -0.091465        0.032202   \n",
       "home_location__Urban          -0.091465        0.091465       -0.032202   \n",
       "education_Post Graduate        0.042201       -0.042201       -0.071682   \n",
       "education_School              -0.061292        0.061292        0.007280   \n",
       "education_Under Graduate       0.007817       -0.007817        0.053112   \n",
       "device_Desktop                -0.071176        0.071176       -0.019400   \n",
       "device_Laptop                 -0.169502        0.169502        0.004597   \n",
       "device_Mobile                  0.196723       -0.196723        0.001847   \n",
       "status_Middle Class           -0.097750        0.097750       -0.010481   \n",
       "status_Poor                    0.134702       -0.134702        0.021850   \n",
       "status_Rich                   -0.015068        0.015068       -0.010900   \n",
       "does_sports_No                 0.077050       -0.077050        0.094760   \n",
       "does_sports_Yes               -0.077050        0.077050       -0.094760   \n",
       "elderly_monitored_No           0.047137       -0.047137        0.082916   \n",
       "elderly_monitored_Yes         -0.047137        0.047137       -0.082916   \n",
       "gaming_interest_No            -0.011165        0.011165        0.066180   \n",
       "gaming_interest_Yes            0.011165       -0.011165       -0.066180   \n",
       "study_room_No                  1.000000       -1.000000        0.077055   \n",
       "study_room_Yes                -1.000000        1.000000       -0.077055   \n",
       "group_study_No                 0.077055       -0.077055        1.000000   \n",
       "group_study_yes               -0.077055        0.077055       -1.000000   \n",
       "interests_Both                -0.069756        0.069756       -0.024060   \n",
       "interests_Practical            0.054214       -0.054214        0.010071   \n",
       "interests_Theory               0.014512       -0.014512        0.015024   \n",
       "\n",
       "                          group_study_yes  interests_Both  \\\n",
       "ageyears                         0.105300        0.090857   \n",
       "number_of_subjects              -0.059633        0.026852   \n",
       "family_size                      0.032359       -0.085559   \n",
       "internet_facility                0.042513        0.095404   \n",
       "study_time_hours                 0.014092        0.028659   \n",
       "sleep_time_hours                -0.043329       -0.025122   \n",
       "socialmedia_hours                0.056690       -0.019810   \n",
       "prepandemic_grades              -0.114598        0.133596   \n",
       "online_interaction               0.103150       -0.015708   \n",
       "online_doubts                    0.112667       -0.007137   \n",
       "performance_in_online            0.104955        0.028967   \n",
       "satisfaction_level               0.053017       -0.053972   \n",
       "gender__Female                   0.056483       -0.096409   \n",
       "gender__Male                    -0.056483        0.096409   \n",
       "home_location__Rural            -0.032202       -0.137686   \n",
       "home_location__Urban             0.032202        0.137686   \n",
       "education_Post Graduate          0.071682       -0.067786   \n",
       "education_School                -0.007280       -0.002510   \n",
       "education_Under Graduate        -0.053112        0.056667   \n",
       "device_Desktop                   0.019400        0.042397   \n",
       "device_Laptop                   -0.004597        0.102789   \n",
       "device_Mobile                   -0.001847       -0.119039   \n",
       "status_Middle Class              0.010481        0.002365   \n",
       "status_Poor                     -0.021850       -0.031115   \n",
       "status_Rich                      0.010900        0.035279   \n",
       "does_sports_No                  -0.094760       -0.016758   \n",
       "does_sports_Yes                  0.094760        0.016758   \n",
       "elderly_monitored_No            -0.082916        0.048648   \n",
       "elderly_monitored_Yes            0.082916       -0.048648   \n",
       "gaming_interest_No              -0.066180        0.008952   \n",
       "gaming_interest_Yes              0.066180       -0.008952   \n",
       "study_room_No                   -0.077055       -0.069756   \n",
       "study_room_Yes                   0.077055        0.069756   \n",
       "group_study_No                  -1.000000       -0.024060   \n",
       "group_study_yes                  1.000000        0.024060   \n",
       "interests_Both                   0.024060        1.000000   \n",
       "interests_Practical             -0.010071       -0.613198   \n",
       "interests_Theory                -0.015024       -0.398451   \n",
       "\n",
       "                          interests_Practical  interests_Theory  \n",
       "ageyears                             0.021785         -0.126185  \n",
       "number_of_subjects                  -0.001452         -0.028132  \n",
       "family_size                          0.083992         -0.002516  \n",
       "internet_facility                   -0.086147         -0.005914  \n",
       "study_time_hours                    -0.031987          0.005316  \n",
       "sleep_time_hours                     0.018234          0.006726  \n",
       "socialmedia_hours                    0.005697          0.015383  \n",
       "prepandemic_grades                  -0.024696         -0.119676  \n",
       "online_interaction                  -0.068462          0.096934  \n",
       "online_doubts                       -0.068703          0.087697  \n",
       "performance_in_online               -0.073326          0.052974  \n",
       "satisfaction_level                  -0.096807          0.172336  \n",
       "gender__Female                      -0.021512          0.132033  \n",
       "gender__Male                         0.021512         -0.132033  \n",
       "home_location__Rural                 0.064030          0.078547  \n",
       "home_location__Urban                -0.064030         -0.078547  \n",
       "education_Post Graduate              0.078015         -0.015312  \n",
       "education_School                    -0.006871          0.010765  \n",
       "education_Under Graduate            -0.058527          0.005031  \n",
       "device_Desktop                      -0.025703         -0.017236  \n",
       "device_Laptop                       -0.000590         -0.113456  \n",
       "device_Mobile                        0.009256          0.121438  \n",
       "status_Middle Class                  0.008527         -0.012527  \n",
       "status_Poor                          0.005624          0.028021  \n",
       "status_Rich                         -0.020463         -0.015416  \n",
       "does_sports_No                      -0.060093          0.088384  \n",
       "does_sports_Yes                      0.060093         -0.088384  \n",
       "elderly_monitored_No                -0.050067          0.004112  \n",
       "elderly_monitored_Yes                0.050067         -0.004112  \n",
       "gaming_interest_No                  -0.083690          0.087232  \n",
       "gaming_interest_Yes                  0.083690         -0.087232  \n",
       "study_room_No                        0.054214          0.014512  \n",
       "study_room_Yes                      -0.054214         -0.014512  \n",
       "group_study_No                       0.010071          0.015024  \n",
       "group_study_yes                     -0.010071         -0.015024  \n",
       "interests_Both                      -0.613198         -0.398451  \n",
       "interests_Practical                  1.000000         -0.480185  \n",
       "interests_Theory                    -0.480185          1.000000  \n",
       "\n",
       "[38 rows x 38 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method ='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8e9aa7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes:title={'center':'Correlation Matrix'}>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "29b2d53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8ac5a4bd3c64513941ca1df2a11375f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fe5859fcd9a456f9534fa5d3e50798e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f29febc17a0e4f3db9bd03aabab15ae1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b81ba132bd44bbea18d983a38ad1454",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54fd0456728d4b6ebfa8a431b5c19b00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render JSON:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bbe8f0ac38a40e18dac1ba0576f08b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile = ProfileReport(df, title=\"Pandas Profiling IMA\")\n",
    "\n",
    "profile.to_file(\"eda_PMA.html\")\n",
    "\n",
    "profile.to_file(\"eda_PMA.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a2d1991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(19, 15))\n",
    "plt.matshow(df.corr(), fignum=f.number)\n",
    "plt.xticks(range(df.select_dtypes(['number']).shape[1]), df.select_dtypes(['number']).columns, fontsize=14, rotation=45)\n",
    "plt.yticks(range(df.select_dtypes(['number']).shape[1]), df.select_dtypes(['number']).columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fa5abba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## getting column names\n",
    "x_columns = list(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7615d4c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     satisfaction_level   R-squared:                       0.394\n",
      "Model:                            OLS   Adj. R-squared:                  0.379\n",
      "Method:                 Least Squares   F-statistic:                     26.06\n",
      "Date:                Mon, 14 Feb 2022   Prob (F-statistic):           2.48e-91\n",
      "Time:                        16:34:09   Log-Likelihood:                -816.12\n",
      "No. Observations:                1026   AIC:                             1684.\n",
      "Df Residuals:                    1000   BIC:                             1813.\n",
      "Df Model:                          25                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "gender                      -0.0191      0.038     -0.506      0.613      -0.093       0.055\n",
      "home_location               -0.0424      0.040     -1.066      0.287      -0.120       0.036\n",
      "ageyears                     0.0093      0.006      1.474      0.141      -0.003       0.022\n",
      "number_of_subjects          -0.0005      0.006     -0.079      0.937      -0.013       0.012\n",
      "family_size                  0.0104      0.014      0.742      0.458      -0.017       0.038\n",
      "internet_facility            0.0207      0.019      1.070      0.285      -0.017       0.059\n",
      "does_sports                 -0.0630      0.037     -1.682      0.093      -0.137       0.010\n",
      "elderly_monitor             -0.0081      0.035     -0.231      0.818      -0.077       0.061\n",
      "study_time_hours             0.0002      0.008      0.026      0.980      -0.016       0.017\n",
      "sleep_time_hours            -0.0087      0.013     -0.656      0.512      -0.035       0.017\n",
      "socialmedia_hours           -0.0087      0.010     -0.899      0.369      -0.028       0.010\n",
      "gaming_interest             -0.0064      0.036     -0.177      0.859      -0.077       0.064\n",
      "study_room                  -0.0066      0.036     -0.180      0.858      -0.078       0.065\n",
      "group_study              -8.323e-17   2.48e-17     -3.362      0.001   -1.32e-16   -3.46e-17\n",
      "prepandemic_grades          -0.0810      0.014     -5.713      0.000      -0.109      -0.053\n",
      "online_interaction           0.1291      0.024      5.475      0.000       0.083       0.175\n",
      "online_doubts                0.1231      0.022      5.520      0.000       0.079       0.167\n",
      "performance_in_online        0.0790      0.011      6.929      0.000       0.057       0.101\n",
      "education_Post Graduate      0.2446      0.081      3.006      0.003       0.085       0.404\n",
      "education_School             0.4396      0.073      6.051      0.000       0.297       0.582\n",
      "education_Under Graduate     0.3003      0.062      4.816      0.000       0.178       0.423\n",
      "device_Desktop               0.3411      0.101      3.392      0.001       0.144       0.538\n",
      "device_Laptop                0.2675      0.071      3.751      0.000       0.128       0.407\n",
      "device_Mobile                0.3759      0.069      5.455      0.000       0.241       0.511\n",
      "status_Middle Class          0.3585      0.067      5.384      0.000       0.228       0.489\n",
      "status_Poor                  0.3288      0.087      3.797      0.000       0.159       0.499\n",
      "status_Rich                  0.2972      0.105      2.840      0.005       0.092       0.502\n",
      "interests_Both               0.2862      0.069      4.128      0.000       0.150       0.422\n",
      "interests_Practical          0.2717      0.065      4.166      0.000       0.144       0.400\n",
      "interests_Theory             0.4267      0.063      6.789      0.000       0.303       0.550\n",
      "==============================================================================\n",
      "Omnibus:                        1.656   Durbin-Watson:                   2.075\n",
      "Prob(Omnibus):                  0.437   Jarque-Bera (JB):                1.731\n",
      "Skew:                           0.085   Prob(JB):                        0.421\n",
      "Kurtosis:                       2.893   Cond. No.                     1.31e+16\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 4.12e-27. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "## creating function to get model statistics\n",
    "import statsmodels.api as sm\n",
    "def get_stats():\n",
    "    x = df[x_columns]\n",
    "    results = sm.OLS(y, x).fit()\n",
    "    print(results.summary())\n",
    "get_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e8fe855e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     satisfaction_level   R-squared:                       0.394\n",
      "Model:                            OLS   Adj. R-squared:                  0.380\n",
      "Method:                 Least Squares   F-statistic:                     27.17\n",
      "Date:                Mon, 14 Feb 2022   Prob (F-statistic):           4.71e-92\n",
      "Time:                        16:36:59   Log-Likelihood:                -816.12\n",
      "No. Observations:                1026   AIC:                             1682.\n",
      "Df Residuals:                    1001   BIC:                             1806.\n",
      "Df Model:                          24                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "gender                      -0.0191      0.038     -0.509      0.611      -0.093       0.055\n",
      "home_location               -0.0424      0.040     -1.069      0.285      -0.120       0.035\n",
      "ageyears                     0.0093      0.006      1.475      0.141      -0.003       0.022\n",
      "number_of_subjects          -0.0005      0.006     -0.078      0.938      -0.013       0.012\n",
      "family_size                  0.0105      0.014      0.746      0.456      -0.017       0.038\n",
      "internet_facility            0.0207      0.019      1.071      0.284      -0.017       0.059\n",
      "does_sports                 -0.0630      0.037     -1.685      0.092      -0.136       0.010\n",
      "elderly_monitor             -0.0080      0.035     -0.230      0.818      -0.077       0.060\n",
      "sleep_time_hours            -0.0087      0.013     -0.658      0.511      -0.035       0.017\n",
      "socialmedia_hours           -0.0087      0.010     -0.904      0.366      -0.028       0.010\n",
      "gaming_interest             -0.0064      0.036     -0.178      0.859      -0.077       0.064\n",
      "study_room                  -0.0065      0.036     -0.178      0.859      -0.078       0.065\n",
      "group_study               6.104e-17   4.66e-17      1.310      0.190   -3.04e-17    1.52e-16\n",
      "prepandemic_grades          -0.0810      0.014     -5.740      0.000      -0.109      -0.053\n",
      "online_interaction           0.1291      0.024      5.480      0.000       0.083       0.175\n",
      "online_doubts                0.1231      0.022      5.542      0.000       0.080       0.167\n",
      "performance_in_online        0.0790      0.011      6.938      0.000       0.057       0.101\n",
      "education_Post Graduate      0.2447      0.081      3.012      0.003       0.085       0.404\n",
      "education_School             0.4396      0.073      6.057      0.000       0.297       0.582\n",
      "education_Under Graduate     0.3005      0.062      4.843      0.000       0.179       0.422\n",
      "device_Desktop               0.3412      0.100      3.397      0.001       0.144       0.538\n",
      "device_Laptop                0.2677      0.071      3.764      0.000       0.128       0.407\n",
      "device_Mobile                0.3760      0.069      5.464      0.000       0.241       0.511\n",
      "status_Middle Class          0.3586      0.066      5.404      0.000       0.228       0.489\n",
      "status_Poor                  0.3290      0.086      3.810      0.000       0.160       0.498\n",
      "status_Rich                  0.2972      0.105      2.842      0.005       0.092       0.502\n",
      "interests_Both               0.2863      0.069      4.141      0.000       0.151       0.422\n",
      "interests_Practical          0.2717      0.065      4.175      0.000       0.144       0.399\n",
      "interests_Theory             0.4268      0.063      6.806      0.000       0.304       0.550\n",
      "==============================================================================\n",
      "Omnibus:                        1.656   Durbin-Watson:                   2.075\n",
      "Prob(Omnibus):                  0.437   Jarque-Bera (JB):                1.731\n",
      "Skew:                           0.085   Prob(JB):                        0.421\n",
      "Kurtosis:                       2.893   Cond. No.                     1.31e+16\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 4.01e-27. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "x_columns.remove(\"study_time_hours\")\n",
    "get_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "15868c77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     satisfaction_level   R-squared:                       0.388\n",
      "Model:                            OLS   Adj. R-squared:                  0.381\n",
      "Method:                 Least Squares   F-statistic:                     53.58\n",
      "Date:                Mon, 14 Feb 2022   Prob (F-statistic):           1.98e-99\n",
      "Time:                        16:47:14   Log-Likelihood:                -821.35\n",
      "No. Observations:                1026   AIC:                             1669.\n",
      "Df Residuals:                    1013   BIC:                             1733.\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "prepandemic_grades          -0.0816      0.014     -6.035      0.000      -0.108      -0.055\n",
      "online_interaction           0.1320      0.023      5.700      0.000       0.087       0.177\n",
      "online_doubts                0.1274      0.022      5.786      0.000       0.084       0.171\n",
      "performance_in_online        0.0817      0.011      7.329      0.000       0.060       0.104\n",
      "education_Post Graduate      0.3008      0.051      5.951      0.000       0.202       0.400\n",
      "education_School             0.4460      0.058      7.650      0.000       0.332       0.560\n",
      "education_Under Graduate     0.3222      0.044      7.355      0.000       0.236       0.408\n",
      "device_Desktop               0.3640      0.088      4.152      0.000       0.192       0.536\n",
      "device_Laptop                0.3004      0.048      6.199      0.000       0.205       0.396\n",
      "device_Mobile                0.4046      0.048      8.457      0.000       0.311       0.498\n",
      "status_Middle Class          0.3843      0.046      8.435      0.000       0.295       0.474\n",
      "status_Poor                  0.3695      0.072      5.116      0.000       0.228       0.511\n",
      "status_Rich                  0.3152      0.087      3.630      0.000       0.145       0.486\n",
      "interests_Both               0.3151      0.045      7.071      0.000       0.228       0.403\n",
      "interests_Practical          0.2978      0.041      7.278      0.000       0.217       0.378\n",
      "interests_Theory             0.4562      0.044     10.433      0.000       0.370       0.542\n",
      "==============================================================================\n",
      "Omnibus:                        1.962   Durbin-Watson:                   2.078\n",
      "Prob(Omnibus):                  0.375   Jarque-Bera (JB):                2.024\n",
      "Skew:                           0.088   Prob(JB):                        0.363\n",
      "Kurtosis:                       2.871   Cond. No.                     1.65e+17\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 5.09e-30. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "x_columns.remove(\"gender\")\n",
    "x_columns.remove(\"ageyears\")\n",
    "x_columns.remove(\"home_location\")\n",
    "x_columns.remove(\"family_size\")\n",
    "x_columns.remove(\"internet_facility\")\n",
    "x_columns.remove(\"does_sports\")\n",
    "x_columns.remove(\"elderly_monitor\")\n",
    "x_columns.remove(\"sleep_time_hours\")\n",
    "x_columns.remove(\"gaming_interest\")\n",
    "x_columns.remove(\"socialmedia_hours\")\n",
    "x_columns.remove(\"study_room\")\n",
    "x_columns.remove(\"group_study\")\n",
    "\n",
    "get_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f2bcda70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['prepandemic_grades',\n",
       " 'online_interaction',\n",
       " 'online_doubts',\n",
       " 'performance_in_online',\n",
       " 'education_Post Graduate',\n",
       " 'education_School',\n",
       " 'education_Under Graduate',\n",
       " 'device_Desktop',\n",
       " 'device_Laptop',\n",
       " 'device_Mobile',\n",
       " 'status_Middle Class',\n",
       " 'status_Poor',\n",
       " 'status_Rich',\n",
       " 'interests_Both',\n",
       " 'interests_Practical',\n",
       " 'interests_Theory']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e79ddb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender',\n",
       " 'home_location',\n",
       " 'ageyears',\n",
       " 'number_of_subjects',\n",
       " 'family_size',\n",
       " 'internet_facility',\n",
       " 'does_sports',\n",
       " 'elderly_monitor',\n",
       " 'study_time_hours',\n",
       " 'sleep_time_hours',\n",
       " 'socialmedia_hours',\n",
       " 'gaming_interest',\n",
       " 'study_room',\n",
       " 'group_study',\n",
       " 'prepandemic_grades',\n",
       " 'online_interaction',\n",
       " 'online_doubts',\n",
       " 'performance_in_online',\n",
       " 'satisfaction_level',\n",
       " 'education_Post Graduate',\n",
       " 'education_School',\n",
       " 'education_Under Graduate',\n",
       " 'device_Desktop',\n",
       " 'device_Laptop',\n",
       " 'device_Mobile',\n",
       " 'status_Middle Class',\n",
       " 'status_Poor',\n",
       " 'status_Rich',\n",
       " 'interests_Both',\n",
       " 'interests_Practical',\n",
       " 'interests_Theory']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fd40ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prepandemic_grades</th>\n",
       "      <th>online_interaction</th>\n",
       "      <th>online_doubts</th>\n",
       "      <th>performance_in_online</th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>education_Post Graduate</th>\n",
       "      <th>education_School</th>\n",
       "      <th>education_Under Graduate</th>\n",
       "      <th>device_Desktop</th>\n",
       "      <th>device_Laptop</th>\n",
       "      <th>device_Mobile</th>\n",
       "      <th>status_Middle Class</th>\n",
       "      <th>status_Poor</th>\n",
       "      <th>status_Rich</th>\n",
       "      <th>interests_Both</th>\n",
       "      <th>interests_Practical</th>\n",
       "      <th>interests_Theory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>9</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prepandemic_grades  online_interaction  online_doubts  \\\n",
       "0                     10                   1              1   \n",
       "1                     10                   1              1   \n",
       "2                      8                   1              1   \n",
       "3                     10                   1              2   \n",
       "4                      9                   3              3   \n",
       "...                  ...                 ...            ...   \n",
       "1028                  10                   4              4   \n",
       "1029                   8                   5              5   \n",
       "1030                   8                   2              2   \n",
       "1031                   7                   3              3   \n",
       "1032                   9                   5              4   \n",
       "\n",
       "      performance_in_online satisfaction_level  education_Post Graduate  \\\n",
       "0                         6            Average                        0   \n",
       "1                         3                Bad                        0   \n",
       "2                         6                Bad                        0   \n",
       "3                         4                Bad                        0   \n",
       "4                         6            Average                        0   \n",
       "...                     ...                ...                      ...   \n",
       "1028                      8            Average                        0   \n",
       "1029                     10            Average                        0   \n",
       "1030                      6            Average                        0   \n",
       "1031                      6               Good                        0   \n",
       "1032                     10            Average                        0   \n",
       "\n",
       "      education_School  education_Under Graduate  device_Desktop  \\\n",
       "0                    0                         1               0   \n",
       "1                    0                         1               0   \n",
       "2                    0                         1               0   \n",
       "3                    0                         1               0   \n",
       "4                    0                         1               0   \n",
       "...                ...                       ...             ...   \n",
       "1028                 0                         1               0   \n",
       "1029                 0                         1               1   \n",
       "1030                 0                         1               0   \n",
       "1031                 0                         1               0   \n",
       "1032                 0                         1               0   \n",
       "\n",
       "      device_Laptop  device_Mobile  status_Middle Class  status_Poor  \\\n",
       "0                 1              0                    1            0   \n",
       "1                 1              0                    1            0   \n",
       "2                 1              0                    1            0   \n",
       "3                 1              0                    1            0   \n",
       "4                 1              0                    1            0   \n",
       "...             ...            ...                  ...          ...   \n",
       "1028              0              1                    1            0   \n",
       "1029              0              0                    1            0   \n",
       "1030              0              1                    1            0   \n",
       "1031              0              1                    1            0   \n",
       "1032              1              0                    1            0   \n",
       "\n",
       "      status_Rich  interests_Both  interests_Practical  interests_Theory  \n",
       "0               0               0                    1                 0  \n",
       "1               0               0                    0                 1  \n",
       "2               0               1                    0                 0  \n",
       "3               0               0                    0                 1  \n",
       "4               0               1                    0                 0  \n",
       "...           ...             ...                  ...               ...  \n",
       "1028            0               0                    0                 1  \n",
       "1029            0               0                    0                 1  \n",
       "1030            0               0                    0                 1  \n",
       "1031            0               1                    0                 0  \n",
       "1032            0               0                    1                 0  \n",
       "\n",
       "[1026 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aa4a648b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reduced = df.drop(['education_Post Graduate','education_School','education_Under Graduate','device_Desktop','device_Mobile','status_Middle Class','status_Poor','status_Rich','interests_Both','interests_Practical','interests_Theory'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9ccaa483",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prepandemic_grades  online_interaction  online_doubts  \\\n",
       "0                     10                   1              1   \n",
       "1                     10                   1              1   \n",
       "2                      8                   1              1   \n",
       "3                     10                   1              2   \n",
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       "...                  ...                 ...            ...   \n",
       "1028                  10                   4              4   \n",
       "1029                   8                   5              5   \n",
       "1030                   8                   2              2   \n",
       "1031                   7                   3              3   \n",
       "1032                   9                   5              4   \n",
       "\n",
       "      performance_in_online  satisfaction_level  device_Laptop  \n",
       "0                         6                   2              1  \n",
       "1                         3                   1              1  \n",
       "2                         6                   1              1  \n",
       "3                         4                   1              1  \n",
       "4                         6                   2              1  \n",
       "...                     ...                 ...            ...  \n",
       "1028                      8                   2              0  \n",
       "1029                     10                   2              0  \n",
       "1030                      6                   2              0  \n",
       "1031                      6                   3              0  \n",
       "1032                     10                   2              1  \n",
       "\n",
       "[1026 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72358edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>1026 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prepandemic_grades  online_interaction  online_doubts  \\\n",
       "0                     10                   1              1   \n",
       "1                     10                   1              1   \n",
       "2                      8                   1              1   \n",
       "3                     10                   1              2   \n",
       "4                      9                   3              3   \n",
       "...                  ...                 ...            ...   \n",
       "1028                  10                   4              4   \n",
       "1029                   8                   5              5   \n",
       "1030                   8                   2              2   \n",
       "1031                   7                   3              3   \n",
       "1032                   9                   5              4   \n",
       "\n",
       "      performance_in_online  device_Laptop  \n",
       "0                         6              1  \n",
       "1                         3              1  \n",
       "2                         6              1  \n",
       "3                         4              1  \n",
       "4                         6              1  \n",
       "...                     ...            ...  \n",
       "1028                      8              0  \n",
       "1029                     10              0  \n",
       "1030                      6              0  \n",
       "1031                      6              0  \n",
       "1032                     10              1  \n",
       "\n",
       "[1026 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bdef868b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.model_selection import StratifiedKFold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "81176961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = SVR(kernel=\"linear\")\n",
    "selector = RFECV(estimator, step=1, cv=5)\n",
    "selector = selector.fit(X_train_scaled, y_train)\n",
    "selector.support_\n",
    "\n",
    "\n",
    "selector.ranking_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f306f50",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Classification metrics can't handle a mix of multiclass and continuous targets",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14900/21604297.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mmin_features_to_select\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmin_features_to_select\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m )\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mrfecv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\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     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Optimal number of features : %d\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mrfecv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_features_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_rfe.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups)\u001b[0m\n\u001b[0;32m    603\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdelayed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_rfe_single_fit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    604\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 605\u001b[1;33m         scores = parallel(\n\u001b[0m\u001b[0;32m    606\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m             for train, test in cv.split(X, y, groups))\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_rfe.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    604\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    605\u001b[0m         scores = parallel(\n\u001b[1;32m--> 606\u001b[1;33m             \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\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    607\u001b[0m             for train, test in cv.split(X, y, groups))\n\u001b[0;32m    608\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_rfe.py\u001b[0m in \u001b[0;36m_rfe_single_fit\u001b[1;34m(rfe, estimator, X, y, train, test, scorer)\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_safe_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_safe_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m     return rfe._fit(\n\u001b[0m\u001b[0;32m     38\u001b[0m         \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m         lambda estimator, features: _score(\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_rfe.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, step_score)\u001b[0m\n\u001b[0;32m    257\u001b[0m             \u001b[1;31m# that have not been eliminated yet\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstep_score\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 259\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeatures\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[0;32m    260\u001b[0m             \u001b[0msupport_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mranks\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m             \u001b[0mranking_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogical_not\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msupport_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_rfe.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(estimator, features)\u001b[0m\n\u001b[0;32m     37\u001b[0m     return rfe._fit(\n\u001b[0;32m     38\u001b[0m         \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         lambda estimator, features: _score(\n\u001b[0m\u001b[0;32m     40\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         )).scores_\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(estimator, X_test, y_test, scorer, error_score)\u001b[0m\n\u001b[0;32m    685\u001b[0m             \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    686\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 687\u001b[1;33m             \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\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    688\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    689\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0merror_score\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'raise'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    197\u001b[0m             \u001b[0mScore\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0mto\u001b[0m \u001b[0mprediction\u001b[0m \u001b[0mof\u001b[0m \u001b[0mestimator\u001b[0m \u001b[0mon\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[0;32m    198\u001b[0m         \"\"\"\n\u001b[1;32m--> 199\u001b[1;33m         return self._score(partial(_cached_call, None), estimator, X, y_true,\n\u001b[0m\u001b[0;32m    200\u001b[0m                            sample_weight=sample_weight)\n\u001b[0;32m    201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[1;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[0;32m    240\u001b[0m                                                  **self._kwargs)\n\u001b[0;32m    241\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 242\u001b[1;33m             return self._sign * self._score_func(y_true, y_pred,\n\u001b[0m\u001b[0;32m    243\u001b[0m                                                  **self._kwargs)\n\u001b[0;32m    244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[1;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m     \u001b[1;31m# Compute accuracy for each possible representation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 202\u001b[1;33m     \u001b[0my_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\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    203\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'multilabel'\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[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m     90\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0m\u001b[0;32m     93\u001b[0m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0;32m     94\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and continuous targets"
     ]
    }
   ],
   "source": [
    "# Create the RFE object and compute a cross-validated score.\n",
    "svc = SVR(kernel=\"linear\")\n",
    "# The \"accuracy\" scoring shows the proportion of correct classifications\n",
    "\n",
    "min_features_to_select = 1  # Minimum number of features to consider\n",
    "rfecv = RFECV(\n",
    "    estimator=svc,\n",
    "    step=1,\n",
    "    cv=StratifiedKFold(2),\n",
    "    scoring=\"\",\n",
    "    min_features_to_select=min_features_to_select,\n",
    ")\n",
    "rfecv.fit(X, y)\n",
    "\n",
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)\n",
    "\n",
    "# Plot number of features VS. cross-validation scores\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Cross validation score (accuracy)\")\n",
    "plt.plot(\n",
    "    range(min_features_to_select, len(rfecv.grid_scores_) + min_features_to_select),\n",
    "    rfecv.grid_scores_,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1525a57a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     feature       VIF\n",
      "0         prepandemic_grades  1.151279\n",
      "1         online_interaction  2.342573\n",
      "2              online_doubts  2.357017\n",
      "3      performance_in_online  1.669802\n",
      "4         satisfaction_level  1.634749\n",
      "5    education_Post Graduate       inf\n",
      "6           education_School       inf\n",
      "7   education_Under Graduate       inf\n",
      "8             device_Desktop       inf\n",
      "9              device_Laptop       inf\n",
      "10             device_Mobile       inf\n",
      "11       status_Middle Class       inf\n",
      "12               status_Poor       inf\n",
      "13               status_Rich       inf\n",
      "14            interests_Both       inf\n",
      "15       interests_Practical       inf\n",
      "16          interests_Theory       inf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pelgu\\Anaconda3\\lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:193: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  vif = 1. / (1. - r_squared_i)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# the independent variables set\n",
    "X1 = df[list(df)]\n",
    "\n",
    "# VIF dataframe\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"feature\"] = X1.columns\n",
    "\n",
    "# calculating VIF for each feature\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X1.values, i)\n",
    "    for i in range(len(X1.columns))]\n",
    "\n",
    "print(vif_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d0540ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prepandemic_grades</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prepandemic_grades  online_interaction  online_doubts  \\\n",
       "0                     10                   1              1   \n",
       "1                     10                   1              1   \n",
       "2                      8                   1              1   \n",
       "3                     10                   1              2   \n",
       "4                      9                   3              3   \n",
       "...                  ...                 ...            ...   \n",
       "1028                  10                   4              4   \n",
       "1029                   8                   5              5   \n",
       "1030                   8                   2              2   \n",
       "1031                   7                   3              3   \n",
       "1032                   9                   5              4   \n",
       "\n",
       "      performance_in_online  satisfaction_level  education_Post Graduate  \\\n",
       "0                         6                   2                        0   \n",
       "1                         3                   1                        0   \n",
       "2                         6                   1                        0   \n",
       "3                         4                   1                        0   \n",
       "4                         6                   2                        0   \n",
       "...                     ...                 ...                      ...   \n",
       "1028                      8                   2                        0   \n",
       "1029                     10                   2                        0   \n",
       "1030                      6                   2                        0   \n",
       "1031                      6                   3                        0   \n",
       "1032                     10                   2                        0   \n",
       "\n",
       "      education_School  education_Under Graduate  device_Desktop  \\\n",
       "0                    0                         1               0   \n",
       "1                    0                         1               0   \n",
       "2                    0                         1               0   \n",
       "3                    0                         1               0   \n",
       "4                    0                         1               0   \n",
       "...                ...                       ...             ...   \n",
       "1028                 0                         1               0   \n",
       "1029                 0                         1               1   \n",
       "1030                 0                         1               0   \n",
       "1031                 0                         1               0   \n",
       "1032                 0                         1               0   \n",
       "\n",
       "      device_Laptop  device_Mobile  status_Middle Class  status_Poor  \\\n",
       "0                 1              0                    1            0   \n",
       "1                 1              0                    1            0   \n",
       "2                 1              0                    1            0   \n",
       "3                 1              0                    1            0   \n",
       "4                 1              0                    1            0   \n",
       "...             ...            ...                  ...          ...   \n",
       "1028              0              1                    1            0   \n",
       "1029              0              0                    1            0   \n",
       "1030              0              1                    1            0   \n",
       "1031              0              1                    1            0   \n",
       "1032              1              0                    1            0   \n",
       "\n",
       "      status_Rich  interests_Both  interests_Practical  interests_Theory  \n",
       "0               0               0                    1                 0  \n",
       "1               0               0                    0                 1  \n",
       "2               0               1                    0                 0  \n",
       "3               0               0                    0                 1  \n",
       "4               0               1                    0                 0  \n",
       "...           ...             ...                  ...               ...  \n",
       "1028            0               0                    0                 1  \n",
       "1029            0               0                    0                 1  \n",
       "1030            0               0                    0                 1  \n",
       "1031            0               1                    0                 0  \n",
       "1032            0               0                    1                 0  \n",
       "\n",
       "[1026 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a16cc0f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale \n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dfbd0345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'hp')"
      ]
     },
     "execution_count": 49,
     "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": [
    "#define predictor and response variables\n",
    "\n",
    "#scale predictor variables\n",
    "pca = PCA()\n",
    "X_reduced = pca.fit_transform(scale(X_train_scaled))\n",
    "\n",
    "#define cross validation method\n",
    "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "\n",
    "regr = Ridge()\n",
    "mse = []\n",
    "\n",
    "# Calculate MSE with only the intercept\n",
    "score = -1*model_selection.cross_val_score(regr,\n",
    "           np.ones((len(X_reduced),1)), y_train, cv=cv,\n",
    "           scoring='neg_mean_squared_error').mean()    \n",
    "mse.append(score)\n",
    "\n",
    "# Calculate MSE using cross-validation, adding one component at a time\n",
    "for i in np.arange(1, 6):\n",
    "    score = -1*model_selection.cross_val_score(regr,\n",
    "               X_reduced[:,:i], y_train, cv=cv, scoring='neg_mean_squared_error').mean()\n",
    "    mse.append(score)\n",
    "    \n",
    "# Plot cross-validation results    \n",
    "plt.plot(mse)\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('MSE')\n",
    "plt.title('hp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a6bf90cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialFeatureSelector(estimator=SVR(), n_features_to_select=5)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "svr = SVR()\n",
    "sfs = SequentialFeatureSelector(svr, n_features_to_select=5)\n",
    "sfs.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "79b79a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False,  True,\n",
       "       False, False, False,  True, False,  True,  True, False,  True,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs.get_support()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6d932154",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sfs= pd.DataFrame(sfs.transform(X_train_scaled).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b583271a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 39,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 42,
   "id": "c5ad409d",
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   "outputs": [
    {
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   "execution_count": 43,
   "id": "0707462c",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'SequentialFeatureSelector' object has no attribute 'get_feature_names_out'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3672/3666827233.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msfs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_feature_names_out\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'SequentialFeatureSelector' object has no attribute 'get_feature_names_out'"
     ]
    }
   ],
   "source": [
    "sfs.get_feature_names_out(input_features=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c69d34d6",
   "metadata": {},
   "outputs": [
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       "      prepandemic_grades  online_interaction  online_doubts  \\\n",
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       "      performance_in_online  satisfaction_level  education_Post Graduate  \\\n",
       "0                         6                   2                        0   \n",
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       "1030                      6                   2                        0   \n",
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       "\n",
       "      education_School  education_Under Graduate  device_Desktop  \\\n",
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       "...                ...                       ...             ...   \n",
       "1028                 0                         1               0   \n",
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       "1030                 0                         1               0   \n",
       "1031                 0                         1               0   \n",
       "1032                 0                         1               0   \n",
       "\n",
       "      device_Laptop  device_Mobile  status_Middle Class  status_Poor  \\\n",
       "0                 1              0                    1            0   \n",
       "1                 1              0                    1            0   \n",
       "2                 1              0                    1            0   \n",
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       "\n",
       "      status_Rich  interests_Both  interests_Practical  interests_Theory  \n",
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       "2               0               1                    0                 0  \n",
       "3               0               0                    0                 1  \n",
       "4               0               1                    0                 0  \n",
       "...           ...             ...                  ...               ...  \n",
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       "1030            0               0                    0                 1  \n",
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       "1032            0               0                    1                 0  \n",
       "\n",
       "[1026 rows x 17 columns]"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a5a7f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reduced = df.drop(['education_Post Graduate','education_School','education_Under Graduate','device_Desktop','device_Mobile','status_Middle Class','status_Poor','status_Rich','interests_Both','interests_Practical','interests_Theory','online_doubts','prepandemic_grades'],axis=1)"
   ]
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  {
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   "execution_count": 18,
   "id": "22b19130",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  home_location  ageyears  number_of_subjects  family_size  \\\n",
       "0          1              1        18                  11            4   \n",
       "1          1              1        19                   7            4   \n",
       "2          1              0        18                   5            5   \n",
       "3          1              1        18                   5            4   \n",
       "4          1              0        18                   5            4   \n",
       "...      ...            ...       ...                 ...          ...   \n",
       "1028       0              0        19                   3            6   \n",
       "1029       0              0        20                   6            3   \n",
       "1030       0              0        20                   3            3   \n",
       "1031       0              0        19                   6            2   \n",
       "1032       0              1        20                   6            4   \n",
       "\n",
       "      internet_facility  does_sports  elderly_monitor  study_time_hours  \\\n",
       "0                     5            0                1                 3   \n",
       "1                     1            1                1                 7   \n",
       "2                     2            0                1                 6   \n",
       "3                     4            1                1                 3   \n",
       "4                     3            0                0                 8   \n",
       "...                 ...          ...              ...               ...   \n",
       "1028                  5            1                1                 4   \n",
       "1029                  1            0                0                 3   \n",
       "1030                  2            0                0                 3   \n",
       "1031                  3            1                0                 4   \n",
       "1032                  3            0                0                 5   \n",
       "\n",
       "      sleep_time_hours  socialmedia_hours  gaming_interest  study_room  \\\n",
       "0                    6                  1                0           0   \n",
       "1                    5                  1                1           1   \n",
       "2                    7                  1                0           1   \n",
       "3                    6                  2                0           0   \n",
       "4                    7                  2                1           1   \n",
       "...                ...                ...              ...         ...   \n",
       "1028                 8                  4                1           1   \n",
       "1029                 7                  2                1           1   \n",
       "1030                 6                  3                0           1   \n",
       "1031                 6                  2                1           0   \n",
       "1032                 7                  1                1           1   \n",
       "\n",
       "      group_study  online_interaction  performance_in_online  \\\n",
       "0               0                   1                      6   \n",
       "1               0                   1                      3   \n",
       "2               0                   1                      6   \n",
       "3               0                   1                      4   \n",
       "4               0                   3                      6   \n",
       "...           ...                 ...                    ...   \n",
       "1028            0                   4                      8   \n",
       "1029            0                   5                     10   \n",
       "1030            0                   2                      6   \n",
       "1031            0                   3                      6   \n",
       "1032            0                   5                     10   \n",
       "\n",
       "      satisfaction_level  device_Laptop  \n",
       "0                      2              1  \n",
       "1                      1              1  \n",
       "2                      1              1  \n",
       "3                      1              1  \n",
       "4                      2              1  \n",
       "...                  ...            ...  \n",
       "1028                   2              0  \n",
       "1029                   2              0  \n",
       "1030                   2              0  \n",
       "1031                   3              0  \n",
       "1032                   2              1  \n",
       "\n",
       "[1026 rows x 18 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b61a17c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['gender',\n",
    " 'home_location',\n",
    " 'ageyears',\n",
    " 'number_of_subjects',\n",
    " 'family_size',\n",
    " 'internet_facility',\n",
    " 'does_sports',\n",
    " 'elderly_monitor',\n",
    " 'study_time_hours',\n",
    " 'sleep_time_hours',\n",
    " 'socialmedia_hours',\n",
    " 'gaming_interest',\n",
    " 'study_room',\n",
    " 'group_study','education_School','device_Mobile','status_Poor','interests_Both','online_doubts','prepandemic_grades'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23817b3c",
   "metadata": {},
   "outputs": [
    {
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       "<p>1026 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      online_interaction  performance_in_online  satisfaction_level  \\\n",
       "0                      1                      6                   2   \n",
       "1                      1                      3                   1   \n",
       "2                      1                      6                   1   \n",
       "3                      1                      4                   1   \n",
       "4                      3                      6                   2   \n",
       "...                  ...                    ...                 ...   \n",
       "1028                   4                      8                   2   \n",
       "1029                   5                     10                   2   \n",
       "1030                   2                      6                   2   \n",
       "1031                   3                      6                   3   \n",
       "1032                   5                     10                   2   \n",
       "\n",
       "      device_Laptop  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  \n",
       "...             ...  \n",
       "1028              0  \n",
       "1029              0  \n",
       "1030              0  \n",
       "1031              0  \n",
       "1032              1  \n",
       "\n",
       "[1026 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "afd5318b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reduced = df_reduced.drop(['device_Laptop'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b772ca54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>1026 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      online_interaction  performance_in_online  satisfaction_level\n",
       "0                      1                      6                   2\n",
       "1                      1                      3                   1\n",
       "2                      1                      6                   1\n",
       "3                      1                      4                   1\n",
       "4                      3                      6                   2\n",
       "...                  ...                    ...                 ...\n",
       "1028                   4                      8                   2\n",
       "1029                   5                     10                   2\n",
       "1030                   2                      6                   2\n",
       "1031                   3                      6                   3\n",
       "1032                   5                     10                   2\n",
       "\n",
       "[1026 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c8e99b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate target values\n",
    "y = df_reduced['satisfaction_level']\n",
    "# Separate variables and remove ID column\n",
    "X = df_reduced.drop(['satisfaction_level'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e0f2285",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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>1028</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1026 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  home_location  ageyears  number_of_subjects  family_size  \\\n",
       "0          1              1        18                  11            4   \n",
       "1          1              1        19                   7            4   \n",
       "2          1              0        18                   5            5   \n",
       "3          1              1        18                   5            4   \n",
       "4          1              0        18                   5            4   \n",
       "...      ...            ...       ...                 ...          ...   \n",
       "1028       0              0        19                   3            6   \n",
       "1029       0              0        20                   6            3   \n",
       "1030       0              0        20                   3            3   \n",
       "1031       0              0        19                   6            2   \n",
       "1032       0              1        20                   6            4   \n",
       "\n",
       "      internet_facility  does_sports  elderly_monitor  study_time_hours  \\\n",
       "0                     5            0                1                 3   \n",
       "1                     1            1                1                 7   \n",
       "2                     2            0                1                 6   \n",
       "3                     4            1                1                 3   \n",
       "4                     3            0                0                 8   \n",
       "...                 ...          ...              ...               ...   \n",
       "1028                  5            1                1                 4   \n",
       "1029                  1            0                0                 3   \n",
       "1030                  2            0                0                 3   \n",
       "1031                  3            1                0                 4   \n",
       "1032                  3            0                0                 5   \n",
       "\n",
       "      sleep_time_hours  socialmedia_hours  gaming_interest  study_room  \\\n",
       "0                    6                  1                0           0   \n",
       "1                    5                  1                1           1   \n",
       "2                    7                  1                0           1   \n",
       "3                    6                  2                0           0   \n",
       "4                    7                  2                1           1   \n",
       "...                ...                ...              ...         ...   \n",
       "1028                 8                  4                1           1   \n",
       "1029                 7                  2                1           1   \n",
       "1030                 6                  3                0           1   \n",
       "1031                 6                  2                1           0   \n",
       "1032                 7                  1                1           1   \n",
       "\n",
       "      group_study  prepandemic_grades  ...  online_doubts  \\\n",
       "0               0                  10  ...              1   \n",
       "1               0                  10  ...              1   \n",
       "2               0                   8  ...              1   \n",
       "3               0                  10  ...              2   \n",
       "4               0                   9  ...              3   \n",
       "...           ...                 ...  ...            ...   \n",
       "1028            0                  10  ...              4   \n",
       "1029            0                   8  ...              5   \n",
       "1030            0                   8  ...              2   \n",
       "1031            0                   7  ...              3   \n",
       "1032            0                   9  ...              4   \n",
       "\n",
       "      performance_in_online  satisfaction_level  education_Post Graduate  \\\n",
       "0                         6                   2                        0   \n",
       "1                         3                   1                        0   \n",
       "2                         6                   1                        0   \n",
       "3                         4                   1                        0   \n",
       "4                         6                   2                        0   \n",
       "...                     ...                 ...                      ...   \n",
       "1028                      8                   2                        0   \n",
       "1029                     10                   2                        0   \n",
       "1030                      6                   2                        0   \n",
       "1031                      6                   3                        0   \n",
       "1032                     10                   2                        0   \n",
       "\n",
       "      education_School  education_Under Graduate  device_Desktop  \\\n",
       "0                    0                         1               0   \n",
       "1                    0                         1               0   \n",
       "2                    0                         1               0   \n",
       "3                    0                         1               0   \n",
       "4                    0                         1               0   \n",
       "...                ...                       ...             ...   \n",
       "1028                 0                         1               0   \n",
       "1029                 0                         1               1   \n",
       "1030                 0                         1               0   \n",
       "1031                 0                         1               0   \n",
       "1032                 0                         1               0   \n",
       "\n",
       "      device_Laptop  device_Mobile  status_Middle Class  status_Poor  \\\n",
       "0                 1              0                    1            0   \n",
       "1                 1              0                    1            0   \n",
       "2                 1              0                    1            0   \n",
       "3                 1              0                    1            0   \n",
       "4                 1              0                    1            0   \n",
       "...             ...            ...                  ...          ...   \n",
       "1028              0              1                    1            0   \n",
       "1029              0              0                    1            0   \n",
       "1030              0              1                    1            0   \n",
       "1031              0              1                    1            0   \n",
       "1032              1              0                    1            0   \n",
       "\n",
       "      status_Rich  interests_Both  interests_Practical  interests_Theory  \n",
       "0               0               0                    1                 0  \n",
       "1               0               0                    0                 1  \n",
       "2               0               1                    0                 0  \n",
       "3               0               0                    0                 1  \n",
       "4               0               1                    0                 0  \n",
       "...           ...             ...                  ...               ...  \n",
       "1028            0               0                    0                 1  \n",
       "1029            0               0                    0                 1  \n",
       "1030            0               0                    0                 1  \n",
       "1031            0               1                    0                 0  \n",
       "1032            0               0                    1                 0  \n",
       "\n",
       "[1026 rows x 31 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "672210b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d578c25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtr = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ae982558",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 1.0\n"
     ]
    }
   ],
   "source": [
    "dtr.fit(X_train_scaled, y_train)\n",
    "\n",
    "score = dtr.score(X_train_scaled, y_train)\n",
    "print(\"R-squared:\", score) \n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "300ffe7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance (L1 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.0\n",
      "R2 score is 1.0\n",
      "\n",
      "\n",
      "Testing performance (L1 regularisation)\n",
      "--------------------------------------\n",
      "RMSE is 0.7240661880382167\n",
      "R2 score is -0.20233463035019472\n",
      "\n",
      "\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'DecisionTreeRegressor' object has no attribute 'coef_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13584/4231293368.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;31m# print the coefficients\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m \u001b[0mbetas\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlasso_model_fit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DecisionTreeRegressor' object has no attribute 'coef_'"
     ]
    }
   ],
   "source": [
    "\n",
    "# fit a L1 model - alpha is a value between 0 and inf where higher\n",
    "# means more regularisation. Typically we use max = 1\n",
    "lasso_model = DecisionTreeRegressor()\n",
    "\n",
    "# fit the model to the training data\n",
    "lasso_model_fit = lasso_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# predict the training data\n",
    "boston_predict_lasso = lasso_model_fit.predict(X_train_scaled)\n",
    "\n",
    "# calculate RMSE (root mean square error) and R^2 (predictive power)\n",
    "# training set\n",
    "rmse = (np.sqrt(mean_squared_error(y_train, boston_predict_lasso)))\n",
    "r2 = r2_score(y_train, boston_predict_lasso)\n",
    "\n",
    "# print the performance metrics\n",
    "print(\"Training performance (L1 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "# predict the testing data\n",
    "boston_predict_lasso_test = lasso_model_fit.predict(X_test_scaled)\n",
    "\n",
    "# calculate RMSE (root mean square error) and R^2 (predictive power)\n",
    "# testing set\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, boston_predict_lasso_test)))\n",
    "r2 = r2_score(y_test, boston_predict_lasso_test)\n",
    "\n",
    "# print the performance metrics\n",
    "print(\"Testing performance (L1 regularisation)\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "# print the coefficients\n",
    "betas = lasso_model_fit.coef_\n",
    "counter = 0\n",
    "for col in df.columns:\n",
    "    if counter == 0:\n",
    "        print(\"Beta weights/co-efficients (L1 regularisation)\")\n",
    "        print(\"-----------------------------------------\")\n",
    "    print(col + \": \" + str(round(betas[counter], 4)))\n",
    "    counter +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d22fd86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = X_train_scaled.drop(['online_doubts'],axis=1)\n",
    "X_test_scaled = X_test_scaled.drop(['online_doubts'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fb1bc5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        vertical-align: top;\n",
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       "    .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>gender</th>\n",
       "      <th>home_location</th>\n",
       "      <th>ageyears</th>\n",
       "      <th>number_of_subjects</th>\n",
       "      <th>family_size</th>\n",
       "      <th>internet_facility</th>\n",
       "      <th>does_sports</th>\n",
       "      <th>elderly_monitor</th>\n",
       "      <th>study_time_hours</th>\n",
       "      <th>sleep_time_hours</th>\n",
       "      <th>socialmedia_hours</th>\n",
       "      <th>gaming_interest</th>\n",
       "      <th>study_room</th>\n",
       "      <th>group_study</th>\n",
       "      <th>prepandemic_grades</th>\n",
       "      <th>online_interaction</th>\n",
       "      <th>performance_in_online</th>\n",
       "      <th>education_Post Graduate</th>\n",
       "      <th>education_School</th>\n",
       "      <th>education_Under Graduate</th>\n",
       "      <th>device_Desktop</th>\n",
       "      <th>device_Laptop</th>\n",
       "      <th>device_Mobile</th>\n",
       "      <th>status_Middle Class</th>\n",
       "      <th>status_Poor</th>\n",
       "      <th>status_Rich</th>\n",
       "      <th>interests_Both</th>\n",
       "      <th>interests_Practical</th>\n",
       "      <th>interests_Theory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>-0.547545</td>\n",
       "      <td>0.374607</td>\n",
       "      <td>-1.122281</td>\n",
       "      <td>0.391313</td>\n",
       "      <td>1.351882</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>-0.188172</td>\n",
       "      <td>-1.461721</td>\n",
       "      <td>-0.357018</td>\n",
       "      <td>-1.105542</td>\n",
       "      <td>0.856513</td>\n",
       "      <td>-0.814424</td>\n",
       "      <td>0.550065</td>\n",
       "      <td>0.051637</td>\n",
       "      <td>0.684379</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>-0.722036</td>\n",
       "      <td>1.170445</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>-0.547545</td>\n",
       "      <td>0.374607</td>\n",
       "      <td>-0.330310</td>\n",
       "      <td>0.391313</td>\n",
       "      <td>-0.739709</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>-1.120505</td>\n",
       "      <td>-0.707877</td>\n",
       "      <td>-0.888333</td>\n",
       "      <td>-1.105542</td>\n",
       "      <td>-1.167524</td>\n",
       "      <td>-0.814424</td>\n",
       "      <td>1.301821</td>\n",
       "      <td>-1.750179</td>\n",
       "      <td>-1.405716</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>1.384973</td>\n",
       "      <td>-0.854376</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>1.292566</td>\n",
       "      <td>4.354529</td>\n",
       "      <td>-0.330310</td>\n",
       "      <td>0.391313</td>\n",
       "      <td>-0.739709</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>0.744161</td>\n",
       "      <td>-0.707877</td>\n",
       "      <td>-0.888333</td>\n",
       "      <td>0.904534</td>\n",
       "      <td>0.856513</td>\n",
       "      <td>1.227862</td>\n",
       "      <td>-0.201691</td>\n",
       "      <td>0.952545</td>\n",
       "      <td>1.206902</td>\n",
       "      <td>2.653152</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>-1.926822</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>1.384973</td>\n",
       "      <td>-0.854376</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>-0.240860</td>\n",
       "      <td>-0.349015</td>\n",
       "      <td>-0.330310</td>\n",
       "      <td>0.391313</td>\n",
       "      <td>-0.739709</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>-0.188172</td>\n",
       "      <td>0.799809</td>\n",
       "      <td>0.174297</td>\n",
       "      <td>0.904534</td>\n",
       "      <td>-1.167524</td>\n",
       "      <td>-0.814424</td>\n",
       "      <td>-0.201691</td>\n",
       "      <td>1.853453</td>\n",
       "      <td>0.684379</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>-1.359126</td>\n",
       "      <td>1.451227</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>-0.722036</td>\n",
       "      <td>1.170445</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>0.679196</td>\n",
       "      <td>-0.349015</td>\n",
       "      <td>2.837573</td>\n",
       "      <td>1.375597</td>\n",
       "      <td>1.351882</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>-0.188172</td>\n",
       "      <td>0.045966</td>\n",
       "      <td>-0.357018</td>\n",
       "      <td>0.904534</td>\n",
       "      <td>-1.167524</td>\n",
       "      <td>1.227862</td>\n",
       "      <td>-0.201691</td>\n",
       "      <td>0.952545</td>\n",
       "      <td>0.684379</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>-0.722036</td>\n",
       "      <td>-0.854376</td>\n",
       "      <td>1.802416</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",
       "      <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>815</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>1.905937</td>\n",
       "      <td>0.736418</td>\n",
       "      <td>1.253632</td>\n",
       "      <td>0.391313</td>\n",
       "      <td>1.351882</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>-1.586672</td>\n",
       "      <td>-1.461721</td>\n",
       "      <td>3.893503</td>\n",
       "      <td>0.904534</td>\n",
       "      <td>-1.167524</td>\n",
       "      <td>1.227862</td>\n",
       "      <td>-0.953447</td>\n",
       "      <td>-0.849271</td>\n",
       "      <td>-1.405716</td>\n",
       "      <td>2.653152</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>-1.926822</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>-0.722036</td>\n",
       "      <td>1.170445</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>-1.215459</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>-0.547545</td>\n",
       "      <td>-0.710827</td>\n",
       "      <td>1.253632</td>\n",
       "      <td>-1.577255</td>\n",
       "      <td>-0.739709</td>\n",
       "      <td>-1.076055</td>\n",
       "      <td>0.744161</td>\n",
       "      <td>0.045966</td>\n",
       "      <td>-0.888333</td>\n",
       "      <td>-1.105542</td>\n",
       "      <td>-1.167524</td>\n",
       "      <td>-0.814424</td>\n",
       "      <td>1.301821</td>\n",
       "      <td>0.051637</td>\n",
       "      <td>-1.928240</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>-1.359126</td>\n",
       "      <td>1.451227</td>\n",
       "      <td>-3.559026</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>1.384973</td>\n",
       "      <td>-0.854376</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>-1.215459</td>\n",
       "      <td>-1.381232</td>\n",
       "      <td>-0.240860</td>\n",
       "      <td>-0.710827</td>\n",
       "      <td>-0.330310</td>\n",
       "      <td>1.375597</td>\n",
       "      <td>-0.739709</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>-1.586672</td>\n",
       "      <td>0.045966</td>\n",
       "      <td>-0.357018</td>\n",
       "      <td>0.904534</td>\n",
       "      <td>0.856513</td>\n",
       "      <td>-0.814424</td>\n",
       "      <td>-0.953447</td>\n",
       "      <td>1.853453</td>\n",
       "      <td>1.729426</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>-0.722036</td>\n",
       "      <td>1.170445</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>-1.381232</td>\n",
       "      <td>-0.240860</td>\n",
       "      <td>-0.349015</td>\n",
       "      <td>-1.122281</td>\n",
       "      <td>-1.577255</td>\n",
       "      <td>-0.739709</td>\n",
       "      <td>-1.076055</td>\n",
       "      <td>0.277994</td>\n",
       "      <td>-1.461721</td>\n",
       "      <td>-0.888333</td>\n",
       "      <td>-1.105542</td>\n",
       "      <td>-1.167524</td>\n",
       "      <td>-0.814424</td>\n",
       "      <td>0.550065</td>\n",
       "      <td>0.051637</td>\n",
       "      <td>0.684379</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>1.384973</td>\n",
       "      <td>-0.854376</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>0.822734</td>\n",
       "      <td>0.723991</td>\n",
       "      <td>0.372510</td>\n",
       "      <td>-0.349015</td>\n",
       "      <td>-0.330310</td>\n",
       "      <td>0.391313</td>\n",
       "      <td>1.351882</td>\n",
       "      <td>0.929320</td>\n",
       "      <td>-0.654339</td>\n",
       "      <td>0.045966</td>\n",
       "      <td>-0.357018</td>\n",
       "      <td>0.904534</td>\n",
       "      <td>0.856513</td>\n",
       "      <td>1.227862</td>\n",
       "      <td>0.550065</td>\n",
       "      <td>0.952545</td>\n",
       "      <td>0.684379</td>\n",
       "      <td>-0.376910</td>\n",
       "      <td>-0.310253</td>\n",
       "      <td>0.518989</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>0.735767</td>\n",
       "      <td>-0.689072</td>\n",
       "      <td>0.280976</td>\n",
       "      <td>-0.214286</td>\n",
       "      <td>-0.17364</td>\n",
       "      <td>1.384973</td>\n",
       "      <td>-0.854376</td>\n",
       "      <td>-0.554811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender  home_location  ageyears  number_of_subjects  family_size  \\\n",
       "0    0.822734       0.723991 -0.547545            0.374607    -1.122281   \n",
       "1    0.822734       0.723991 -0.547545            0.374607    -0.330310   \n",
       "2    0.822734       0.723991  1.292566            4.354529    -0.330310   \n",
       "3    0.822734       0.723991 -0.240860           -0.349015    -0.330310   \n",
       "4    0.822734       0.723991  0.679196           -0.349015     2.837573   \n",
       "..        ...            ...       ...                 ...          ...   \n",
       "815  0.822734       0.723991  1.905937            0.736418     1.253632   \n",
       "816 -1.215459       0.723991 -0.547545           -0.710827     1.253632   \n",
       "817 -1.215459      -1.381232 -0.240860           -0.710827    -0.330310   \n",
       "818  0.822734      -1.381232 -0.240860           -0.349015    -1.122281   \n",
       "819  0.822734       0.723991  0.372510           -0.349015    -0.330310   \n",
       "\n",
       "     internet_facility  does_sports  elderly_monitor  study_time_hours  \\\n",
       "0             0.391313     1.351882         0.929320         -0.188172   \n",
       "1             0.391313    -0.739709         0.929320         -1.120505   \n",
       "2             0.391313    -0.739709         0.929320          0.744161   \n",
       "3             0.391313    -0.739709         0.929320         -0.188172   \n",
       "4             1.375597     1.351882         0.929320         -0.188172   \n",
       "..                 ...          ...              ...               ...   \n",
       "815           0.391313     1.351882         0.929320         -1.586672   \n",
       "816          -1.577255    -0.739709        -1.076055          0.744161   \n",
       "817           1.375597    -0.739709         0.929320         -1.586672   \n",
       "818          -1.577255    -0.739709        -1.076055          0.277994   \n",
       "819           0.391313     1.351882         0.929320         -0.654339   \n",
       "\n",
       "     sleep_time_hours  socialmedia_hours  gaming_interest  study_room  \\\n",
       "0           -1.461721          -0.357018        -1.105542    0.856513   \n",
       "1           -0.707877          -0.888333        -1.105542   -1.167524   \n",
       "2           -0.707877          -0.888333         0.904534    0.856513   \n",
       "3            0.799809           0.174297         0.904534   -1.167524   \n",
       "4            0.045966          -0.357018         0.904534   -1.167524   \n",
       "..                ...                ...              ...         ...   \n",
       "815         -1.461721           3.893503         0.904534   -1.167524   \n",
       "816          0.045966          -0.888333        -1.105542   -1.167524   \n",
       "817          0.045966          -0.357018         0.904534    0.856513   \n",
       "818         -1.461721          -0.888333        -1.105542   -1.167524   \n",
       "819          0.045966          -0.357018         0.904534    0.856513   \n",
       "\n",
       "     group_study  prepandemic_grades  online_interaction  \\\n",
       "0      -0.814424            0.550065            0.051637   \n",
       "1      -0.814424            1.301821           -1.750179   \n",
       "2       1.227862           -0.201691            0.952545   \n",
       "3      -0.814424           -0.201691            1.853453   \n",
       "4       1.227862           -0.201691            0.952545   \n",
       "..           ...                 ...                 ...   \n",
       "815     1.227862           -0.953447           -0.849271   \n",
       "816    -0.814424            1.301821            0.051637   \n",
       "817    -0.814424           -0.953447            1.853453   \n",
       "818    -0.814424            0.550065            0.051637   \n",
       "819     1.227862            0.550065            0.952545   \n",
       "\n",
       "     performance_in_online  education_Post Graduate  education_School  \\\n",
       "0                 0.684379                -0.376910         -0.310253   \n",
       "1                -1.405716                -0.376910         -0.310253   \n",
       "2                 1.206902                 2.653152         -0.310253   \n",
       "3                 0.684379                -0.376910         -0.310253   \n",
       "4                 0.684379                -0.376910         -0.310253   \n",
       "..                     ...                      ...               ...   \n",
       "815              -1.405716                 2.653152         -0.310253   \n",
       "816              -1.928240                -0.376910         -0.310253   \n",
       "817               1.729426                -0.376910         -0.310253   \n",
       "818               0.684379                -0.376910         -0.310253   \n",
       "819               0.684379                -0.376910         -0.310253   \n",
       "\n",
       "     education_Under Graduate  device_Desktop  device_Laptop  device_Mobile  \\\n",
       "0                    0.518989        -0.17364       0.735767      -0.689072   \n",
       "1                    0.518989        -0.17364       0.735767      -0.689072   \n",
       "2                   -1.926822        -0.17364       0.735767      -0.689072   \n",
       "3                    0.518989        -0.17364      -1.359126       1.451227   \n",
       "4                    0.518989        -0.17364       0.735767      -0.689072   \n",
       "..                        ...             ...            ...            ...   \n",
       "815                 -1.926822        -0.17364       0.735767      -0.689072   \n",
       "816                  0.518989        -0.17364      -1.359126       1.451227   \n",
       "817                  0.518989        -0.17364       0.735767      -0.689072   \n",
       "818                  0.518989        -0.17364       0.735767      -0.689072   \n",
       "819                  0.518989        -0.17364       0.735767      -0.689072   \n",
       "\n",
       "     status_Middle Class  status_Poor  status_Rich  interests_Both  \\\n",
       "0               0.280976    -0.214286     -0.17364       -0.722036   \n",
       "1               0.280976    -0.214286     -0.17364        1.384973   \n",
       "2               0.280976    -0.214286     -0.17364        1.384973   \n",
       "3               0.280976    -0.214286     -0.17364       -0.722036   \n",
       "4               0.280976    -0.214286     -0.17364       -0.722036   \n",
       "..                   ...          ...          ...             ...   \n",
       "815             0.280976    -0.214286     -0.17364       -0.722036   \n",
       "816            -3.559026     4.666667     -0.17364        1.384973   \n",
       "817             0.280976    -0.214286     -0.17364       -0.722036   \n",
       "818             0.280976    -0.214286     -0.17364        1.384973   \n",
       "819             0.280976    -0.214286     -0.17364        1.384973   \n",
       "\n",
       "     interests_Practical  interests_Theory  \n",
       "0               1.170445         -0.554811  \n",
       "1              -0.854376         -0.554811  \n",
       "2              -0.854376         -0.554811  \n",
       "3               1.170445         -0.554811  \n",
       "4              -0.854376          1.802416  \n",
       "..                   ...               ...  \n",
       "815             1.170445         -0.554811  \n",
       "816            -0.854376         -0.554811  \n",
       "817             1.170445         -0.554811  \n",
       "818            -0.854376         -0.554811  \n",
       "819            -0.854376         -0.554811  \n",
       "\n",
       "[820 rows x 29 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ecd5174",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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