BC_VR_Impact_Julia_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the DataSet\n",
    "impact_vr = pd.read_csv('/Users/julianuss/Downloads/Virtual_Reality_in_Education_Impact.csv')\n",
    "\n",
    "# Creating a DataFrame\n",
    "vr = pd.DataFrame(impact_vr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Grade_Level</th>\n",
       "      <th>Field_of_Study</th>\n",
       "      <th>Usage_of_VR_in_Education</th>\n",
       "      <th>Hours_of_VR_Usage_Per_Week</th>\n",
       "      <th>Engagement_Level</th>\n",
       "      <th>Improvement_in_Learning_Outcomes</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Instructor_VR_Proficiency</th>\n",
       "      <th>Perceived_Effectiveness_of_VR</th>\n",
       "      <th>Access_to_VR_Equipment</th>\n",
       "      <th>Impact_on_Creativity</th>\n",
       "      <th>Stress_Level_with_VR_Usage</th>\n",
       "      <th>Collaboration_with_Peers_via_VR</th>\n",
       "      <th>Feedback_from_Educators_on_VR</th>\n",
       "      <th>Interest_in_Continuing_VR_Based_Learning</th>\n",
       "      <th>Region</th>\n",
       "      <th>School_Support_for_VR_in_Curriculum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>STUD0001</td>\n",
       "      <td>13</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Postgraduate</td>\n",
       "      <td>Science</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Intermediate</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>No</td>\n",
       "      <td>South America</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STUD0002</td>\n",
       "      <td>16</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Undergraduate</td>\n",
       "      <td>Medicine</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Math</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>No</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STUD0003</td>\n",
       "      <td>15</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>High School</td>\n",
       "      <td>Science</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Art</td>\n",
       "      <td>Advanced</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>STUD0004</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Postgraduate</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Economics</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>No</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>STUD0005</td>\n",
       "      <td>22</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Undergraduate</td>\n",
       "      <td>Arts</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Art</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Yes</td>\n",
       "      <td>North America</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>STUD0006</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>High School</td>\n",
       "      <td>Science</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Economics</td>\n",
       "      <td>Intermediate</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>No</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>STUD0007</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Undergraduate</td>\n",
       "      <td>Business</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>History</td>\n",
       "      <td>Intermediate</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>No</td>\n",
       "      <td>North America</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>STUD0008</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>High School</td>\n",
       "      <td>Education</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Physics</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>STUD0009</td>\n",
       "      <td>29</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Undergraduate</td>\n",
       "      <td>Law</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Asia</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>STUD0010</td>\n",
       "      <td>16</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>Postgraduate</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Art</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Africa</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>STUD0011</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>High School</td>\n",
       "      <td>Business</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Math</td>\n",
       "      <td>Intermediate</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "      <td>No</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>STUD0012</td>\n",
       "      <td>15</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Undergraduate</td>\n",
       "      <td>Arts</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Math</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>Negative</td>\n",
       "      <td>No</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>STUD0013</td>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>High School</td>\n",
       "      <td>Business</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Art</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Yes</td>\n",
       "      <td>North America</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>STUD0014</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "      <td>Postgraduate</td>\n",
       "      <td>Business</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Economics</td>\n",
       "      <td>Intermediate</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>No</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>STUD0015</td>\n",
       "      <td>21</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>Undergraduate</td>\n",
       "      <td>Law</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Math</td>\n",
       "      <td>Intermediate</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Yes</td>\n",
       "      <td>North America</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>STUD0016</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>Postgraduate</td>\n",
       "      <td>Law</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Art</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>No</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>STUD0017</td>\n",
       "      <td>28</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Undergraduate</td>\n",
       "      <td>Medicine</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Biology</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "      <td>No</td>\n",
       "      <td>South America</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>STUD0018</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>High School</td>\n",
       "      <td>Business</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Economics</td>\n",
       "      <td>Advanced</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Negative</td>\n",
       "      <td>No</td>\n",
       "      <td>Europe</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>STUD0019</td>\n",
       "      <td>16</td>\n",
       "      <td>Female</td>\n",
       "      <td>High School</td>\n",
       "      <td>Law</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Economics</td>\n",
       "      <td>Advanced</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Yes</td>\n",
       "      <td>North America</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>STUD0020</td>\n",
       "      <td>16</td>\n",
       "      <td>Female</td>\n",
       "      <td>High School</td>\n",
       "      <td>Education</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Physics</td>\n",
       "      <td>Intermediate</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Negative</td>\n",
       "      <td>No</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student_ID  Age             Gender    Grade_Level Field_of_Study  \\\n",
       "0    STUD0001   13         Non-binary   Postgraduate        Science   \n",
       "1    STUD0002   16         Non-binary  Undergraduate       Medicine   \n",
       "2    STUD0003   15  Prefer not to say    High School        Science   \n",
       "3    STUD0004   24             Female   Postgraduate    Engineering   \n",
       "4    STUD0005   22         Non-binary  Undergraduate           Arts   \n",
       "5    STUD0006   28               Male    High School        Science   \n",
       "6    STUD0007   19               Male  Undergraduate       Business   \n",
       "7    STUD0008   19               Male    High School      Education   \n",
       "8    STUD0009   29         Non-binary  Undergraduate            Law   \n",
       "9    STUD0010   16  Prefer not to say   Postgraduate    Engineering   \n",
       "10   STUD0011   26               Male    High School       Business   \n",
       "11   STUD0012   15         Non-binary  Undergraduate           Arts   \n",
       "12   STUD0013   22               Male    High School       Business   \n",
       "13   STUD0014   27             Female   Postgraduate       Business   \n",
       "14   STUD0015   21  Prefer not to say  Undergraduate            Law   \n",
       "15   STUD0016   18             Female   Postgraduate            Law   \n",
       "16   STUD0017   28         Non-binary  Undergraduate       Medicine   \n",
       "17   STUD0018   18             Female    High School       Business   \n",
       "18   STUD0019   16             Female    High School            Law   \n",
       "19   STUD0020   16             Female    High School      Education   \n",
       "\n",
       "   Usage_of_VR_in_Education  Hours_of_VR_Usage_Per_Week  Engagement_Level  \\\n",
       "0                        No                           6                 1   \n",
       "1                        No                           6                 1   \n",
       "2                        No                           4                 5   \n",
       "3                       Yes                           2                 4   \n",
       "4                       Yes                          10                 3   \n",
       "5                       Yes                          10                 5   \n",
       "6                       Yes                           9                 4   \n",
       "7                        No                           1                 5   \n",
       "8                       Yes                           0                 2   \n",
       "9                       Yes                          10                 5   \n",
       "10                       No                          10                 1   \n",
       "11                      Yes                           5                 1   \n",
       "12                       No                           2                 5   \n",
       "13                       No                           9                 5   \n",
       "14                      Yes                           2                 2   \n",
       "15                      Yes                           3                 1   \n",
       "16                       No                           9                 1   \n",
       "17                      Yes                          10                 1   \n",
       "18                       No                           4                 3   \n",
       "19                      Yes                           8                 3   \n",
       "\n",
       "   Improvement_in_Learning_Outcomes           Subject  \\\n",
       "0                               Yes  Computer Science   \n",
       "1                               Yes              Math   \n",
       "2                               Yes               Art   \n",
       "3                                No         Economics   \n",
       "4                                No               Art   \n",
       "5                                No         Economics   \n",
       "6                               Yes           History   \n",
       "7                                No           Physics   \n",
       "8                               Yes  Computer Science   \n",
       "9                               Yes               Art   \n",
       "10                              Yes              Math   \n",
       "11                              Yes              Math   \n",
       "12                              Yes               Art   \n",
       "13                               No         Economics   \n",
       "14                              Yes              Math   \n",
       "15                              Yes               Art   \n",
       "16                              Yes           Biology   \n",
       "17                               No         Economics   \n",
       "18                              Yes         Economics   \n",
       "19                              Yes           Physics   \n",
       "\n",
       "   Instructor_VR_Proficiency  Perceived_Effectiveness_of_VR  \\\n",
       "0               Intermediate                              3   \n",
       "1                   Beginner                              2   \n",
       "2                   Advanced                              5   \n",
       "3                   Beginner                              5   \n",
       "4                   Beginner                              4   \n",
       "5               Intermediate                              1   \n",
       "6               Intermediate                              4   \n",
       "7                   Beginner                              5   \n",
       "8                   Beginner                              2   \n",
       "9                   Beginner                              4   \n",
       "10              Intermediate                              3   \n",
       "11                  Beginner                              1   \n",
       "12                  Beginner                              1   \n",
       "13              Intermediate                              3   \n",
       "14              Intermediate                              2   \n",
       "15                  Beginner                              5   \n",
       "16                  Beginner                              4   \n",
       "17                  Advanced                              4   \n",
       "18                  Advanced                              5   \n",
       "19              Intermediate                              1   \n",
       "\n",
       "   Access_to_VR_Equipment  Impact_on_Creativity Stress_Level_with_VR_Usage  \\\n",
       "0                     Yes                     5                       High   \n",
       "1                     Yes                     3                        Low   \n",
       "2                     Yes                     2                        Low   \n",
       "3                      No                     3                       High   \n",
       "4                     Yes                     1                     Medium   \n",
       "5                     Yes                     1                        Low   \n",
       "6                     Yes                     5                     Medium   \n",
       "7                     Yes                     2                        Low   \n",
       "8                      No                     2                       High   \n",
       "9                      No                     1                     Medium   \n",
       "10                    Yes                     1                        Low   \n",
       "11                     No                     4                        Low   \n",
       "12                    Yes                     2                     Medium   \n",
       "13                     No                     3                     Medium   \n",
       "14                    Yes                     3                     Medium   \n",
       "15                     No                     1                       High   \n",
       "16                    Yes                     3                        Low   \n",
       "17                    Yes                     5                     Medium   \n",
       "18                     No                     4                       High   \n",
       "19                     No                     3                     Medium   \n",
       "\n",
       "   Collaboration_with_Peers_via_VR Feedback_from_Educators_on_VR  \\\n",
       "0                               No                       Neutral   \n",
       "1                              Yes                      Positive   \n",
       "2                              Yes                       Neutral   \n",
       "3                               No                       Neutral   \n",
       "4                               No                      Negative   \n",
       "5                               No                       Neutral   \n",
       "6                              Yes                       Neutral   \n",
       "7                               No                       Neutral   \n",
       "8                              Yes                      Positive   \n",
       "9                              Yes                      Positive   \n",
       "10                              No                      Positive   \n",
       "11                              No                      Negative   \n",
       "12                              No                      Negative   \n",
       "13                              No                       Neutral   \n",
       "14                              No                      Positive   \n",
       "15                             Yes                      Positive   \n",
       "16                             Yes                      Positive   \n",
       "17                             Yes                      Negative   \n",
       "18                              No                       Neutral   \n",
       "19                             Yes                      Negative   \n",
       "\n",
       "   Interest_in_Continuing_VR_Based_Learning         Region  \\\n",
       "0                                        No  South America   \n",
       "1                                        No        Oceania   \n",
       "2                                       Yes        Oceania   \n",
       "3                                        No         Europe   \n",
       "4                                       Yes  North America   \n",
       "5                                        No           Asia   \n",
       "6                                        No  North America   \n",
       "7                                       Yes        Oceania   \n",
       "8                                       Yes           Asia   \n",
       "9                                       Yes         Africa   \n",
       "10                                       No         Africa   \n",
       "11                                       No        Oceania   \n",
       "12                                      Yes  North America   \n",
       "13                                       No        Oceania   \n",
       "14                                      Yes  North America   \n",
       "15                                       No        Oceania   \n",
       "16                                       No  South America   \n",
       "17                                       No         Europe   \n",
       "18                                      Yes  North America   \n",
       "19                                       No         Europe   \n",
       "\n",
       "   School_Support_for_VR_in_Curriculum  \n",
       "0                                   No  \n",
       "1                                   No  \n",
       "2                                   No  \n",
       "3                                  Yes  \n",
       "4                                  Yes  \n",
       "5                                  Yes  \n",
       "6                                  Yes  \n",
       "7                                   No  \n",
       "8                                   No  \n",
       "9                                   No  \n",
       "10                                 Yes  \n",
       "11                                  No  \n",
       "12                                 Yes  \n",
       "13                                  No  \n",
       "14                                 Yes  \n",
       "15                                  No  \n",
       "16                                 Yes  \n",
       "17                                  No  \n",
       "18                                 Yes  \n",
       "19                                 Yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the first 20 rows of the DataFrame for quick overview\n",
    "vr.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                    Non-Null Count  Dtype \n",
      "---  ------                                    --------------  ----- \n",
      " 0   Student_ID                                5000 non-null   object\n",
      " 1   Age                                       5000 non-null   int64 \n",
      " 2   Gender                                    5000 non-null   object\n",
      " 3   Grade_Level                               5000 non-null   object\n",
      " 4   Field_of_Study                            5000 non-null   object\n",
      " 5   Usage_of_VR_in_Education                  5000 non-null   object\n",
      " 6   Hours_of_VR_Usage_Per_Week                5000 non-null   int64 \n",
      " 7   Engagement_Level                          5000 non-null   int64 \n",
      " 8   Improvement_in_Learning_Outcomes          5000 non-null   object\n",
      " 9   Subject                                   5000 non-null   object\n",
      " 10  Instructor_VR_Proficiency                 5000 non-null   object\n",
      " 11  Perceived_Effectiveness_of_VR             5000 non-null   int64 \n",
      " 12  Access_to_VR_Equipment                    5000 non-null   object\n",
      " 13  Impact_on_Creativity                      5000 non-null   int64 \n",
      " 14  Stress_Level_with_VR_Usage                5000 non-null   object\n",
      " 15  Collaboration_with_Peers_via_VR           5000 non-null   object\n",
      " 16  Feedback_from_Educators_on_VR             5000 non-null   object\n",
      " 17  Interest_in_Continuing_VR_Based_Learning  5000 non-null   object\n",
      " 18  Region                                    5000 non-null   object\n",
      " 19  School_Support_for_VR_in_Curriculum       5000 non-null   object\n",
      "dtypes: int64(5), object(15)\n",
      "memory usage: 781.4+ KB\n",
      "None\n",
      "(5000, 20)\n",
      "               Age  Hours_of_VR_Usage_Per_Week  Engagement_Level  \\\n",
      "count  5000.000000                 5000.000000       5000.000000   \n",
      "mean     21.182200                    5.025400          3.021400   \n",
      "std       5.461957                    3.140816          1.427358   \n",
      "min      12.000000                    0.000000          1.000000   \n",
      "25%      16.000000                    2.000000          2.000000   \n",
      "50%      21.000000                    5.000000          3.000000   \n",
      "75%      26.000000                    8.000000          4.000000   \n",
      "max      30.000000                   10.000000          5.000000   \n",
      "\n",
      "       Perceived_Effectiveness_of_VR  Impact_on_Creativity  \n",
      "count                    5000.000000           5000.000000  \n",
      "mean                        2.951600              3.019600  \n",
      "std                         1.417625              1.437508  \n",
      "min                         1.000000              1.000000  \n",
      "25%                         2.000000              2.000000  \n",
      "50%                         3.000000              3.000000  \n",
      "75%                         4.000000              4.000000  \n",
      "max                         5.000000              5.000000  \n",
      "Index(['Student_ID', 'Age', 'Gender', 'Grade_Level', 'Field_of_Study',\n",
      "       'Usage_of_VR_in_Education', 'Hours_of_VR_Usage_Per_Week',\n",
      "       'Engagement_Level', 'Improvement_in_Learning_Outcomes', 'Subject',\n",
      "       'Instructor_VR_Proficiency', 'Perceived_Effectiveness_of_VR',\n",
      "       'Access_to_VR_Equipment', 'Impact_on_Creativity',\n",
      "       'Stress_Level_with_VR_Usage', 'Collaboration_with_Peers_via_VR',\n",
      "       'Feedback_from_Educators_on_VR',\n",
      "       'Interest_in_Continuing_VR_Based_Learning', 'Region',\n",
      "       'School_Support_for_VR_in_Curriculum'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Looking into the basic details of the DataFrame\n",
    "print(vr.info())\n",
    "print(vr.shape)\n",
    "print(vr.describe())\n",
    "print(vr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Dataset has 5000 rows and 20 columns, there are no null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "['STUD0001' 'STUD0002' 'STUD0003' ... 'STUD4998' 'STUD4999' 'STUD5000']\n"
     ]
    }
   ],
   "source": [
    "# Finding out the (number of) unique values for each column\n",
    "\n",
    "# Student_ID\n",
    "print(vr[\"Student_ID\"].nunique())\n",
    "print(vr[\"Student_ID\"].unique())\n",
    "\n",
    "# Student IDs are all unique (primary key, auto-increment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "[13 16 15 24 22 28 19 29 26 27 21 18 17 25 23 12 30 14 20]\n",
      "[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]\n"
     ]
    }
   ],
   "source": [
    "# Age\n",
    "print(vr[\"Age\"].nunique())\n",
    "print(vr[\"Age\"].unique())\n",
    "\n",
    "# Finding out the age of students\n",
    "student_ages_sorted = sorted(vr[\"Age\"].unique())\n",
    "int_list_sorted_ages = [int(age) for age in student_ages_sorted]\n",
    "print(int_list_sorted_ages)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The youngest student is 12 years old.\n",
      "The oldest student is 30 years old.\n",
      "The age difference between the youngest and the oldest student is 18 years.\n"
     ]
    }
   ],
   "source": [
    "# Finding out the minimum and maximum age and the age range of the students\n",
    "print(f\"The youngest student is {int_list_sorted_ages[0]} years old.\")\n",
    "print(f\"The oldest student is {int_list_sorted_ages[-1]} years old.\")\n",
    "\n",
    "age_range = int_list_sorted_ages[-1] - int_list_sorted_ages[0]\n",
    "\n",
    "print(f\"The age difference between the youngest and the oldest student is {age_range} years.\")\n",
    "\n",
    "# This already shows that the data was collected from various grade levels.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean age is 21.1822, the median is 21.0 and the mode is 21.\n"
     ]
    }
   ],
   "source": [
    "# Calculating the mean, the median and the mode in age\n",
    "mean_age = vr['Age'].mean()\n",
    "median_age = vr['Age'].median()\n",
    "mode_age = vr['Age'].mode().iloc[0] \n",
    "\n",
    "print(f\"The mean age is {mean_age}, the median is {median_age} and the mode is {mode_age}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The standard deviation is 5.461956586604363.\n",
      "The variance is 29.83296975395079.\n"
     ]
    }
   ],
   "source": [
    "# Calculating the standard deviation and variance\n",
    "print(f\"The standard deviation is {vr['Age'].std()}.\")\n",
    "print(f\"The variance is {vr['Age'].var()}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the age \n",
    "sns.countplot(x='Age', data=vr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "['Non-binary' 'Prefer not to say' 'Female' 'Male']\n"
     ]
    }
   ],
   "source": [
    "# Gender Unique values\n",
    "print(vr[\"Gender\"].nunique())\n",
    "print(vr[\"Gender\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Gender\n",
    "sns.countplot(x='Gender', data=vr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "['Postgraduate' 'Undergraduate' 'High School']\n"
     ]
    }
   ],
   "source": [
    "# Grade Level Unique values\n",
    "print(vr[\"Grade_Level\"].nunique())\n",
    "print(vr[\"Grade_Level\"].unique())\n",
    "\n",
    "# This shows there are students in the Data set that still go to high school "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Grade_Level', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Grade Level\n",
    "sns.countplot(x='Grade_Level', data=vr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "['Science' 'Medicine' 'Engineering' 'Arts' 'Business' 'Education' 'Law']\n",
      "Number of High School students: 1666\n",
      "Number of students in Education: 695\n"
     ]
    }
   ],
   "source": [
    "# Field of Study Unique values\n",
    "print(vr[\"Field_of_Study\"].nunique())\n",
    "print(vr[\"Field_of_Study\"].unique())\n",
    "\n",
    "# The 'Education'-Field of study seems to be used for the high school students\n",
    "\n",
    "# Verifying that by counting occurrences where Grade_Level is 'High School'\n",
    "high_school_count = (vr['Grade_Level'] == 'High School').sum()\n",
    "\n",
    "# Count occurrences where Field_of_Study is 'Education'\n",
    "education_count = (vr['Field_of_Study'] == 'Education').sum()\n",
    "\n",
    "print(f\"Number of High School students: {high_school_count}\")\n",
    "print(f\"Number of students in Education: {education_count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# That doesn't seem to be necessarily connected!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Grade_Level Student_ID  Age Field_of_Study\n",
      "2     High School   STUD0003   15        Science\n",
      "5     High School   STUD0006   28        Science\n",
      "7     High School   STUD0008   19      Education\n",
      "10    High School   STUD0011   26       Business\n",
      "12    High School   STUD0013   22       Business\n",
      "...           ...        ...  ...            ...\n",
      "4977  High School   STUD4978   24        Science\n",
      "4983  High School   STUD4984   19           Arts\n",
      "4987  High School   STUD4988   24        Science\n",
      "4996  High School   STUD4997   25       Medicine\n",
      "4997  High School   STUD4998   25    Engineering\n",
      "\n",
      "[1666 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Creating a table with all the high school students\n",
    "high_school_students = vr[(vr['Grade_Level'] == 'High School')]\n",
    "\n",
    "# Select relevant columns\n",
    "high_school_students = high_school_students[['Grade_Level','Student_ID', 'Age', 'Field_of_Study']]\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(high_school_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15 28 19 26 22 18 16 24 27 23 12 29 14 20 13 30 25 17 21]\n",
      "[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]\n",
      "The youngest high school student is 12 years old.\n",
      "The oldest high school student is 30 years old.\n"
     ]
    }
   ],
   "source": [
    "# Looking at the age of high school students\n",
    "print(high_school_students[\"Age\"].unique())\n",
    "\n",
    "# Finding out the age of students\n",
    "highschool_student_ages_sorted = sorted(high_school_students[\"Age\"].unique())\n",
    "int_list_sorted_ages_highschool_students = [int(age) for age in highschool_student_ages_sorted]\n",
    "print(int_list_sorted_ages_highschool_students)\n",
    "\n",
    "print(f\"The youngest high school student is {int_list_sorted_ages_highschool_students[0]} years old.\")\n",
    "print(f\"The oldest high school student is {int_list_sorted_ages_highschool_students[-1]} years old.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This shows the surprising fact that there's an almost unrealistic age range in high school students in the data that seems suspicious.\n",
    "# Anyways the high school students here are from 12 to 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Field_of_Study', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the fields of study of high school students \n",
    "sns.countplot(x='Field_of_Study', data=high_school_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unlike I thought High school students use VR in all 7 fields of study, surprisingly also fields that are not typical for high school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Grade_Level Student_ID  Age Field_of_Study\n",
      "1     Undergraduate   STUD0002   16       Medicine\n",
      "4     Undergraduate   STUD0005   22           Arts\n",
      "6     Undergraduate   STUD0007   19       Business\n",
      "8     Undergraduate   STUD0009   29            Law\n",
      "11    Undergraduate   STUD0012   15           Arts\n",
      "...             ...        ...  ...            ...\n",
      "4992  Undergraduate   STUD4993   28      Education\n",
      "4993  Undergraduate   STUD4994   17            Law\n",
      "4995  Undergraduate   STUD4996   18    Engineering\n",
      "4998  Undergraduate   STUD4999   22        Science\n",
      "4999  Undergraduate   STUD5000   21       Medicine\n",
      "\n",
      "[1685 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Creating a table for all Undergraduate students\n",
    "undergraduate_students = vr[(vr['Grade_Level'] == 'Undergraduate')]\n",
    "\n",
    "# Select relevant columns\n",
    "undergraduate_students = undergraduate_students[['Grade_Level','Student_ID', 'Age', 'Field_of_Study']]\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(undergraduate_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Field_of_Study', ylabel='count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AHqNMYefZZ5/VtGnTNHHiRHe3BwAAwK3K9Dk7J0+e1ODBg93dFgAAALcrU9gZPHiwVq9e7e62AAAAuF2ZbmPVq1dPTz31lLZs2aJmzZqpYsWKLuVjx451S+MAAACuVpnCzquvvqpKlSopNTVVqampLmUOh4OwAwAAPEaZws6BAwfc3Q4AAIBrokxjdgAAAG4UZbqy88ADD5RY/vrrr5epMQAAAO5WprBz8uRJl9f5+fn65ptvdOrUqSK/IBQAAKC8lCnsLFu27JJ5BQUFGjVqlOrWrXvVjQIAAHAXt43Z8fLyUmJiombPnu2uVQIAAFw1tw5QzsjI0Pnz5925SgAAgKtSpttYiYmJLq+NMcrMzNRHH32kYcOGuaVhAAAA7lCmsPPVV1+5vPby8lLVqlU1c+bMyz6pBQAAcD2VKex89tln7m4HAADANVGmsFPo2LFjSk9PlyQ1aNBAVatWdUujAAAA3KVMA5TPnDmjBx54QBEREercubM6d+6syMhIJSQk6OzZs+5uIwAAQJmVKewkJiYqNTVVK1as0KlTp3Tq1Cl9+OGHSk1N1fjx493dRgAAgDIr022s999/X++99566du1qzevTp4/8/Pz0P//zP5o/f7672gcAAHBVynRl5+zZswoLC7tkfrVq1biNBQAAPEqZwk5sbKwmT56sc+fOWfN+/fVXTZ06VbGxsW5rHAAAwNUq022sOXPmKD4+XjVq1FCLFi0kSV9//bWcTqdWr17t1gYCAABcjTKFnWbNmmnfvn1asmSJ9u7dK0m6++67NXToUPn5+bm1gQAAAFejTGFn+vTpCgsL04gRI1zmv/766zp27JgmTpzolsYBAABcrTKN2XnllVfUsGHDS+Y3adJECxYsuOpGAQAAuEuZwk5WVpYiIiIumV+1alVlZmZedaMAAADcpUxhJyoqSps2bbpk/qZNmxQZGXnVjQIAAHCXMo3ZGTFihMaNG6f8/Hx1795dkrRu3To98cQTfIIyAADwKGUKOxMmTNDx48f18MMPKy8vT5Lk6+uriRMnKikpya0NBAAAuBplCjsOh0PPP/+8nnrqKe3Zs0d+fn66+eab5XQ63d0+AACAq1KmsFOoUqVKatu2rbvaAgAA4HZlGqAMAABwoyDsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyvXsLNhwwb1799fkZGRcjgcWr58uUu5MUaTJk1SRESE/Pz81LNnT+3bt8+lzokTJzR06FAFBQUpJCRECQkJysnJuY57AQAAPFm5hp0zZ86oRYsWmjdvXpHlM2bM0EsvvaQFCxZo69atCggIUFxcnM6dO2fVGTp0qHbv3q01a9Zo5cqV2rBhgx566KHrtQsAAMDDVSjPjffu3Vu9e/cusswYozlz5ujJJ5/U7bffLkl68803FRYWpuXLl+uuu+7Snj17lJKSou3bt6tNmzaSpLlz56pPnz568cUXFRkZed32BQAAeCaPHbNz4MABZWVlqWfPnta84OBgxcTEaPPmzZKkzZs3KyQkxAo6ktSzZ095eXlp69atxa47NzdX2dnZLhMAALAnjw07WVlZkqSwsDCX+WFhYVZZVlaWqlWr5lJeoUIFhYaGWnWKMn36dAUHB1tTVFSUm1sPAAA8hceGnWspKSlJp0+ftqbDhw+Xd5MAAMA14rFhJzw8XJJ05MgRl/lHjhyxysLDw3X06FGX8vPnz+vEiRNWnaI4nU4FBQW5TAAAwJ48NuzUrl1b4eHhWrdunTUvOztbW7duVWxsrCQpNjZWp06d0o4dO6w6n376qQoKChQTE3Pd2wwAADxPuT6NlZOTo/3791uvDxw4oLS0NIWGhqpmzZoaN26cnn32Wd18882qXbu2nnrqKUVGRmrAgAGSpEaNGik+Pl4jRozQggULlJ+frzFjxuiuu+7iSSwAACCpnMPOl19+qW7dulmvExMTJUnDhg3T4sWL9cQTT+jMmTN66KGHdOrUKXXs2FEpKSny9fW1llmyZInGjBmjHj16yMvLS4MGDdJLL7103fcFAAB4pnINO127dpUxpthyh8Ohp59+Wk8//XSxdUJDQ7V06dJr0TwAAGADHjtmBwAAwB0IOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNY8OuxMmTJFDofDZWrYsKFVfu7cOY0ePVqVK1dWpUqVNGjQIB05cqQcWwwAADyNR4cdSWrSpIkyMzOt6fPPP7fKHnvsMa1YsULvvvuuUlNT9dNPP2ngwIHl2FoAAOBpKpR3Ay6nQoUKCg8Pv2T+6dOntWjRIi1dulTdu3eXJCUnJ6tRo0basmWLbr311uvdVAAA4IE8/srOvn37FBkZqTp16mjo0KE6dOiQJGnHjh3Kz89Xz549rboNGzZUzZo1tXnz5hLXmZubq+zsbJcJAADYk0eHnZiYGC1evFgpKSmaP3++Dhw4oE6dOumXX35RVlaWfHx8FBIS4rJMWFiYsrKySlzv9OnTFRwcbE1RUVHXcC8AAEB58ujbWL1797Z+bt68uWJiYhQdHa1//vOf8vPzK/N6k5KSlJiYaL3Ozs4m8AAAYFMefWXnYiEhIapfv77279+v8PBw5eXl6dSpUy51jhw5UuQYn99zOp0KCgpymQAAgD3dUGEnJydHGRkZioiIUOvWrVWxYkWtW7fOKk9PT9ehQ4cUGxtbjq0EAACexKNvYz3++OPq37+/oqOj9dNPP2ny5Mny9vbW3XffreDgYCUkJCgxMVGhoaEKCgrSI488otjYWJ7EAgAAFo8OOz/88IPuvvtuHT9+XFWrVlXHjh21ZcsWVa1aVZI0e/ZseXl5adCgQcrNzVVcXJxefvnlcm41AADwJB4ddt55550Sy319fTVv3jzNmzfvOrUIAADcaG6oMTsAAABXirADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABsjbADAABszTZhZ968eapVq5Z8fX0VExOjbdu2lXeTAACAB7BF2PnHP/6hxMRETZ48Wf/+97/VokULxcXF6ejRo+XdNAAAUM5sEXZmzZqlESNG6P7771fjxo21YMEC+fv76/XXXy/vpgEAgHJ2w4edvLw87dixQz179rTmeXl5qWfPntq8eXM5tgwAAHiCCuXdgKv1888/68KFCwoLC3OZHxYWpr179xa5TG5urnJzc63Xp0+fliRlZ2cXWf9C7q9uaq1nKm6/S4O+KZrd+0Wib0pC3xSPvikefVO84vqmcL4xpuQVmBvcjz/+aCSZL774wmX+hAkTTLt27YpcZvLkyUYSExMTExMTkw2mw4cPl5gVbvgrO1WqVJG3t7eOHDniMv/IkSMKDw8vcpmkpCQlJiZarwsKCnTixAlVrlxZDofjmrb3crKzsxUVFaXDhw8rKCioXNviaeib4tE3xaNvikffFI1+KZ6n9Y0xRr/88osiIyNLrHfDhx0fHx+1bt1a69at04ABAyT9Fl7WrVunMWPGFLmM0+mU0+l0mRcSEnKNW3plgoKCPOJA8kT0TfHom+LRN8Wjb4pGvxTPk/omODj4snVu+LAjSYmJiRo2bJjatGmjdu3aac6cOTpz5ozuv//+8m4aAAAoZ7YIO0OGDNGxY8c0adIkZWVlqWXLlkpJSblk0DIAAPjvY4uwI0ljxowp9rbVjcTpdGry5MmX3GYDfVMS+qZ49E3x6Jui0S/Fu1H7xmHM5Z7XAgAAuHHd8B8qCAAAUBLCDgAAsDXCTjlav369HA6HTp06JUlavHjxFT0CX6tWLc2ZM+eatA2e7eJjB/+9rvS8ca05HA4tX778v2a7Fxs+fLj1MSg3Gk87ltyJsFOC4cOHy+FwaOTIkZeUjR49Wg6HQ8OHD3fb9oYMGaLvvvuu1PW3b9+uhx56yG3bLy+bN2+Wt7e3+vbtW6r6U6ZMUcuWLa9to65S4bFTOFWuXFnx8fHauXOnW9bfvn17ZWZmlurzJezmRvpjcvFxUDjFx8e7bRtXet641jIzM9W7d++rWsf16LerdfDgQTkcDqWlpbnM/+tf/6rFixeXS5sk6dixYxo1apRq1qwpp9Op8PBwxcXFadOmTZdd1tOOJXeyzdNY10pUVJTeeecdzZ49W35+fpKkc+fOaenSpapZs6Zbt+Xn52dtozSqVq3q1u2Xl0WLFumRRx7RokWL9NNPPxX7SZjGGF24cOE6t67s4uPjlZycLEnKysrSk08+qX79+unQoUNXvW4fH59iPyEcnuX3x0Ehdz7JcqXnjWslLy/Prcflte63a6W8/wEZNGiQ8vLy9MYbb6hOnTo6cuSI1q1bp+PHj192WU85lq4Fruxcxi233KKoqCh98MEH1rwPPvhANWvWVKtWrax5BQUFmj59umrXri0/Pz+1aNFC7733nsu6Vq1apfr168vPz0/dunXTwYMHXcqLuoS4YsUKtW3bVr6+vqpSpYruuOMOq+zi21gOh0Ovvfaa7rjjDvn7++vmm2/Wv/71L5f1ffPNN+rdu7cqVaqksLAw3Xvvvfr555/L2DtXLycnR//4xz80atQo9e3b1+U/osJbNR9//LFat24tp9Opt956S1OnTtXXX39t/be3ePFiGWM0ZcoU67+ZyMhIjR07ttz2S5L1X1V4eLhatmypP/3pTzp8+LCOHTtW5G2otLQ0ORwO67j4/vvv1b9/f910000KCAhQkyZNtGrVKknF3wL95JNP1KhRI1WqVEnx8fHKzMx0adNrr72mRo0aydfXVw0bNtTLL79sleXl5WnMmDGKiIiQr6+voqOjNX36dEnyyP4tyqxZs9SsWTMFBAQoKipKDz/8sHJyciT9tg9Vq1Z1eV+2bNlSERER1uvPP/9cTqdTZ8+edVubfn8cFE433XSTpNK9Z//1r3/p5ptvlq+vr7p166Y33nijxNvfhVc+//73v6tWrVoKDg7WXXfdpV9++cWqU5rz1eXOFV27dtWYMWM0btw4ValSRXFxcdY+Fd5OKrz68cEHH6hbt27y9/dXixYttHnzZpdtLVy4UFFRUfL399cdd9yh3bt3a+3atcX22759+9S5c2f5+vqqcePGWrNmjcv6SvP+kqRNmzapa9eu8vf310033aS4uDidPHlSkpSSkqKOHTsqJCRElStXVr9+/ZSRkWEtW7t2bUlSq1at5HA41LVrV0mXXnnMzc3V2LFjVa1aNfn6+qpjx47avn37JW1dt26d2rRpI39/f7Vv317p6em6UqdOndLGjRv1/PPPq1u3boqOjla7du2UlJSkP/zhD1adP/7xjwoLC5Ovr6+aNm2qlStXSir6b9CHH36oW265Rb6+vqpTp46mTp2q8+fPW+WlOYZ3796tfv36KSgoSIGBgerUqZNLX5Z0XnKbq/8qTvsaNmyYuf32282sWbNMjx49rPk9evQws2fPNrfffrsZNmyYMcaYZ5991jRs2NCkpKSYjIwMk5ycbJxOp1m/fr0xxphDhw4Zp9NpEhMTzd69e81bb71lwsLCjCRz8uRJY4wxycnJJjg42NrOypUrjbe3t5k0aZL59ttvTVpamnnuuees8ujoaDN79mzrtSRTo0YNs3TpUrNv3z4zduxYU6lSJXP8+HFjjDEnT540VatWNUlJSWbPnj3m3//+t7nttttMt27drk0HlsKiRYtMmzZtjDHGrFixwtStW9cUFBQYY4z57LPPjCTTvHlzs3r1arN//37zww8/mPHjx5smTZqYzMxMk5mZac6ePWveffddExQUZFatWmW+//57s3XrVvPqq6+W234VHjuFfvnlF/PHP/7R1KtXz1y4cMHat8LfvTHGfPXVV0aSOXDggDHGmL59+5rbbrvN7Ny502RkZJgVK1aY1NRUY4y5ZPnk5GRTsWJF07NnT7N9+3azY8cO06hRI3PPPfdY63/rrbdMRESEef/9981//vMf8/7775vQ0FCzePFiY4wxL7zwgomKijIbNmwwBw8eNBs3bjRLly41xhiP6t+L+/b3Zs+ebT799FNz4MABs27dOtOgQQMzatQoq3zgwIFm9OjRxhhjTpw4YXx8fExwcLDZs2ePMea393GHDh2uS1uNufx79j//+Y+pWLGiefzxx83evXvN22+/bapXr17ieWPy5MmmUqVKZuDAgWbXrl1mw4YNJjw83Pz5z3+26lzufFWac0WXLl1MpUqVzIQJE8zevXvN3r17rX1atmyZMcaYAwcOGEmmYcOGZuXKlSY9Pd3ceeedJjo62uTn5xtjjPn888+Nl5eXeeGFF0x6erqZN2+e8fHxMRUqVCiyzy5cuGCaNm1qevToYdLS0kxqaqpp1aqVy3ZL8/766quvjNPpNKNGjTJpaWnmm2++MXPnzjXHjh0zxhjz3nvvmffff9/s27fPfPXVV6Z///6mWbNm5sKFC8YYY7Zt22YkmbVr15rMzEzrd3bx73zs2LEmMjLSrFq1yuzevdsMGzbM3HTTTVb9wrbGxMSY9evXm927d5tOnTqZ9u3bF3vcFCc/P99UqlTJjBs3zpw7d67Ivrv11ltNkyZNzOrVq63zyqpVq4wxlx5LGzZsMEFBQWbx4sUmIyPDrF692tSqVctMmTLFqnO5Y/iHH34woaGhZuDAgWb79u0mPT3dvP7669bxcrnzkrsQdkpQeNAePXrUOJ1Oc/DgQXPw4EHj6+trjh07ZoWdc+fOGX9//0u+eT0hIcHcfffdxhhjkpKSTOPGjV3KJ06cWOJJKzY21gwdOrTY9hUVdp588knrdU5OjpFkPv74Y2OMMc8884zp1auXyzoOHz5sJJn09PRS94s7tW/f3syZM8cY89sbtUqVKuazzz4zxvz/k8Dy5ctdlpk8ebJp0aKFy7yZM2ea+vXrm7y8vOvR7MsaNmyY8fb2NgEBASYgIMBIMhEREWbHjh3GmNKdjJs1a+ZyUvm9osKOJLN//36rzrx580xYWJj1um7dulZ4KfTMM8+Y2NhYY4wxjzzyiOnevbsVNn/Pk/r3cgHi9959911TuXJl6/VLL71kmjRpYowxZvny5SYmJsbcfvvtZv78+cYYY3r27OkSCtzR1t8fB4XTtGnTjDGXf89OnDjRNG3a1GWdf/nLXy4bdvz9/U12drY1b8KECSYmJsYYY0p1virNuaJLly6mVatWl+xzUWHntddes8p3795tJFkBc8iQIaZv374u66hTp46RVGS/ffLJJ6ZChQrmxx9/tOp//PHHVxx27r777isKtseOHTOSzK5du1z27auvvnKp9/vjMycnx1SsWNEsWbLEKs/LyzORkZFmxowZLm1du3atVeejjz4yksyvv/5a6vYVeu+998xNN91kfH19Tfv27U1SUpL5+uuvjTHGfPLJJ8bLy6vY8/3Fx1KPHj1c/sE2xpi///3vJiIiwnp9uWM4KSnJ1K5du9hzx+XOS+7CmJ1SqFq1qnWLxRijvn37qkqVKlb5/v37dfbsWd12220uy+Xl5Vm3uvbs2aOYmBiX8tjY2BK3m5aWphEjRlxRW5s3b279HBAQoKCgIB09elSS9PXXX+uzzz5TpUqVLlkuIyND9evXv6JtXa309HRt27ZNy5YtkyRVqFBBQ4YM0aJFi6xLwpLUpk2by65r8ODBmjNnjurUqaP4+Hj16dNH/fv3V4UK5XeId+vWTfPnz5cknTx5Ui+//LJ69+6tbdu2lWr5sWPHatSoUVq9erV69uypQYMGufx+L+bv76+6detaryMiIqzf/ZkzZ5SRkaGEhASXY+r8+fPWGIPhw4frtttuU4MGDRQfH69+/fqpV69ekjyzf4uydu1aTZ8+XXv37lV2drbOnz+vc+fO6ezZs/L391eXLl306KOP6tixY0pNTVXXrl0VHh6u9evXKyEhQV988YWeeOIJt7bp98dBodDQUOvnkt6z6enpatu2rcuy7dq1u+w2a9WqpcDAQOv174+F0pyvSnuuaN269WXbIrnuY+Ftw6NHj6phw4ZKT093uT0vSVWqVNGhQ4cuGfwbGhqqv//974qKinIZ23e5c2lR0tLSNHjw4GLL9+3bp0mTJmnr1q36+eefVVBQIEk6dOiQmjZtWqptZGRkKD8/Xx06dLDmVaxYUe3atdOePXtc6hbXR1c6NnTQoEHq27evNm7cqC1btujjjz/WjBkz9Nprr+no0aOqUaNGqc/1X3/9tTZt2qRp06ZZ8y5cuODynrq47Rcfw2lpaerUqZMqVqx4yfpLc15yF886U3mwBx54wPo6innz5rmUFY4J+Oijj1S9enWXsqsZUFeWgWIXH1AOh8N6k+bk5Kh///56/vnnL1nu9+MWrpdFixbp/PnzLictY4ycTqf+9re/WfMCAgIuu66oqCilp6dr7dq1WrNmjR5++GG98MILSk1NLfJNdj0EBASoXr161uvXXntNwcHBWrhwoRUizO8+wDw/P99l+QcffFBxcXH66KOPtHr1ak2fPl0zZ87UI488UuT2ivrdF66/8BhduHDhJaHb29tb0m/j0w4cOKCPP/5Ya9eu1f/8z/+oZ8+eeu+99zyyfy928OBB9evXT6NGjdK0adMUGhqqzz//XAkJCcrLy5O/v7+aNWum0NBQpaamKjU1VdOmTVN4eLief/55bd++Xfn5+Wrfvr1b23XxcXCxkt6zZXW584BU8vmqtOeK0rw3L26Pw+GQpFLtY0n9VhIvr9+Go5b0/rrc+bV///6Kjo7WwoULFRkZqYKCAjVt2lR5eXllatPllLWPiuLr66vbbrtNt912m5566ik9+OCDmjx5sh5//PErWk9OTo6mTp2qgQMHFrmNotouuR5vJfVzac5L7kLYKaX4+Hjl5eXJ4XBYA/EKNW7cWE6nU4cOHVKXLl2KXL5Ro0aXDNrasmVLidts3ry51q1b57Zvb7/lllv0/vvvq1atWuX+H/n58+f15ptvaubMmdYf/kIDBgzQ22+/rYYNGxa5rI+PT5FPZfn5+al///7q37+/Ro8erYYNG2rXrl265ZZbrsk+XCmHwyEvLy/9+uuv1pN0mZmZ1qDLi/+LlX4LcSNHjtTIkSOVlJSkhQsXFht2ShIWFqbIyEj95z//0dChQ4utFxQUpCFDhmjIkCG68847FR8frxMnTig0NNTj+3fHjh0qKCjQzJkzrT92//znP13qOBwOderUSR9++KF2796tjh07yt/fX7m5uXrllVfUpk2bUv8Bvx4aNGhgDUov9PvBrWVRmvPV9TxXNGjQ4JJ9KumhiUaNGunw4cPKzMy0gtfF59LSvL8Kz69Tp069ZBvHjx9Xenq6Fi5cqE6dOkn6bfD67/n4+EhSiU+I1q1bVz4+Ptq0aZOio6Ml/Ra6tm/frnHjxhW7nLs1btxYy5cvV/PmzfXDDz/ou+++K9XVnVtuuUXp6ellDp3Sb/38xhtvKD8//5JQVNrzkjsQdkrJ29vbuux4ceIMDAzU448/rscee0wFBQXq2LGjTp8+rU2bNikoKEjDhg3TyJEjNXPmTE2YMEEPPvigduzYcdnPYpg8ebJ69OihunXr6q677tL58+e1atUqTZw4sUz7MHr0aC1cuFB33323nnjiCYWGhmr//v1655139Nprr7k9SZdk5cqVOnnypBISEi65XDlo0CAtWrRIL7zwQpHL1qpVSwcOHFBaWppq1KihwMBAvf3227pw4YJiYmLk7++vt956S35+ftYJpjzk5uYqKytL0m+3sf72t79Z/zHXq1dPUVFRmjJliqZNm6bvvvtOM2fOdFl+3Lhx6t27t+rXr6+TJ0/qs88+U6NGjcrcnqlTp2rs2LEKDg5WfHy8cnNz9eWXX+rkyZNKTEzUrFmzFBERoVatWsnLy0vvvvuuwsPDFRISosWLF3tU/54+ffqSP15VqlRRfn6+5s6dq/79+2vTpk1asGDBJct27dpV48ePV5s2bazbNJ07d9aSJUs0YcIEt7f198dBoQoVKrjcCi/OH//4R82aNUsTJ05UQkKC0tLSrPNG4X//V6o056vrea545JFH1LlzZ82aNUv9+/fXp59+qh9//FGSiuy3nj17qn79+ho2bJheeOEFZWdn6y9/+YtLvdK8v5KSktSsWTM9/PDDGjlypHx8fPTZZ59p8ODBCg0NVeXKlfXqq68qIiJChw4d0p/+9CeX5atVqyY/Pz+lpKSoRo0a8vX1veRcFhAQoFGjRmnChAkKDQ1VzZo1NWPGDJ09e1YJCQnu6kLL8ePHNXjwYD3wwANq3ry5AgMD9eWXX2rGjBm6/fbb1aVLF3Xu3FmDBg3SrFmzVK9ePe3du7fYzzCaNGmS+vXrp5o1a+rOO++Ul5eXvv76a33zzTd69tlnS9WmMWPGaO7cubrrrruUlJSk4OBgbdmyRe3atVODBg0ue15yG7eOALKZyw2E/P3TWAUFBWbOnDmmQYMGpmLFiqZq1aomLi7OenrGmN+eNqpXr55xOp2mU6dO5vXXXy9xoKExxrz//vumZcuWxsfHx1SpUsUMHDjQKitqgHLhAL1CwcHBJjk52Xr93XffmTvuuMOEhIQYPz8/07BhQzNu3LgiB6VeS/369TN9+vQpsmzr1q1GkvnrX/96ySBDY34bYDlo0CATEhJiJJnk5GSzbNkyExMTY4KCgkxAQIC59dZbXQb8XW/Dhg0zkqwpMDDQtG3b1rz33ntWnc8//9w0a9bM+Pr6mk6dOpl3333XZQDlmDFjTN26dY3T6TRVq1Y19957r/n555+NMUUPUL742Fm2bJm5+C2+ZMkS63i66aabTOfOnc0HH3xgjDHm1VdfNS1btjQBAQEmKCjI9OjRw/z73/+21uUp/Xtx3xZOCQkJZtasWSYiIsL4+fmZuLg48+abbxY7UHXixInWvNmzZxtJJiUl5bq0tUGDBsaY0r1nP/zwQ+u80bVrVzN//nyXwatFDVC+eAD/7NmzTXR0tPW6NOery50runTpYh599NFL9vn3+1TUIN6TJ08aSdaDCMb8duxVr17d+Pn5mQEDBlhPVxXXb+np6aZjx47Gx8fH1K9f36SkpFzSl5d7fxljzPr160379u2N0+k0ISEhJi4uzjpW1qxZYxo1amScTqdp3ry5Wb9+/SXbWLhwoYmKijJeXl6mS5cuxphL/278+uuv5pFHHjFVqlQxTqfTdOjQwWzbts0qL81g6tI6d+6c+dOf/mRuueUWExwcbPz9/U2DBg3Mk08+ac6ePWuMMeb48ePm/vvvN5UrVza+vr6madOmZuXKlcaYos8jKSkppn379sbPz88EBQWZdu3auTyJWZpj+Ouvvza9evUy/v7+JjAw0HTq1MlkZGRY5SWdl9yFbz0HgBvItGnTtGDBAh0+fLi8m3LNjBgxQnv37tXGjRvLuymwCW5jAYAHe/nll9W2bVtVrlxZmzZt0gsvvGA9LGEXL774om677TYFBATo448/1htvvHFtPlgO/7UIOwDgwfbt26dnn31WJ06cUM2aNTV+/HglJSWVd7Pcatu2bZoxY4Z++eUX1alTRy+99JIefPDB8m4WbITbWAAAwNb4biwAAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AZdK1a9cr+n6fxYsXKyQkpMQ6U6ZMUcuWLa+qXb+3d+9e3XrrrfL19XXret3h4MGDcjgcRX4nGgD3IuwAKNHw4cPlcDgumWbMmKFnnnmmvJtXosmTJysgIEDp6elat27dZeunpqaqe/fuCg0Nlb+/v26++WYNGzbM+qbr0gQ2AJ6HsAPgsuLj45WZmekytW7dWoGBgeXdtBJlZGSoY8eOio6OVuXKlUus++233yo+Pl5t2rTRhg0btGvXLs2dO1c+Pj4lfrM1AM9H2AFwWU6nU+Hh4S5Tjx49XG5j5ebm6vHHH1f16tUVEBCgmJgYrV+/vsT1/t///Z/CwsIUGBiohIQEnTt3rtRtKigo0NNPP60aNWrI6XSqZcuWSklJscodDod27Nihp59+Wg6HQ1OmTClxfatXr1Z4eLhmzJihpk2bqm7duoqPj9fChQvl5+en9evX6/7779fp06etq1uF63Q4HFq+fLnL+gq/Lb7Qtm3b1KpVK/n6+qpNmzb66quvrDJjjOrVq6cXX3zRZR1paWlyOBzav39/qfsFwKUIOwDcYsyYMdq8ebPeeecd7dy5U4MHD1Z8fLz27dtXZP1//vOfmjJlip577jl9+eWXioiIuKLvQ/rrX/+qmTNn6sUXX9TOnTsVFxenP/zhD9b2MjMz1aRJE40fP16ZmZl6/PHHS1xfeHi4MjMztWHDhiLL27dvrzlz5igoKMi6unW5dRbKyclRv3791LhxY+3YsUNTpkxxWdbhcOiBBx5QcnKyy3LJycnq3Lmz6tWrV6rtACiGW79DHYDtDBs2zHh7e5uAgABruvPOO02XLl3Mo48+aowx5vvvvzfe3t7mxx9/dFm2R48eJikpyRhjTHJysgkODrbKYmNjzcMPP+xSPyYmxrRo0aJU7YqMjDTTpk1zmde2bVuXdbZo0cJMnjy5VOs7f/68GT58uJFkwsPDzYABA8zcuXPN6dOnrToX70MhSWbZsmUu84KDg01ycrIxxphXXnnFVK5c2fz6669W+fz5840k89VXXxljjPnxxx+Nt7e32bp1qzHGmLy8PFOlShWzePHiUrUfQPG4sgPgsrp166a0tDRreumll1zKd+3apQsXLqh+/fqqVKmSNaWmpiojI6PIde7Zs0cxMTEu82JjY0vVnuzsbP3000/q0KGDy/wOHTpoz549V7Bn/5+3t7eSk5P1ww8/aMaMGapevbqee+45NWnSRJmZmWVaZ6E9e/aoefPm8vX1teZdvK+RkZHq27evXn/9dUnSihUrlJubq8GDB1/VtgHwrecASiEgIKDEWyk5OTny9vbWjh075O3t7VJWqVKla908t6pevbruvfde3XvvvXrmmWdUv359LViwQFOnTi12GYfDIXPRdyrn5+df8bYffPBB3XvvvZo9e7aSk5M1ZMgQ+fv7X/F6ALjiyg6Aq9aqVStduHBBR48eVb169Vym8PDwIpdp1KiRtm7d6jJvy5YtpdpeUFCQIiMjtWnTJpf5mzZtUuPGjcu2E0W46aabFBERoTNnzkhSsU9mVa1a1eXqz759+3T27FnrdaNGjbRz506XAdhF7WufPn0UEBCg+fPnKyUlRQ888IDb9gX4b8aVHQBXrX79+ho6dKjuu+8+zZw5U61atdKxY8e0bt06NW/eXH379r1kmUcffVTDhw9XmzZt1KFDBy1ZskS7d+9WnTp1SrXNCRMmaPLkyapbt65atmyp5ORkpaWlacmSJWXah1deeUVpaWm64447VLduXZ07d05vvvmmdu/erblz50qSatWqpZycHK1bt04tWrSQv7+//P391b17d/3tb39TbGysLly4oIkTJ6pixYrWuu+55x795S9/0YgRI5SUlKSDBw9e8uSV9NuttOHDhyspKUk333xzqW/rASgZV3YAuEVycrLuu+8+jR8/Xg0aNNCAAQO0fft21axZs8j6Q4YM0VNPPaUnnnhCrVu31vfff69Ro0aVentjx45VYmKixo8fr2bNmiklJUX/+te/dPPNN5ep/e3atVNOTo5GjhypJk2aqEuXLtqyZYuWL1+uLl26SPrtiayRI0dqyJAhqlq1qmbMmCFJmjlzpqKiotSpUyfdc889evzxx11uP1WqVEkrVqzQrl271KpVK/3lL3/R888/X2Q7EhISlJeXp/vvv79M+wHgUg5z8Y1mAEC52bhxo3r06KHDhw8rLCysvJsD2AJhBwA8QG5uro4dO6Zhw4YpPDy8zLfjAFyK21gAPNLvH2G/eNq4ceMVr++5554rdn29e/e+BntwZd5++21FR0fr1KlT1u0xAO7BlR0AHqmkr0ioXr26/Pz8rmh9J06c0IkTJ4os8/PzU/Xq1a9ofQBuHIQdAABga9zGAgAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtvb/AEngONp38Wh8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the fields of study of undergraduate students \n",
    "sns.countplot(x='Field_of_Study', data=undergraduate_students)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Grade_Level Student_ID  Age Field_of_Study\n",
      "0     Postgraduate   STUD0001   13        Science\n",
      "3     Postgraduate   STUD0004   24    Engineering\n",
      "9     Postgraduate   STUD0010   16    Engineering\n",
      "13    Postgraduate   STUD0014   27       Business\n",
      "15    Postgraduate   STUD0016   18            Law\n",
      "...            ...        ...  ...            ...\n",
      "4985  Postgraduate   STUD4986   23    Engineering\n",
      "4986  Postgraduate   STUD4987   29           Arts\n",
      "4988  Postgraduate   STUD4989   17       Medicine\n",
      "4991  Postgraduate   STUD4992   20       Medicine\n",
      "4994  Postgraduate   STUD4995   19      Education\n",
      "\n",
      "[1649 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame with all the postgraduate students\n",
    "postgraduate_students = vr[(vr['Grade_Level'] == 'Postgraduate')]\n",
    "\n",
    "# Select relevant columns\n",
    "postgraduate_students = postgraduate_students[['Grade_Level','Student_ID', 'Age', 'Field_of_Study']]\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(postgraduate_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Field_of_Study', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the fields of study of postgraduate students \n",
    "sns.countplot(x='Field_of_Study', data=postgraduate_students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Age             Gender    Grade_Level Field_of_Study  \\\n",
      "Student_ID                                                         \n",
      "STUD0001     13         Non-binary   Postgraduate        Science   \n",
      "STUD0002     16         Non-binary  Undergraduate       Medicine   \n",
      "STUD0003     15  Prefer not to say    High School        Science   \n",
      "STUD0004     24             Female   Postgraduate    Engineering   \n",
      "STUD0005     22         Non-binary  Undergraduate           Arts   \n",
      "...         ...                ...            ...            ...   \n",
      "STUD4996     18               Male  Undergraduate    Engineering   \n",
      "STUD4997     25               Male    High School       Medicine   \n",
      "STUD4998     25             Female    High School    Engineering   \n",
      "STUD4999     22             Female  Undergraduate        Science   \n",
      "STUD5000     21               Male  Undergraduate       Medicine   \n",
      "\n",
      "                   Region  \n",
      "Student_ID                 \n",
      "STUD0001    South America  \n",
      "STUD0002          Oceania  \n",
      "STUD0003          Oceania  \n",
      "STUD0004           Europe  \n",
      "STUD0005    North America  \n",
      "...                   ...  \n",
      "STUD4996             Asia  \n",
      "STUD4997    South America  \n",
      "STUD4998    North America  \n",
      "STUD4999           Africa  \n",
      "STUD5000             Asia  \n",
      "\n",
      "[5000 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame that shows all the student information\n",
    "students = vr[['Student_ID', 'Age', 'Gender', 'Grade_Level', 'Field_of_Study', 'Region']].set_index('Student_ID')\n",
    "\n",
    "print(students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Grade_Level Field_of_Study           Subject Usage_of_VR_in_Education\n",
      "0      Postgraduate        Science  Computer Science                       No\n",
      "1     Undergraduate       Medicine              Math                       No\n",
      "2       High School        Science               Art                       No\n",
      "3      Postgraduate    Engineering         Economics                      Yes\n",
      "4     Undergraduate           Arts               Art                      Yes\n",
      "...             ...            ...               ...                      ...\n",
      "4995  Undergraduate    Engineering           Biology                       No\n",
      "4996    High School       Medicine               Art                       No\n",
      "4997    High School    Engineering  Computer Science                       No\n",
      "4998  Undergraduate        Science              Math                       No\n",
      "4999  Undergraduate       Medicine         Economics                      Yes\n",
      "\n",
      "[5000 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame that shows all the student information\n",
    "subjects = vr[['Grade_Level', 'Field_of_Study', 'Subject', 'Usage_of_VR_in_Education']]\n",
    "\n",
    "print(subjects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Grade_Level Field_of_Study           Subject Usage_of_VR_in_Education\n",
      "3      Postgraduate    Engineering         Economics                      Yes\n",
      "4     Undergraduate           Arts               Art                      Yes\n",
      "5       High School        Science         Economics                      Yes\n",
      "6     Undergraduate       Business           History                      Yes\n",
      "8     Undergraduate            Law  Computer Science                      Yes\n",
      "...             ...            ...               ...                      ...\n",
      "4987    High School        Science           Biology                      Yes\n",
      "4992  Undergraduate      Education           History                      Yes\n",
      "4993  Undergraduate            Law           History                      Yes\n",
      "4994   Postgraduate      Education              Math                      Yes\n",
      "4999  Undergraduate       Medicine         Economics                      Yes\n",
      "\n",
      "[2528 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Finding out in which fields of study VR is used (used the most and the least)\n",
    "vr_subjects = vr[(vr['Usage_of_VR_in_Education'] == 'Yes')][['Grade_Level', 'Field_of_Study', 'Subject', 'Usage_of_VR_in_Education']]\n",
    "\n",
    "# Displaying the filtered DataFrame\n",
    "print(vr_subjects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Subject', ylabel='count'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Subject', data=vr_subjects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In History VR seems to be mostly used but all 7 subjects show a similar count of usage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigating further the VR related data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Grade_Level Field_of_Study           Subject Usage_of_VR_in_Education  \\\n",
      "0      Postgraduate        Science  Computer Science                       No   \n",
      "1     Undergraduate       Medicine              Math                       No   \n",
      "2       High School        Science               Art                       No   \n",
      "3      Postgraduate    Engineering         Economics                      Yes   \n",
      "4     Undergraduate           Arts               Art                      Yes   \n",
      "...             ...            ...               ...                      ...   \n",
      "4995  Undergraduate    Engineering           Biology                       No   \n",
      "4996    High School       Medicine               Art                       No   \n",
      "4997    High School    Engineering  Computer Science                       No   \n",
      "4998  Undergraduate        Science              Math                       No   \n",
      "4999  Undergraduate       Medicine         Economics                      Yes   \n",
      "\n",
      "      Hours_of_VR_Usage_Per_Week Improvement_in_Learning_Outcomes  \\\n",
      "0                              6                              Yes   \n",
      "1                              6                              Yes   \n",
      "2                              4                              Yes   \n",
      "3                              2                               No   \n",
      "4                             10                               No   \n",
      "...                          ...                              ...   \n",
      "4995                          10                               No   \n",
      "4996                           5                              Yes   \n",
      "4997                           1                               No   \n",
      "4998                           4                              Yes   \n",
      "4999                           1                               No   \n",
      "\n",
      "     Instructor_VR_Proficiency  Perceived_Effectiveness_of_VR  \\\n",
      "0                 Intermediate                              3   \n",
      "1                     Beginner                              2   \n",
      "2                     Advanced                              5   \n",
      "3                     Beginner                              5   \n",
      "4                     Beginner                              4   \n",
      "...                        ...                            ...   \n",
      "4995                  Advanced                              1   \n",
      "4996                  Advanced                              4   \n",
      "4997                  Beginner                              2   \n",
      "4998                  Beginner                              3   \n",
      "4999              Intermediate                              2   \n",
      "\n",
      "     Access_to_VR_Equipment  Impact_on_Creativity Stress_Level_with_VR_Usage  \\\n",
      "0                       Yes                     5                       High   \n",
      "1                       Yes                     3                        Low   \n",
      "2                       Yes                     2                        Low   \n",
      "3                        No                     3                       High   \n",
      "4                       Yes                     1                     Medium   \n",
      "...                     ...                   ...                        ...   \n",
      "4995                    Yes                     1                       High   \n",
      "4996                    Yes                     5                     Medium   \n",
      "4997                     No                     3                        Low   \n",
      "4998                    Yes                     3                        Low   \n",
      "4999                    Yes                     4                        Low   \n",
      "\n",
      "     Collaboration_with_Peers_via_VR Feedback_from_Educators_on_VR  \\\n",
      "0                                 No                       Neutral   \n",
      "1                                Yes                      Positive   \n",
      "2                                Yes                       Neutral   \n",
      "3                                 No                       Neutral   \n",
      "4                                 No                      Negative   \n",
      "...                              ...                           ...   \n",
      "4995                             Yes                       Neutral   \n",
      "4996                              No                      Negative   \n",
      "4997                             Yes                      Negative   \n",
      "4998                              No                      Negative   \n",
      "4999                              No                      Positive   \n",
      "\n",
      "     Interest_in_Continuing_VR_Based_Learning         Region  \\\n",
      "0                                          No  South America   \n",
      "1                                          No        Oceania   \n",
      "2                                         Yes        Oceania   \n",
      "3                                          No         Europe   \n",
      "4                                         Yes  North America   \n",
      "...                                       ...            ...   \n",
      "4995                                      Yes           Asia   \n",
      "4996                                      Yes  South America   \n",
      "4997                                      Yes  North America   \n",
      "4998                                      Yes         Africa   \n",
      "4999                                      Yes           Asia   \n",
      "\n",
      "     School_Support_for_VR_in_Curriculum  \n",
      "0                                     No  \n",
      "1                                     No  \n",
      "2                                     No  \n",
      "3                                    Yes  \n",
      "4                                    Yes  \n",
      "...                                  ...  \n",
      "4995                                  No  \n",
      "4996                                  No  \n",
      "4997                                  No  \n",
      "4998                                  No  \n",
      "4999                                 Yes  \n",
      "\n",
      "[5000 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "vr_usage = vr[['Grade_Level', 'Field_of_Study', 'Subject', 'Usage_of_VR_in_Education', 'Hours_of_VR_Usage_Per_Week', 'Improvement_in_Learning_Outcomes', 'Instructor_VR_Proficiency', 'Perceived_Effectiveness_of_VR', 'Access_to_VR_Equipment', 'Impact_on_Creativity', 'Stress_Level_with_VR_Usage', 'Collaboration_with_Peers_via_VR', 'Feedback_from_Educators_on_VR', 'Interest_in_Continuing_VR_Based_Learning', 'Region', 'School_Support_for_VR_in_Curriculum' ]]\n",
    "\n",
    "print(vr_usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Improvement_in_Learning_Outcomes', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Improvement_in_Learning_Outcomes', data=vr_usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Yes' 'No']\n",
      "['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "# This suggests that there is almost no improvement to be counted in learning outcomes. \n",
    "# BUT since there are no values and the only Yes or No, just like in the column if VR is even used for educational purpuses,\n",
    "# it's obvious that there's always a no if VR wasn't even used, so the count of improvement Yes or Nos is misleading.\n",
    "print(vr['Improvement_in_Learning_Outcomes'].unique())\n",
    "print(vr['Usage_of_VR_in_Education'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mismatched rows: 1246\n"
     ]
    }
   ],
   "source": [
    "# It has to be verified that the Improvement in Learning outcomes is only related to VR, which means we have to see if \n",
    "# there's a No in Usage of VR in Education there is also a No in Improvement in Learning Outcomes.\n",
    "\n",
    "# Filter rows where 'Usage_of_VR_in_Education' is 'No'\n",
    "no_vr_usage = vr[vr['Usage_of_VR_in_Education'] == 'No']\n",
    "\n",
    "# Check if there are any rows where 'Usage_of_VR_in_Education' is 'No' but 'Improvement_in_Learning_Outcomes' is 'Yes'\n",
    "mismatch = no_vr_usage[no_vr_usage['Improvement_in_Learning_Outcomes'] == 'Yes']\n",
    "\n",
    "# Count the number of mismatched rows\n",
    "number_of_mismatched_rows = mismatch.shape[0]\n",
    "\n",
    "# Print the count\n",
    "print(f\"Number of mismatched rows: {number_of_mismatched_rows}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Student_ID  Age             Gender    Grade_Level Field_of_Study  \\\n",
      "3      STUD0004   24             Female   Postgraduate    Engineering   \n",
      "4      STUD0005   22         Non-binary  Undergraduate           Arts   \n",
      "5      STUD0006   28               Male    High School        Science   \n",
      "6      STUD0007   19               Male  Undergraduate       Business   \n",
      "8      STUD0009   29         Non-binary  Undergraduate            Law   \n",
      "...         ...  ...                ...            ...            ...   \n",
      "4987   STUD4988   24  Prefer not to say    High School        Science   \n",
      "4992   STUD4993   28  Prefer not to say  Undergraduate      Education   \n",
      "4993   STUD4994   17         Non-binary  Undergraduate            Law   \n",
      "4994   STUD4995   19               Male   Postgraduate      Education   \n",
      "4999   STUD5000   21               Male  Undergraduate       Medicine   \n",
      "\n",
      "     Usage_of_VR_in_Education  Hours_of_VR_Usage_Per_Week  Engagement_Level  \\\n",
      "3                         Yes                           2                 4   \n",
      "4                         Yes                          10                 3   \n",
      "5                         Yes                          10                 5   \n",
      "6                         Yes                           9                 4   \n",
      "8                         Yes                           0                 2   \n",
      "...                       ...                         ...               ...   \n",
      "4987                      Yes                          10                 2   \n",
      "4992                      Yes                           8                 1   \n",
      "4993                      Yes                           7                 4   \n",
      "4994                      Yes                           9                 5   \n",
      "4999                      Yes                           1                 1   \n",
      "\n",
      "     Improvement_in_Learning_Outcomes           Subject  \\\n",
      "3                                  No         Economics   \n",
      "4                                  No               Art   \n",
      "5                                  No         Economics   \n",
      "6                                 Yes           History   \n",
      "8                                 Yes  Computer Science   \n",
      "...                               ...               ...   \n",
      "4987                              Yes           Biology   \n",
      "4992                               No           History   \n",
      "4993                              Yes           History   \n",
      "4994                              Yes              Math   \n",
      "4999                               No         Economics   \n",
      "\n",
      "     Instructor_VR_Proficiency  Perceived_Effectiveness_of_VR  \\\n",
      "3                     Beginner                              5   \n",
      "4                     Beginner                              4   \n",
      "5                 Intermediate                              1   \n",
      "6                 Intermediate                              4   \n",
      "8                     Beginner                              2   \n",
      "...                        ...                            ...   \n",
      "4987              Intermediate                              1   \n",
      "4992                  Beginner                              2   \n",
      "4993                  Advanced                              2   \n",
      "4994                  Advanced                              5   \n",
      "4999              Intermediate                              2   \n",
      "\n",
      "     Access_to_VR_Equipment  Impact_on_Creativity Stress_Level_with_VR_Usage  \\\n",
      "3                        No                     3                       High   \n",
      "4                       Yes                     1                     Medium   \n",
      "5                       Yes                     1                        Low   \n",
      "6                       Yes                     5                     Medium   \n",
      "8                        No                     2                       High   \n",
      "...                     ...                   ...                        ...   \n",
      "4987                     No                     2                     Medium   \n",
      "4992                     No                     1                     Medium   \n",
      "4993                     No                     2                       High   \n",
      "4994                    Yes                     4                        Low   \n",
      "4999                    Yes                     4                        Low   \n",
      "\n",
      "     Collaboration_with_Peers_via_VR Feedback_from_Educators_on_VR  \\\n",
      "3                                 No                       Neutral   \n",
      "4                                 No                      Negative   \n",
      "5                                 No                       Neutral   \n",
      "6                                Yes                       Neutral   \n",
      "8                                Yes                      Positive   \n",
      "...                              ...                           ...   \n",
      "4987                              No                      Negative   \n",
      "4992                              No                      Positive   \n",
      "4993                              No                       Neutral   \n",
      "4994                             Yes                       Neutral   \n",
      "4999                              No                      Positive   \n",
      "\n",
      "     Interest_in_Continuing_VR_Based_Learning         Region  \\\n",
      "3                                          No         Europe   \n",
      "4                                         Yes  North America   \n",
      "5                                          No           Asia   \n",
      "6                                          No  North America   \n",
      "8                                         Yes           Asia   \n",
      "...                                       ...            ...   \n",
      "4987                                       No           Asia   \n",
      "4992                                       No         Europe   \n",
      "4993                                       No  North America   \n",
      "4994                                       No         Africa   \n",
      "4999                                      Yes           Asia   \n",
      "\n",
      "     School_Support_for_VR_in_Curriculum  \n",
      "3                                    Yes  \n",
      "4                                    Yes  \n",
      "5                                    Yes  \n",
      "6                                    Yes  \n",
      "8                                     No  \n",
      "...                                  ...  \n",
      "4987                                  No  \n",
      "4992                                 Yes  \n",
      "4993                                  No  \n",
      "4994                                  No  \n",
      "4999                                 Yes  \n",
      "\n",
      "[2528 rows x 20 columns]\n"
     ]
    }
   ],
   "source": [
    "# So obviously the improvement in learning outcomes is not related to VR.\n",
    "# To evaluate the VR success in students' academic performance, we need to filter where VR was even used.\n",
    "\n",
    "vr_use_in_education = vr[vr['Usage_of_VR_in_Education'] == 'Yes']\n",
    "print(vr_use_in_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3        No\n",
      "4        No\n",
      "5        No\n",
      "6       Yes\n",
      "8       Yes\n",
      "       ... \n",
      "4987    Yes\n",
      "4992     No\n",
      "4993    Yes\n",
      "4994    Yes\n",
      "4999     No\n",
      "Name: Improvement_in_Learning_Outcomes, Length: 2528, dtype: object]\n"
     ]
    }
   ],
   "source": [
    "print([vr_use_in_education['Improvement_in_Learning_Outcomes']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Improvement_in_Learning_Outcomes', ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize where there was an improvement when VR was used\n",
    "\n",
    "sns.countplot(x='Improvement_in_Learning_Outcomes', data=vr_use_in_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So it has to be admitted that there was acutally no improvement in learning outcomes.\n",
    "# Anyways there might be other positive effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6  4  2 10  9  1  0  5  3  8  7]\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "print(vr['Hours_of_VR_Usage_Per_Week'].unique())\n",
    "print(vr['Hours_of_VR_Usage_Per_Week'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "hours_of_usage_sorted = sorted(vr['Hours_of_VR_Usage_Per_Week'].unique())\n",
    "int_list_sorted_hours = [int(hours) for hours in hours_of_usage_sorted]\n",
    "print(int_list_sorted_hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The range between the hours to use VR per week is 10.\n",
      "The mean hours spent in VR per week are 5.0254, the median are 5.0 and the mode are 6 hours spent in VR.\n"
     ]
    }
   ],
   "source": [
    "# Calculating the hour range of VR usage, the mean, the median and the mode of VR usage\n",
    "hour_range = int_list_sorted_hours[-1] - int_list_sorted_hours[0]\n",
    "print(f\"The range between the hours to use VR per week is {hour_range}.\")\n",
    "\n",
    "mean_hours = vr['Hours_of_VR_Usage_Per_Week'].mean()\n",
    "median_hours = vr['Hours_of_VR_Usage_Per_Week'].median()\n",
    "mode_hours = vr['Hours_of_VR_Usage_Per_Week'].mode().iloc[0] \n",
    "\n",
    "print(f\"The mean hours spent in VR per week are {mean_hours}, the median are {median_hours} and the mode are {mode_hours} hours spent in VR.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 5 4 3 2]\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print(vr['Engagement_Level'].unique())\n",
    "print(vr['Engagement_Level'].nunique())\n",
    "\n",
    "# Engagement levels can be rated from 1 - 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Intermediate' 'Beginner' 'Advanced']\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(vr['Instructor_VR_Proficiency'].unique())\n",
    "print(vr['Instructor_VR_Proficiency'].nunique())\n",
    "\n",
    "# Instructors VR proficiency can be beginner, intermediate or advanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 2 5 4 1]\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print(vr['Perceived_Effectiveness_of_VR'].unique())\n",
    "print(vr['Perceived_Effectiveness_of_VR'].nunique())\n",
    "\n",
    "# Since we found out around half of the students in the DataSet haven't even used VR this is again misleading since there are \n",
    "# no null values for the students that haven't used VR.\n",
    "\n",
    "# Again we can only rate the perceived effectiveness for those students who actually used it. (= vr_use_in_education)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Perceived_Effectiveness_of_VR', ylabel='count'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the perceived effectiveness of VR\n",
    "sns.countplot(x='Perceived_Effectiveness_of_VR', data=vr_use_in_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 3 2 1 4]\n",
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Impact_on_Creativity', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same with impact on creativity\n",
    "print(vr['Impact_on_Creativity'].unique())\n",
    "print(vr['Impact_on_Creativity'].nunique())\n",
    "\n",
    "sns.countplot(x='Impact_on_Creativity', data=vr_use_in_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Stress_Level_with_VR_Usage', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Stress_Level_with_VR_Usage', data=vr_use_in_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Region', ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Counting the VR use by region\n",
    "sns.countplot(x='Region',data=vr_use_in_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This result is actually surprising."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "students.to_csv('students.csv',index='Student_ID')\n",
    "subjects.to_csv('subjects.csv',index=False)\n",
    "vr_usage.to_csv('vr_usage.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql.connector\n",
    "\n",
    "# Connect to the MySQL database\n",
    "cnx = mysql.connector.connect(\n",
    "    host='localhost', \n",
    "    port=3306,\n",
    "    user='Julia', \n",
    "    password='asphodelaceae',\n",
    "    database='Virtual_Reality_in_Education_Impact'  \n",
    ")\n",
    "cursor = cnx.cursor()\n",
    "\n",
    "# Query to create the 'students' table\n",
    "create_table_query = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS students (\n",
    "    Student_ID VARCHAR(50) PRIMARY KEY,\n",
    "    Age INT,\n",
    "    Gender VARCHAR(50),\n",
    "    Grade_Level VARCHAR(50),\n",
    "    Field_of_Study VARCHAR(100),\n",
    "    Region VARCHAR(50)\n",
    ");\n",
    "\"\"\"\n",
    "cursor.execute(create_table_query)\n",
    "\n",
    "# Read the CSV file\n",
    "import pandas as pd\n",
    "students_df = pd.read_csv('/Users/julianuss/Documents/GitHub/project-2-eda-sql/students.csv')\n",
    "\n",
    "# Insert data into the 'student' table\n",
    "insert_query = \"\"\"\n",
    "INSERT INTO students (Student_ID, Age, Gender, Grade_Level, Field_of_Study, Region) \n",
    "VALUES (%s, %s, %s, %s, %s, %s);\n",
    "\"\"\"\n",
    "\n",
    "# Loop through the dataframe and insert each row\n",
    "for index, row in students_df.iterrows():\n",
    "    cursor.execute(insert_query, tuple(row))\n",
    "\n",
    "# Commit the changes and close the connection\n",
    "cnx.commit()\n",
    "cursor.close()\n",
    "cnx.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Connect to the MySQL database\n",
    "cnx = mysql.connector.connect(\n",
    "    host='localhost', \n",
    "    port=3306,\n",
    "    user='Julia', \n",
    "    password='asphodelaceae',\n",
    "    database='Virtual_Reality_in_Education_Impact'  \n",
    ")\n",
    "cursor = cnx.cursor()\n",
    "\n",
    "# Query to create the 'subjects' table\n",
    "create_table_query = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS subjects (\n",
    "    Grade_Level VARCHAR(50),\n",
    "    Field_of_Study VARCHAR(100),\n",
    "    Subject VARCHAR(100),\n",
    "    Usage_of_VR_in_Education VARCHAR(10)\n",
    ");\n",
    "\"\"\"\n",
    "cursor.execute(create_table_query)\n",
    "\n",
    "# Read the CSV file\n",
    "subjects_df = pd.read_csv('/Users/julianuss/Documents/GitHub/project-2-eda-sql/subjects.csv')\n",
    "\n",
    "# Insert data into the 'subjects' table\n",
    "insert_query = \"\"\"\n",
    "INSERT INTO subjects (Grade_Level, Field_of_Study, Subject, Usage_of_VR_in_Education) \n",
    "VALUES (%s, %s, %s, %s);\n",
    "\"\"\"\n",
    "\n",
    "# Loop through the dataframe and insert each row\n",
    "for index, row in subjects_df.iterrows():\n",
    "    cursor.execute(insert_query, tuple(row))\n",
    "\n",
    "# Commit the changes and close the connection\n",
    "cnx.commit()\n",
    "cursor.close()\n",
    "cnx.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to the MySQL database\n",
    "cnx = mysql.connector.connect(\n",
    "    host='localhost', \n",
    "    port=3306,\n",
    "    user='Julia', \n",
    "    password='asphodelaceae',\n",
    "    database='Virtual_Reality_in_Education_Impact'  \n",
    ")\n",
    "cursor = cnx.cursor()\n",
    "\n",
    "# Query to create the 'vr_usage' table\n",
    "create_table_query = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS vr_usage (\n",
    "    Grade_Level VARCHAR(50),\n",
    "    Field_of_Study VARCHAR(100),\n",
    "    Subject VARCHAR(100),\n",
    "    Usage_of_VR_in_Education VARCHAR(10),\n",
    "    Hours_of_VR_Usage_Per_Week INT,\n",
    "    Improvement_in_Learning_Outcomes VARCHAR(10),\n",
    "    Instructor_VR_Proficiency VARCHAR(50),\n",
    "    Perceived_Effectiveness_of_VR INT,\n",
    "    Access_to_VR_Equipment VARCHAR(10),\n",
    "    Impact_on_Creativity VARCHAR(50),\n",
    "    Stress_Level_with_VR_Usage VARCHAR(50),\n",
    "    Collaboration_with_Peers_via_VR VARCHAR(50),\n",
    "    Feedback_from_Educators_on_VR VARCHAR(50),\n",
    "    Interest_in_Continuing_VR_Based_Learning VARCHAR(50),\n",
    "    Region VARCHAR(50),\n",
    "    School_Support_for_VR_in_Curriculum VARCHAR(50)\n",
    ");\n",
    "\"\"\"\n",
    "cursor.execute(create_table_query)\n",
    "\n",
    "# Read the CSV file\n",
    "vr_usage_df = pd.read_csv('/Users/julianuss/Documents/GitHub/project-2-eda-sql/vr_usage.csv')\n",
    "\n",
    "# Insert data into the 'vr_usage' table\n",
    "insert_query = \"\"\"\n",
    "INSERT INTO vr_usage (\n",
    "    Grade_Level, Field_of_Study, Subject, Usage_of_VR_in_Education, \n",
    "    Hours_of_VR_Usage_Per_Week, Improvement_in_Learning_Outcomes, \n",
    "    Instructor_VR_Proficiency, Perceived_Effectiveness_of_VR, Access_to_VR_Equipment, \n",
    "    Impact_on_Creativity, Stress_Level_with_VR_Usage, Collaboration_with_Peers_via_VR, \n",
    "    Feedback_from_Educators_on_VR, Interest_in_Continuing_VR_Based_Learning, \n",
    "    Region, School_Support_for_VR_in_Curriculum\n",
    ") \n",
    "VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);\n",
    "\"\"\"\n",
    "\n",
    "# Loop through the dataframe and insert each row\n",
    "for index, row in vr_usage_df.iterrows():\n",
    "    cursor.execute(insert_query, tuple(row))\n",
    "\n",
    "# Commit the changes and close the connection\n",
    "cnx.commit()\n",
    "cursor.close()\n",
    "cnx.close()"
   ]
  }
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