{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21abd26c73fd0070",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<div>\n",
    "    <h1><center>CS105 Mini-Project</center></h1>\n",
    "    <h2><center>Does who a student is living with effect if and how they work jobs?</center></h2>\n",
    "</div>\n",
    "\n",
    "By:\n",
    "- Anshul Gupta <agupt109@ucr.edu>\n",
    "- Ali Naqvi <anaqv007@ucr.edu>\n",
    "- Alex Zhang <zzhan309@ucr.edu>\n",
    "- Nathan Lee <nlee097@ucr.edu>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d8e8ad7c61ba61",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Data Loading & Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b68b27041fdab1a5",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:39.410627Z",
     "start_time": "2024-02-24T06:34:39.389092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "              Timestamp What is your current class standing?  \\\n0     2/9/2024 20:12:14                               Senior   \n1     2/9/2024 20:16:34                               Junior   \n2     2/9/2024 20:18:55                               Junior   \n3     2/9/2024 20:24:00                               Senior   \n4     2/9/2024 20:26:16                             Graduate   \n..                  ...                                  ...   \n255  2/14/2024 19:46:28                               Junior   \n256   2/15/2024 0:28:38                                  NaN   \n257   2/15/2024 8:33:45                               Senior   \n258  2/15/2024 16:10:40                            Sophomore   \n259  2/15/2024 16:14:11                            Sophomore   \n\n    What is your age? Who do you live with?  \\\n0                 23+               Neither   \n1                  20                  Both   \n2                 23+               Friends   \n3                 23+               Neither   \n4                  22               Neither   \n..                ...                   ...   \n255                21               Friends   \n256                21                Family   \n257                21                Family   \n258                21                Family   \n259                18               Friends   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n0                                                House      \n1                                            Apartment      \n2                                                House      \n3                                            Apartment      \n4                                            Apartment      \n..                                                 ...      \n255                                              House      \n256                                          Apartment      \n257                                              House      \n258                                          Apartment      \n259                                               Dorm      \n\n    How many people live in your household?  \\\n0                                         6   \n1                                         4   \n2                                         4   \n3                                         1   \n4                                         1   \n..                                      ...   \n255                                       5   \n256             North District 4 bed 2 bath   \n257                                       9   \n258                                       4   \n259      3 (room), 8 (hall), ~70 (building)   \n\n    What was your GPA your very first quarter at UCR?  \\\n0                                                2.73   \n1                                                 3.7   \n2                                                3.75   \n3                                                3.81   \n4                                                3.23   \n..                                                ...   \n255                                                 4   \n256                                               3.5   \n257                                               3.7   \n258                                                 3   \n259                                                 4   \n\n    What are your career plans right after graduation? Do you currently work?  \\\n0                            Get into the Job Industry                    Yes   \n1                            Get into the Job Industry                     No   \n2                      If no job go to graduate school                     No   \n3                                         Not Sure Yet                     No   \n4                            Get into the Job Industry                    Yes   \n..                                                 ...                    ...   \n255                          Get into the Job Industry                    Yes   \n256                          Get into the Job Industry                     No   \n257                                 Attend Grad School                     No   \n258                          Get into the Job Industry                    Yes   \n259                                 Attend Grad School                     No   \n\n    How many hours do you work per week on average?  \\\n0                                            5 - 10   \n1                                               NaN   \n2                                               NaN   \n3                                               NaN   \n4                                           10 - 20   \n..                                              ...   \n255                                         10 - 20   \n256                                             NaN   \n257                                           1 - 5   \n258                                          5 - 10   \n259                                             NaN   \n\n    Do you work on or off campus?  \\\n0                      Off-campus   \n1                             NaN   \n2                             NaN   \n3                             NaN   \n4                      Off-campus   \n..                            ...   \n255                     On-campus   \n256                           NaN   \n257                    Off-campus   \n258                     On-campus   \n259                           NaN   \n\n    Do you work in a department related to your major?  \\\n0                                                   No   \n1                                                  NaN   \n2                                                  NaN   \n3                                                   No   \n4                                                  Yes   \n..                                                 ...   \n255                                                 No   \n256                                                NaN   \n257                                                 No   \n258                                                 No   \n259                                                NaN   \n\n    Do you have family members who have careers related to your career aspirations?  \\\n0                  No family in related fields/careers                                \n1                  No family in related fields/careers                                \n2    1 person in my immediate family (parent/legal ...                                \n3                  No family in related fields/careers                                \n4    1 person in my immediate family (parent/legal ...                                \n..                                                 ...                                \n255  1 person in my immediate family (parent/legal ...                                \n256  1 person in my immediate family (parent/legal ...                                \n257                No family in related fields/careers                                \n258                No family in related fields/careers                                \n259  1 person in my immediate family (parent/legal ...                                \n\n    Do you have roommates that are part of your major?  \n0                                                   No  \n1                                                  Yes  \n2                                                   No  \n3                                                   No  \n4                                                   No  \n..                                                 ...  \n255                                                 No  \n256                                                 No  \n257                                                 No  \n258                                                 No  \n259                                                Yes  \n\n[260 rows x 14 columns]",
      "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>Timestamp</th>\n      <th>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>What are your career plans right after graduation?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have family members who have careers related to your career aspirations?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2/9/2024 20:12:14</td>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>House</td>\n      <td>6</td>\n      <td>2.73</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2/9/2024 20:16:34</td>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.7</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2/9/2024 20:18:55</td>\n      <td>Junior</td>\n      <td>23+</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>3.75</td>\n      <td>If no job go to graduate school</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2/9/2024 20:24:00</td>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.81</td>\n      <td>Not Sure Yet</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2/9/2024 20:26:16</td>\n      <td>Graduate</td>\n      <td>22</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.23</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>Yes</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>2/14/2024 19:46:28</td>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>5</td>\n      <td>4</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>2/15/2024 0:28:38</td>\n      <td>NaN</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>North District 4 bed 2 bath</td>\n      <td>3.5</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>2/15/2024 8:33:45</td>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>3.7</td>\n      <td>Attend Grad School</td>\n      <td>No</td>\n      <td>1 - 5</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>2/15/2024 16:10:40</td>\n      <td>Sophomore</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>2/15/2024 16:14:11</td>\n      <td>Sophomore</td>\n      <td>18</td>\n      <td>Friends</td>\n      <td>Dorm</td>\n      <td>3 (room), 8 (hall), ~70 (building)</td>\n      <td>4</td>\n      <td>Attend Grad School</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>260 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load dataframe from data.csv\n",
    "df = pd.read_csv(\"data.csv\")\n",
    "\n",
    "# Select relevant columns\n",
    "df = df.iloc[:, [0, 2, 3, 7, 8, 9, 34, 55, 58, 59, 60, 61, 62, 26]]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ee1fc9a8abba2b",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f72adcb3bc0285e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:39.422395Z",
     "start_time": "2024-02-24T06:34:39.411276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "              Timestamp What is your current class standing?  \\\n0     2/9/2024 20:12:14                               Senior   \n1     2/9/2024 20:16:34                               Junior   \n2     2/9/2024 20:18:55                               Junior   \n3     2/9/2024 20:24:00                               Senior   \n4     2/9/2024 20:26:16                             Graduate   \n..                  ...                                  ...   \n255  2/14/2024 19:46:28                               Junior   \n256   2/15/2024 0:28:38                                  NaN   \n257   2/15/2024 8:33:45                               Senior   \n258  2/15/2024 16:10:40                            Sophomore   \n259  2/15/2024 16:14:11                            Sophomore   \n\n    What is your age? Who do you live with?  \\\n0                 23+               Neither   \n1                  20                  Both   \n2                 23+               Friends   \n3                 23+               Neither   \n4                  22               Neither   \n..                ...                   ...   \n255                21               Friends   \n256                21                Family   \n257                21                Family   \n258                21                Family   \n259                18               Friends   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n0                                                House      \n1                                            Apartment      \n2                                                House      \n3                                            Apartment      \n4                                            Apartment      \n..                                                 ...      \n255                                              House      \n256                                          Apartment      \n257                                              House      \n258                                          Apartment      \n259                                               Dorm      \n\n     How many people live in your household?  \\\n0                                          6   \n1                                          4   \n2                                          4   \n3                                          1   \n4                                          1   \n..                                       ...   \n255                                        5   \n256                                        4   \n257                                        9   \n258                                        4   \n259                                        3   \n\n     What was your GPA your very first quarter at UCR?  \\\n0                                                 2.73   \n1                                                 3.70   \n2                                                 3.75   \n3                                                 3.81   \n4                                                 3.23   \n..                                                 ...   \n255                                               4.00   \n256                                               3.50   \n257                                               3.70   \n258                                               3.00   \n259                                               4.00   \n\n    What are your career plans right after graduation? Do you currently work?  \\\n0                            Get into the Job Industry                    Yes   \n1                            Get into the Job Industry                     No   \n2                      If no job go to graduate school                     No   \n3                                         Not Sure Yet                     No   \n4                            Get into the Job Industry                    Yes   \n..                                                 ...                    ...   \n255                          Get into the Job Industry                    Yes   \n256                          Get into the Job Industry                     No   \n257                                 Attend Grad School                     No   \n258                          Get into the Job Industry                    Yes   \n259                                 Attend Grad School                     No   \n\n    How many hours do you work per week on average?  \\\n0                                            5 - 10   \n1                                                 0   \n2                                                 0   \n3                                                 0   \n4                                           10 - 20   \n..                                              ...   \n255                                         10 - 20   \n256                                               0   \n257                                               0   \n258                                          5 - 10   \n259                                               0   \n\n    Do you work on or off campus?  \\\n0                      Off-campus   \n1                             NaN   \n2                             NaN   \n3                             NaN   \n4                      Off-campus   \n..                            ...   \n255                     On-campus   \n256                           NaN   \n257                    Off-campus   \n258                     On-campus   \n259                           NaN   \n\n    Do you work in a department related to your major?  \\\n0                                                   No   \n1                                                  NaN   \n2                                                  NaN   \n3                                                  NaN   \n4                                                  Yes   \n..                                                 ...   \n255                                                 No   \n256                                                NaN   \n257                                                NaN   \n258                                                 No   \n259                                                NaN   \n\n    Do you have family members who have careers related to your career aspirations?  \\\n0                  No family in related fields/careers                                \n1                  No family in related fields/careers                                \n2    1 person in my immediate family (parent/legal ...                                \n3                  No family in related fields/careers                                \n4    1 person in my immediate family (parent/legal ...                                \n..                                                 ...                                \n255  1 person in my immediate family (parent/legal ...                                \n256  1 person in my immediate family (parent/legal ...                                \n257                No family in related fields/careers                                \n258                No family in related fields/careers                                \n259  1 person in my immediate family (parent/legal ...                                \n\n    Do you have roommates that are part of your major?  \n0                                                   No  \n1                                                  Yes  \n2                                                   No  \n3                                                   No  \n4                                                   No  \n..                                                 ...  \n255                                                 No  \n256                                                 No  \n257                                                 No  \n258                                                 No  \n259                                                Yes  \n\n[260 rows x 14 columns]",
      "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>Timestamp</th>\n      <th>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>What are your career plans right after graduation?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have family members who have careers related to your career aspirations?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2/9/2024 20:12:14</td>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>House</td>\n      <td>6</td>\n      <td>2.73</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2/9/2024 20:16:34</td>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.70</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2/9/2024 20:18:55</td>\n      <td>Junior</td>\n      <td>23+</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>3.75</td>\n      <td>If no job go to graduate school</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2/9/2024 20:24:00</td>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.81</td>\n      <td>Not Sure Yet</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2/9/2024 20:26:16</td>\n      <td>Graduate</td>\n      <td>22</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.23</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>Yes</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>2/14/2024 19:46:28</td>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>5</td>\n      <td>4.00</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>2/15/2024 0:28:38</td>\n      <td>NaN</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.50</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>2/15/2024 8:33:45</td>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>3.70</td>\n      <td>Attend Grad School</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Off-campus</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>2/15/2024 16:10:40</td>\n      <td>Sophomore</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.00</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>2/15/2024 16:14:11</td>\n      <td>Sophomore</td>\n      <td>18</td>\n      <td>Friends</td>\n      <td>Dorm</td>\n      <td>3</td>\n      <td>4.00</td>\n      <td>Attend Grad School</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>260 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fixes empty values\n",
    "df['Do you currently work?'] = df['Do you currently work?'].fillna('No')\n",
    "\n",
    "# Replaces custom text answers with appropriate values\n",
    "df['How many people live in your household?'] = (df['How many people live in your household?']\n",
    "                                                 .fillna(0)\n",
    "                                                 .replace('4 in total', '4')\n",
    "                                                 .replace('4 (Including me)', '4')\n",
    "                                                 .replace('at school 4 including me ', '4')\n",
    "                                                 .replace('3 excluding me', '4')\n",
    "                                                 .replace('5 including me', '5')\n",
    "                                                 .replace('North District 4 bed 2 bath', '4')\n",
    "                                                 .replace('3 (room), 8 (hall), ~70 (building)', '3')\n",
    "                                                 .astype(int))\n",
    "df['Who do you live with?'] = df['Who do you live with?'].replace('Family, Friends', 'Both').replace(\n",
    "    'Family, Friends, Both', 'Both')\n",
    "df['Do you currently live in a house, apartment, or dorm?'] = (\n",
    "    df['Do you currently live in a house, apartment, or dorm?']\n",
    "    .replace('house (renting)', 'House'))\n",
    "\n",
    "df.loc[df['What was your GPA your very first quarter at UCR?'].str.contains(\n",
    "    \"I am not sure|idk|I don't know|This is my first quarter|i don't rem|not sure|I never checked. |I dont know\") == True, 'What was your GPA your very first quarter at UCR?'] = np.nan\n",
    "df['What was your GPA your very first quarter at UCR?'] = (\n",
    "    df['What was your GPA your very first quarter at UCR?']\n",
    "    .replace('Idk, I think 3.2 or something along those lines', '3.2')\n",
    "    .replace('2.8?', '2.8')\n",
    "    .replace('3 point something', '3.0')\n",
    "    .replace('3.67 I think', '3.67')\n",
    "    .replace('3.0?', '3.0')\n",
    "    .replace('about 3.0', '3.0')\n",
    "    .astype(np.float64))\n",
    "# Normalizes non-applicable answers\n",
    "df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\n",
    "df.loc[df['Do you currently work?'] == 'No', 'Do you work in a department related to your major?'] = np.nan\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "285236650ff590d8",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:39.430868Z",
     "start_time": "2024-02-24T06:34:39.423750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "              Timestamp What is your current class standing?  \\\n0     2/9/2024 20:12:14                               Senior   \n4     2/9/2024 20:26:16                             Graduate   \n8     2/9/2024 22:02:49                               Junior   \n9     2/9/2024 22:08:43                               Senior   \n13    2/9/2024 22:15:13                               Junior   \n..                  ...                                  ...   \n246  2/13/2024 19:37:02                             Graduate   \n247  2/13/2024 21:39:14                               Senior   \n252   2/14/2024 9:48:12                               Junior   \n255  2/14/2024 19:46:28                               Junior   \n258  2/15/2024 16:10:40                            Sophomore   \n\n    What is your age? Who do you live with?  \\\n0                 23+               Neither   \n4                  22               Neither   \n8                  20               Friends   \n9                  22                Family   \n13                 21                Family   \n..                ...                   ...   \n246               23+                Family   \n247                21               Friends   \n252                20                Family   \n255                21               Friends   \n258                21                Family   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n0                                                House      \n4                                            Apartment      \n8                                                House      \n9                                                House      \n13                                           Apartment      \n..                                                 ...      \n246                                              House      \n247                                          Apartment      \n252                                              House      \n255                                              House      \n258                                          Apartment      \n\n     How many people live in your household?  \\\n0                                          6   \n4                                          1   \n8                                          6   \n9                                          5   \n13                                         4   \n..                                       ...   \n246                                        2   \n247                                        3   \n252                                        5   \n255                                        5   \n258                                        4   \n\n     What was your GPA your very first quarter at UCR?  \\\n0                                                 2.73   \n4                                                 3.23   \n8                                                 3.40   \n9                                                  NaN   \n13                                                3.50   \n..                                                 ...   \n246                                               4.00   \n247                                               3.60   \n252                                               3.50   \n255                                               4.00   \n258                                               3.00   \n\n    What are your career plans right after graduation? Do you currently work?  \\\n0                            Get into the Job Industry                    Yes   \n4                            Get into the Job Industry                    Yes   \n8                            Get into the Job Industry                    Yes   \n9                                         Not Sure Yet                    Yes   \n13                                  Attend Grad School                    Yes   \n..                                                 ...                    ...   \n246                          Get into the Job Industry                    Yes   \n247                          Get into the Job Industry                    Yes   \n252                          Get into the Job Industry                    Yes   \n255                          Get into the Job Industry                    Yes   \n258                          Get into the Job Industry                    Yes   \n\n    How many hours do you work per week on average?  \\\n0                                            5 - 10   \n4                                           10 - 20   \n8                                           10 - 20   \n9                                             1 - 5   \n13                                          10 - 20   \n..                                              ...   \n246                                         10 - 20   \n247                                         20 - 40   \n252                                         20 - 40   \n255                                         10 - 20   \n258                                          5 - 10   \n\n    Do you work on or off campus?  \\\n0                      Off-campus   \n4                      Off-campus   \n8                       On-campus   \n9                       On-campus   \n13                     Off-campus   \n..                            ...   \n246                     On-campus   \n247                    Off-campus   \n252                    Off-campus   \n255                     On-campus   \n258                     On-campus   \n\n    Do you work in a department related to your major?  \\\n0                                                   No   \n4                                                  Yes   \n8                                                   No   \n9                                                   No   \n13                                                  No   \n..                                                 ...   \n246                                                Yes   \n247                                                 No   \n252                                                 No   \n255                                                 No   \n258                                                 No   \n\n    Do you have family members who have careers related to your career aspirations?  \\\n0                  No family in related fields/careers                                \n4    1 person in my immediate family (parent/legal ...                                \n8                  No family in related fields/careers                                \n9                  No family in related fields/careers                                \n13            Extended family (Aunts, uncles, cousins)                                \n..                                                 ...                                \n246  1 person in my immediate family (parent/legal ...                                \n247                No family in related fields/careers                                \n252                No family in related fields/careers                                \n255  1 person in my immediate family (parent/legal ...                                \n258                No family in related fields/careers                                \n\n    Do you have roommates that are part of your major?  \n0                                                   No  \n4                                                   No  \n8                                                   No  \n9                                                   No  \n13                                                  No  \n..                                                 ...  \n246                                                 No  \n247                                                Yes  \n252                                                 No  \n255                                                 No  \n258                                                 No  \n\n[77 rows x 14 columns]",
      "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>Timestamp</th>\n      <th>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>What are your career plans right after graduation?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have family members who have careers related to your career aspirations?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2/9/2024 20:12:14</td>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>House</td>\n      <td>6</td>\n      <td>2.73</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2/9/2024 20:26:16</td>\n      <td>Graduate</td>\n      <td>22</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.23</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>Yes</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2/9/2024 22:02:49</td>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>6</td>\n      <td>3.40</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2/9/2024 22:08:43</td>\n      <td>Senior</td>\n      <td>22</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>Not Sure Yet</td>\n      <td>Yes</td>\n      <td>1 - 5</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2/9/2024 22:15:13</td>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.50</td>\n      <td>Attend Grad School</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>Extended family (Aunts, uncles, cousins)</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>246</th>\n      <td>2/13/2024 19:37:02</td>\n      <td>Graduate</td>\n      <td>23+</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>2</td>\n      <td>4.00</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>Yes</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>2/13/2024 21:39:14</td>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>Apartment</td>\n      <td>3</td>\n      <td>3.60</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>20 - 40</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>2/14/2024 9:48:12</td>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>3.50</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>20 - 40</td>\n      <td>Off-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>2/14/2024 19:46:28</td>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>5</td>\n      <td>4.00</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>10 - 20</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>2/15/2024 16:10:40</td>\n      <td>Sophomore</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.00</td>\n      <td>Get into the Job Industry</td>\n      <td>Yes</td>\n      <td>5 - 10</td>\n      <td>On-campus</td>\n      <td>No</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n<p>77 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Working DataFrame\n",
    "w_df = df[df['Do you currently work?'] == 'Yes']\n",
    "# Not working DataFrame\n",
    "nw_df = df[df['Do you currently work?'] == 'No']\n",
    "w_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6516c926e6efd1c3",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:39.445408Z",
     "start_time": "2024-02-24T06:34:39.434297Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "              Timestamp What is your current class standing?  \\\n1     2/9/2024 20:16:34                               Junior   \n2     2/9/2024 20:18:55                               Junior   \n3     2/9/2024 20:24:00                               Senior   \n5     2/9/2024 20:45:09                               Junior   \n6     2/9/2024 21:55:59                            Sophomore   \n..                  ...                                  ...   \n253  2/14/2024 13:45:45                               Senior   \n254  2/14/2024 16:26:06                               Junior   \n256   2/15/2024 0:28:38                                  NaN   \n257   2/15/2024 8:33:45                               Senior   \n259  2/15/2024 16:14:11                            Sophomore   \n\n    What is your age? Who do you live with?  \\\n1                  20                  Both   \n2                 23+               Friends   \n3                 23+               Neither   \n5                  21                  Both   \n6                  19               Friends   \n..                ...                   ...   \n253                21                Family   \n254                19                Family   \n256                21                Family   \n257                21                Family   \n259                18               Friends   \n\n    Do you currently live in a house, apartment, or dorm?  \\\n1                                            Apartment      \n2                                                House      \n3                                            Apartment      \n5                                            Apartment      \n6                                            Apartment      \n..                                                 ...      \n253                                              House      \n254                                              House      \n256                                          Apartment      \n257                                              House      \n259                                               Dorm      \n\n     How many people live in your household?  \\\n1                                          4   \n2                                          4   \n3                                          1   \n5                                          4   \n6                                          4   \n..                                       ...   \n253                                        6   \n254                                        5   \n256                                        4   \n257                                        9   \n259                                        3   \n\n     What was your GPA your very first quarter at UCR?  \\\n1                                                 3.70   \n2                                                 3.75   \n3                                                 3.81   \n5                                                 4.00   \n6                                                 4.00   \n..                                                 ...   \n253                                               4.00   \n254                                               3.80   \n256                                               3.50   \n257                                               3.70   \n259                                               4.00   \n\n    What are your career plans right after graduation? Do you currently work?  \\\n1                            Get into the Job Industry                     No   \n2                      If no job go to graduate school                     No   \n3                                         Not Sure Yet                     No   \n5                            Get into the Job Industry                     No   \n6                            Get into the Job Industry                     No   \n..                                                 ...                    ...   \n253                          Get into the Job Industry                     No   \n254                          Get into the Job Industry                     No   \n256                          Get into the Job Industry                     No   \n257                                 Attend Grad School                     No   \n259                                 Attend Grad School                     No   \n\n    How many hours do you work per week on average?  \\\n1                                                 0   \n2                                                 0   \n3                                                 0   \n5                                                 0   \n6                                                 0   \n..                                              ...   \n253                                               0   \n254                                               0   \n256                                               0   \n257                                               0   \n259                                               0   \n\n    Do you work on or off campus?  \\\n1                             NaN   \n2                             NaN   \n3                             NaN   \n5                             NaN   \n6                             NaN   \n..                            ...   \n253                           NaN   \n254                           NaN   \n256                           NaN   \n257                    Off-campus   \n259                           NaN   \n\n    Do you work in a department related to your major?  \\\n1                                                  NaN   \n2                                                  NaN   \n3                                                  NaN   \n5                                                  NaN   \n6                                                  NaN   \n..                                                 ...   \n253                                                NaN   \n254                                                NaN   \n256                                                NaN   \n257                                                NaN   \n259                                                NaN   \n\n    Do you have family members who have careers related to your career aspirations?  \\\n1                  No family in related fields/careers                                \n2    1 person in my immediate family (parent/legal ...                                \n3                  No family in related fields/careers                                \n5    2 or more in my immediate family (parents/lega...                                \n6                                                  NaN                                \n..                                                 ...                                \n253           Extended family (Aunts, uncles, cousins)                                \n254                No family in related fields/careers                                \n256  1 person in my immediate family (parent/legal ...                                \n257                No family in related fields/careers                                \n259  1 person in my immediate family (parent/legal ...                                \n\n    Do you have roommates that are part of your major?  \n1                                                  Yes  \n2                                                   No  \n3                                                   No  \n5                                                   No  \n6                                                   No  \n..                                                 ...  \n253                                                 No  \n254                                                Yes  \n256                                                 No  \n257                                                 No  \n259                                                Yes  \n\n[183 rows x 14 columns]",
      "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>Timestamp</th>\n      <th>What is your current class standing?</th>\n      <th>What is your age?</th>\n      <th>Who do you live with?</th>\n      <th>Do you currently live in a house, apartment, or dorm?</th>\n      <th>How many people live in your household?</th>\n      <th>What was your GPA your very first quarter at UCR?</th>\n      <th>What are your career plans right after graduation?</th>\n      <th>Do you currently work?</th>\n      <th>How many hours do you work per week on average?</th>\n      <th>Do you work on or off campus?</th>\n      <th>Do you work in a department related to your major?</th>\n      <th>Do you have family members who have careers related to your career aspirations?</th>\n      <th>Do you have roommates that are part of your major?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>2/9/2024 20:16:34</td>\n      <td>Junior</td>\n      <td>20</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.70</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2/9/2024 20:18:55</td>\n      <td>Junior</td>\n      <td>23+</td>\n      <td>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>3.75</td>\n      <td>If no job go to graduate school</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2/9/2024 20:24:00</td>\n      <td>Senior</td>\n      <td>23+</td>\n      <td>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>3.81</td>\n      <td>Not Sure Yet</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2/9/2024 20:45:09</td>\n      <td>Junior</td>\n      <td>21</td>\n      <td>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>4.00</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2 or more in my immediate family (parents/lega...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2/9/2024 21:55:59</td>\n      <td>Sophomore</td>\n      <td>19</td>\n      <td>Friends</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>4.00</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>253</th>\n      <td>2/14/2024 13:45:45</td>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>6</td>\n      <td>4.00</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Extended family (Aunts, uncles, cousins)</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>2/14/2024 16:26:06</td>\n      <td>Junior</td>\n      <td>19</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>3.80</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>Yes</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>2/15/2024 0:28:38</td>\n      <td>NaN</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>3.50</td>\n      <td>Get into the Job Industry</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>2/15/2024 8:33:45</td>\n      <td>Senior</td>\n      <td>21</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>3.70</td>\n      <td>Attend Grad School</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Off-campus</td>\n      <td>NaN</td>\n      <td>No family in related fields/careers</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>2/15/2024 16:14:11</td>\n      <td>Sophomore</td>\n      <td>18</td>\n      <td>Friends</td>\n      <td>Dorm</td>\n      <td>3</td>\n      <td>4.00</td>\n      <td>Attend Grad School</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1 person in my immediate family (parent/legal ...</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>183 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nw_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efd20d58edbb05d",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6deea60d8966fa15",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:39.625934Z",
     "start_time": "2024-02-24T06:34:39.447091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of people who work and don't work\n",
    "work_counts = df['Do you currently work?'].value_counts()\n",
    "\n",
    "# Plotting a pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(work_counts, labels=work_counts.index, autopct='%1.1f%%', startangle=90, colors=['lightblue', 'lightcoral'])\n",
    "plt.title('Distribution of People Who Work and Don\\'t Work')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "The majority of student respondents (70.4%) do **not** work while attending school."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5dbc734405a3858d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Do you currently live in a house, apartment, or dorm?  Apartment      Dorm  \\\nDo you currently work?                                                       \nNo                                                      0.500000  0.131868   \nYes                                                     0.493506  0.064935   \n\nDo you currently live in a house, apartment, or dorm?     House      Room  \nDo you currently work?                                                     \nNo                                                     0.362637  0.005495  \nYes                                                    0.441558  0.000000  ",
      "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>Do you currently live in a house, apartment, or dorm?</th>\n      <th>Apartment</th>\n      <th>Dorm</th>\n      <th>House</th>\n      <th>Room</th>\n    </tr>\n    <tr>\n      <th>Do you currently work?</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>No</th>\n      <td>0.500000</td>\n      <td>0.131868</td>\n      <td>0.362637</td>\n      <td>0.005495</td>\n    </tr>\n    <tr>\n      <th>Yes</th>\n      <td>0.493506</td>\n      <td>0.064935</td>\n      <td>0.441558</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 2 Axes>",
      "image/png": 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4M4tZqhvF2NhYrVq1SpcuXdJLL72kwMDA9KwLAAAATpaqoeetW7cqJCRE27Zt05EjRxQaGqpJkyald20AAAAPhZvFeY+MLFWN4qRJk9StWzctXLhQn376qSIjI/XFF1+ob9++OnPmjCQpKSlJp06dStdiAQAAXNn169c1cOBABQYGKigoSJGRkXddd8WKFXrppZdUpkwZtW7dWvv373d4f6lqFOPi4tSkSRPb84oVK2rOnDmKjo5WzZo19cYbb+jcuXOqXbu2wwUAAAAgdSZMmKADBw5ozpw5Gjp0qMLDw7Vu3boU6+3cuVODBg1Sjx49tHr1avn7+6tr1666fPmyQ/tL1TmKTz/9tL755ht16NDBtszPz08rV67U8ePHlZCQoBw5cmjOnDkO7RwAACAjeBQms1y5ckVLlizRJ598Ij8/P/n5+enQoUNasGCB6tWrZ7dufHy8evTooaZNm0qSevbsqcjISMXGxqpMmTKp3meqGsU333xTffr00Xfffad+/fqpRIkSkm4d1Geeeca2XsWKFVO9YwAAAKRedHS0EhMT5e/vb1sWEBCgGTNmKCkpSW5u/zdQXL9+fdufr127ptmzZytPnjzy8fFxaJ+pahT/85//aOHChVq6dKmSkpIc2gEAAEBG58xAMSEhQQkJCXbLPDw85OHhYbcsPj5euXLlslueN29eXb9+XRcuXFDu3LlTbHvbtm3q1KmTrFar3n//fT355JMO1Zbqy+OULl1apUuX1qVLl+66zqZNm1SnTh2HCgAAAHBlM2fOVHh4uN2yXr16qXfv3nbLrl69mqJ5TH5+Z6OZ7Pnnn1dUVJS++eYb9e/fX08//bTKlSuX6tocvuB2u3btNGvWLLuu9fjx4xoxYoS2bdumAwcOOLpJAAAAl9WtWzd17NjRbtmdDaEkeXp6pmgIk597eXkZt503b17lzZtXJUuW1L59+7Rw4UKHGkWHb+FXpEgRtWnTRqdOnVJCQoI+/PBDNWzYUNeuXdPSpUsd3RwAAIDTWZz4n4eHh7JmzWr3MDWKBQoU0Pnz55WYmGhbFh8fLy8vL2XPnt1u3f379+vXX3+1W+bj46Pz5887dFwcThQ//PBDjR49Wq1bt5aHh4eSkpI0fvx4u5MmAQAAkLZKliwpd3d37d2713aHvF27dql06dJ2E1kk6csvv9TJkyc1a9Ys27Jff/1VL7zwgkP7dDhRtFgsGjx4sNq3b6/Tp09rzJgxNIkAAOCR9ijcmSVLliwKCQnRsGHDtH//fm3atEmRkZFq3769pFvp4rVr1yRJrVq10k8//aQ5c+bo6NGjmjp1qvbv36/Q0FCHjkuqEsXg4OC7Xl+oW7duyps3r+355s2bHSoAAAAAqTNgwAANGzZMHTp0UNasWdW7d2/VrVtXkhQUFKSxY8eqefPm8vPzU3h4uCZPnqxJkybp+eef16xZs1SgQAGH9peqRvHOWTcAAACPk0fhgtvSrVRx/PjxGj9+fIrXYmJi7J7XqlVLtWrV+lf7S1Wj2KxZM9ufe/Toof/+978OX7ARAAAAjxaHz1HcvXu33N0dngMDAACAR4zDHV/btm319ttvq3Xr1ipUqJA8PT3tXq9QoUKaFQcAAPAwPCIjzw+dw43iRx99JEl67733UrxmsVh08ODBf18VAAAAnM7hRjE6Ojo96gAAAHAaNyJFowc62fDatWtasWKFYmNjdfPmTRUrVkwNGjRQzpw507g8AAAAOIvDk1l+//131a1bVxERETp16pROnTqlmTNnqn79+jp8+HB61AgAAAAncDhRHD16tKpVq6aRI0faZj8nJiZq8ODBGjNmjCIjI9O8SAAAgPTEyLOZw4ni3r171bVrV7tL5Li7u6tr167as2dPmhYHAAAA53G4UcyXL5+OHTuWYvmxY8f05JNPpklRAAAAD5PFYnHaIyNzeOi5devWGjx4sN566y2VKVNGkrRv3z5NnTpVLVu2TPMCAQAA4BwON4qdO3fW1atX9f777+vixYuSpLx58yo0NFSdOnVK8wIBAADSWwYP9pzG4UbxxIkT6t27t3r37q2zZ8/K09NTWbNmTY/aAAAA4EQON4r169dX4cKF9eKLL+rFF19UpUqV0qMuAAAAOJnDjeKOHTu0Y8cObdu2Te+//76OHTumgIAAW+P43HPPpUedAAAA6YY7s5g53Cg+8cQTqlmzpmrWrClJiouL0/Tp0zVhwgRNmDCBez0DAAA8JhxuFE+ePKndu3dr9+7d2rVrl+Li4uTt7a1WrVopMDAwPWoEAABIV+SJZg43irVr15abm5uqV6+ut956S4GBgcqRI0d61AYAAAAncrhRnDBhgnbu3Kmff/5ZAwYMULly5RQYGKjy5curTJky8vDwSI86AQAA8JA53Cg2adJETZo0kSSdPXtWO3fu1JYtWxQeHi6LxaJ9+/aleZEAAADpKaPfIcVZHG4UJenSpUvatWuXtm/fru3btysmJkYlS5ZUUFBQWtcHAAAAJ3G4UXz55ZcVExOjvHnzqlq1aurcubOqVq2qnDlzpkN5AAAA6c+NQNHI4UaxcePGmjBhgnx8fNKjHgAAAGQQDjeKoaGh6VAGAACA83COopmbswsAAABAxkSjCAAAACOHG8Xjx4+nRx0AAABOY7E475GROdwo1qtXTy1bttTs2bN15syZ9KgJAAAAGYDDjeL333+v5s2b6+uvv1bt2rX12muv6fPPP9e5c+fSoz4AAIB0Z7FYnPbIyBxuFHPnzq02bdpo7ty52rJlixo2bKjvvvtOderUUefOnbVs2TJdvXo1PWoFAADAQ/SvJrPEx8crPj5ep0+fVlJSkp588kktXrxYNWvW1IYNG9KqRgAAADiBw9dRPHjwoNatW6d169bp5MmTqlq1qjp27Kg6deroySeflCR99NFHGjJkiOrWrZvmBQMAAKQ17sxi5nCj2Lx5cwUEBCg0NFT16tVTrly5UqwTEBDA7GgAAIBHnMON4jfffKOnnnrqnutUqlRJlSpVeuCiAAAAHqaMPqnEWVLVKIaHh6d6g7169XrgYgAAAJBxpKpR3L59e6o2RjcOAAAeRXQwZqlqFOfNm2f786lTp/TUU0/Jzc1+wvTNmzcVHR2dttUBAADAaRy+PE7t2rV14cKFFMtPnDihtm3bpkVNAAAAyABSlSguWbJEM2bMkCRZrVa9/PLLKRLFv//+Wz4+PmlfIQAAQDpz4/Q5o1Q1iiEhIcqcObOSkpI0cOBAdezYUdmyZbO9brFYlCVLFlWuXDndCgUAAMDDlapGMXPmzAoJCZEkPf300ypfvrzc3R2+sg4AAECGRKBo5nC3V7FiRW3btk2//PKLbty4IavVavc6l8cBAAB4PDjcKI4bN05z585ViRIlbLfsS8blcQAAAB4fDjeKS5cu1bhx49SkSZP0qAcAAOChI+wyc/jyOJkyZVKZMmXSoxYAAABkIA43iq+++qqmTZumK1eupEc9AAAAD53F4rxHRubw0POOHTu0Z88erVu3Tnny5FHmzJntXt+8eXOaFQcAAADncbhRbN68uZo3b54etQAAACADcbhRbNasme3PFy9eVLZs2WSxWDgJFAAAPLK4M4uZw+coWq1WRUREqFKlSqpSpYpOnjypfv366b333lNCQkJ61AgAAAAncLhRnD59ulasWKFx48bJw8ND0q2U8ccff9SECRPSvEAAAID0xmQWM4cbxWXLlmnEiBGqVauWbbi5WrVqGj9+vNauXZvmBQIAAMA5HD5H8ezZs8qfP3+K5dmzZ+eSOQAA4JHEXAszhxPFypUra9asWXbLLl26pMmTJ6tSpUppVhgAAACcy+FGcdiwYfrtt99UrVo1Xb9+XT169FCNGjV08uRJDR48OD1qBAAAgBM4PPScPXt2ffnll9q2bZvi4uKUmJgob29vBQUFyc3N4b4zfWTKfP918NgonCuLs0vAQ/THwaPOLgEPEZcswcOSQTqYDMfhRrFRo0YKDw9XlSpVVKVKlfSoCQAAABmAw42im5ubbty4kR61AAAAOAWTWcwcbhRr1qypjh07qlatWipcuLDtWorJevXqlWbFAQAAwHkcbhRjYmLk5+env/76S3/99Zfda3TjAAAAjw+HG8VWrVqpWrVqypUrV3rUAwAA8NC5kXUZOTzJZ/jw4bpw4UI6lAIAAICMxOFGsVKlSlq5cqUSEhLSox4AAICHzs3ivEdG9kC38Pvoo480Y8YM5c6dW56ennavb968Oc2KAwAAgPM43Ci+8soreuWVV9KjFgAAAKdgQq6Zw41is2bN0qMOAAAAZDAON4rt2rW7Z9c9d+7cf1UQAAAAMgaHG8VKlSrZPU9MTNTx48e1ZcsWde/ePc0KAwAAeFgy+qQSZ3G4UbzbnVeioqK0YcMGde7c+V8XBQAAAOdz+PI4d1OhQgVt27YtrTYHAADw0FgszntkZA4niqdOnUqx7PLly5o1a5YKFy6cJkUBAADA+RxuFIODg2WxWGS1Wm2TWqxWqwoWLKgxY8akeYEAAABwDocbxTsvqG2xWJQ5c2blzZuXaxABAIBHkhs9jJHD5ygWLlxY3377rfbs2aPChQurUKFCGj58uBYuXJge9QEAAMBJHG4Up0yZooiICD3xxBO2ZRUrVtRHH32k6dOnp2lxAAAAD4ObEx8ZmcP1LV26VB988IGCg4Nty9q3b6/3339fixYtStPiAAAA4DwOn6N49epVZc2aNcXyXLly6Z9//kmTogAAAB4mTlE0czhRfPHFFzV69Gi7y+ScOXNG48ePV1BQUJoWBwAAAOdxuFF87733dOPGDdWuXVuVK1dW5cqVVbNmTSUlJWno0KHpUSMAAACcwOGh59y5c2vhwoWKiYnRkSNH5O7urmeffVbPPfdcetQHAACQ7rg8jpnDjWIyX19f+fr6pmUtAAAAyEAeuFEEAAB4XBAommX0y/cAAADASWgUAQAAYOTw0PPPP/98z9crVKjwwMUAAAA4gxtDz0YON4rt2rUzLvfw8FC+fPm0efPmf10UAAAAnM/hRjE6Otru+c2bN3Xs2DGNHDlSjRs3TrPCAAAAHhYuj2P2r89RzJQpk7y9vdW/f399+OGHaVETAAAAMoA0uzzO2bNn9ffff6fV5gAAAB4aAkUzhxvFAQMGpFh2+fJlbd26VfXq1UuTogAAAOB8aZIo5syZU2FhYWratGlabA4AAAAZgMON4tixY9OjDgAAAKfh8jhmD5Qobtq0SZ9++qni4uJ08+ZNeXt767XXXlNISEgalwcAAABncbhRXLhwocaPH6/XXntNr7/+upKSkrR7924NHz5cN27cUMuWLdOjTgAAgHRjEZGiicON4qeffqqhQ4fapYd16tTR888/rxkzZtAoAgAAPCYcvo7i2bNnVa5cuRTL/f399eeff6ZFTQAAAMgAHG4US5YsqeXLl6dYvmzZMj333HNpURMAAMBD5WZx3iMjc3jouV+/fgoNDdX27dtVtmxZSdLevXsVHR2tGTNmpHmBAAAAcA6HG0V/f39FRUVp8eLFio2NlaenpypUqKApU6aoYMGC6VEjAABAusroyZ6zONwoLl26VC+99JLxDi0AAAB4fDh8juLs2bNVtWpVde/eXatXr9bVq1fToy4AAICHxmKxOO2RkTncKK5cuVLLli2Tn5+fIiIiVLVqVfXp00cbN25UQkJCetQIAAAAJ3C4UZQkHx8f9erVS6tWrdKXX36pZ555Rv369VPVqlU1YMAA7d69O63rBAAAwEP2QLfwk6QzZ85o/fr12rBhg/bu3asyZcqoQYMGio+PV/fu3fXKK6/ov//9b1rWCgAAkC6YzGLmcKM4e/ZsrV+/Xvv27VPx4sXVsGFDTZw40W7G87PPPqsRI0bQKAIAADzCHG4Uv/jiCzVs2FCjRo2Sj4+PcZ0XXnhBgwcP/tfFAQAAPAwZfE6J0zjcKK5fv/6+6/j6+srX1/eBCgIAAEDG8ECTWQAAAPD4e+DJLAAAAI8LN8aejUgUAQAAYPTAieLRo0cVGxurpKQkeXt767nnnkvLugAAAB4aLo9j5nCj+Pfff2vAgAHavHmzcuTIoZs3b+ry5cuqUKGCpk+frmzZsqVHnQAAAHjIHB56HjVqlE6fPq01a9Zo+/bt2rlzp1auXKkrV65o7Nix6VEjAABAurJYnPdwxPXr1zVw4EAFBgYqKChIkZGRd13322+/VdOmTeXv76/GjRtr8+bNDh8XhxvFr7/+WsOGDVOxYsVsy5577jm99957D1QAAAAAUmfChAk6cOCA5syZo6FDhyo8PFzr1q1LsV50dLR69eqll19+WcuXL1fr1q311ltvKTo62qH9OTz07OnpKTe3lP2lxWLRzZs3Hd0cAAAAUuHKlStasmSJPvnkE/n5+cnPz0+HDh3SggULVK9ePbt1V61apcqVK6t9+/aSpKJFi+rrr7/W2rVrVaJEiVTv0+FGMTg4WMOHD9f777+vZ555RtKtiS2jRo1SjRo1HN0cAACA07nJebNZEhISlJCQYLfMw8NDHh4edsuio6OVmJgof39/27KAgADNmDFDSUlJdkFes2bNdOPGjRT7+ueffxyqzeGh5379+snT01N169ZVpUqVVKlSJdWvX185cuTQkCFDHN0cAACAS5s5c6YCAgLsHjNnzkyxXnx8vHLlymXXQObNm1fXr1/XhQsX7Nb18fGxSw4PHTqkbdu2qUqVKg7V5nCimD17ds2bN08xMTGKjY2Vp6envL297c5ZBAAAeJQ483rb3bp1U8eOHe2W3ZkmStLVq1dTLE9+fmciebtz586pd+/eKl++vGrXru1QbQ43isePH9fhw4d1+fJlZc2aVc8//7wKFy7s6GYAAAAg8zCziaenZ4qGMPm5l5eX8T3/+9//1LFjR1mtVk2dOtU4z+ReUt0obtu2TWPHjtWhQ4dktVptyy0Wi/z8/NS/f38FBgY6tHMAAACkToECBXT+/HklJibK3f1WCxcfHy8vLy9lz549xfpnzpyxTWaZO3eucufO7fA+U9VW/vDDD+rSpYtKlCihefPm6aefftKvv/6q7du3a/bs2SpWrJg6duyoPXv2OFwAAACAs7lZnPdIrZIlS8rd3V179+61Ldu1a5dKly6dIim8cuWKunTpIjc3N82fP18FChR4oOOSqkRx+vTpCg0NVb9+/eyW58iRwzahJUeOHIqIiNDHH3/8QIUAAADg7rJkyaKQkBANGzZMY8aM0V9//aXIyEjbDU/i4+OVLVs2eXl5aebMmTp27JjmzZtne026NUTtyF30UpUoRkdHq1mzZvdcp2XLlvrtt99SvWMAAICMws1icdrDEQMGDJCfn586dOig4cOHq3fv3qpbt64kKSgoSGvWrJEkrV+/XteuXVPLli0VFBRke4wePdqh/aUqUbx27Zpy5Mhxz3Vy5cqlc+fOObRzAAAApF6WLFk0fvx4jR8/PsVrMTExtj+b7tbyIFKVKFqt1vvOkrFYLHaTXAAAAPBoS/Ws57Vr1ypr1qx3fd3RK30DAABkFM68jmJGlqpGsVChQoqMjLzvegULFvzXBQEAACBjSFWj+PXXX6d3HQAAAE7j6KQSV+HwvZ4BAADgGhy+hR8AAMDjhkDRjEQRAAAARjSKAAAAMGLoGQAAuDySMzOOCwAAAIxIFAEAgMuzMJvFiEQRAAAARjSKAAAAMGLoGQAAuDwGns1IFAEAAGBEoggAAFwe93o2I1EEAACAEYkiAABweeSJZiSKAAAAMKJRBAAAgBFDzwAAwOUxl8WMRBEAAABGJIoAAMDlca9nMxJFAAAAGNEoAgAAwIihZwAA4PJIzsw4LgAAADAiUQQAAC6PySxmJIoAAAAwIlEEAAAujzzRjEQRAAAARjSKAAAAMGLoGQAAuDwms5iRKAIAAMCIRBEAALg8kjMzjgsAAACMaBQBAABgxNAzAABweUxmMSNRBAAAgBGJIgAAcHnkiWYkigAAADAiUQQAAC6PUxTNSBQBAABgRKMIAAAAI4aeAQCAy3NjOosRiSIAAACMSBQBAIDLYzKLGYkiAAAAjGgUAQAAYMTQMwAAcHkWJrMYkSgCAADAiEQRAAC4PCazmJEoAgAAwIhEEQAAuDwuuG1GoggAAAAjGkUAAAAYMfQMAABcHpNZzEgUAQAAYESiCAAAXB6JohmJIgAAAIxoFAEAAGDE0DMAAHB53OvZjEQRAAAARiSKAADA5bkRKBqRKAIAAMCIRBEAALg8zlE0I1EEAACAEY0iAAAAjBh6BgAALo87s5iRKAIAAMCIRBEAALg8JrOYkSgCAADAiEYRAAAARgw9AwAAl8edWcxIFAEAAGBEoggAAFwek1nMMmSieO7cOVmtVmeXAQAA4NKc3iieOXNGb7/9tg4ePKjr16/rtddeU7Vq1RQcHKzo6GhnlwcAAOCynN4oDhs2TOfOnVPOnDkVFRWl33//XQsXLlRwcLBGjhzp7PIAAIALsFic98jInH6O4k8//aSoqCgVLFhQmzZtUu3atVW2bFnlzp1bjRo1cnZ5AAAALsvpiaKnp6euX7+uixcvavv27apZs6Yk6cSJE8qRI4dziwMAAC7B4sRHRub0RLFOnTrq06ePvLy8lCNHDtWsWVNr1qzRmDFj1KxZM2eXBwAA4LKc3igOGzZM8+fP18mTJ9WqVSt5enoqISFBb7zxhl599VVnlwcAAFyAW0Y/WdBJnN4ouru7KzQ0VJJ08eJFJSUlqWnTprLwFwYAAOBUTj9H0Wq1KiIiQpUqVVKVKlV08uRJ9evXT++9954SEhKcXR4AAIDLcnqjOH36dK1YsULjxo2Th4eHJKlZs2b68ccfNWHCBCdXBwAAXAGTWcyc3iguW7ZMI0aMUK1atWzDzdWqVdP48eO1du1aJ1cHAADgupx+juLZs2eVP3/+FMuzZ8+uK1euOKEiAADgcjJ6tOckTk8UK1eurFmzZtktu3TpkiZPnqxKlSo5qSoAAAA4pVFs27atYmNjJd26PM5vv/2matWq6fr16+rRo4dq1KihkydPavDgwc4oDwAAAHLS0LOnp6dCQkLUqVMn9ezZU19++aW2bdumuLg4JSYmytvbW0FBQXJzc3rgCQAAXICFsWcjpzSKn332mTZs2KBx48Zp7dq1GjZsmKpWraoqVao4oxwAAAAYOG0yS926dVWjRg198skn6tWrl4KDg9WzZ095enrarVeoUCEnVQgAAFwF9/kwc+qsZ09PT/Xq1UslSpRQnz59tHr1attrVqtVFotFBw8edGKFAAAArsupjeLJkyc1YcIEbdy4UY0aNVK3bt3k5eXlzJIAAIALIlA0c0qjeP36dc2YMUOfffaZihQporlz5yowMNAZpQAAAOAunNIovvTSS7p06ZL69Omjdu3aKVOmTM4oAwAAAPfglEYxICBAYWFhxjuyAAAAPHSMPRs5pVGcNGmSM3YLAAAABzj9Xs8AAADOxgW3zbj1CQAAAIxoFAEAAGDE0DMAAHB53JnFjEQRAAAARiSKAADA5REompEoAgAAwIhEEQAAgEjRiEQRAAAARjSKAAAAMGLoGQAAuDzuzGJGoggAAAAjGkUAAODyLBbnPRxx/fp1DRw4UIGBgQoKClJkZOR937Nz507Vrl37gY4LQ88AAACPiAkTJujAgQOaM2eOTp06pbCwMBUqVEj16tUzrh8TE6O33npLnp6eD7Q/EkUAAIBHwJUrV7RkyRINGjRIfn5++s9//qMuXbpowYIFxvUXLlyo1q1bK0+ePA+8TxpFAADg8ixOfKRWdHS0EhMT5e/vb1sWEBCgffv2KSkpKcX63333ncaPH6/Q0FAH9mKPoWcAAAAnSkhIUEJCgt0yDw8PeXh42C2Lj49Xrly57JbnzZtX169f14ULF5Q7d2679T/66CNJUlRU1APXRqIIAADgxEhx5syZCggIsHvMnDkzRYlXr15N0TwmP7+z0UwrJIoAAABO1K1bN3Xs2NFu2Z0NoSR5enqmaAiTn3t5eaVLbTSKAADA5TnzgtumYWaTAgUK6Pz580pMTJS7+60WLj4+Xl5eXsqePXu61MbQMwAAwCOgZMmScnd31969e23Ldu3apdKlS8vNLX1aOhpFAACAR0CWLFkUEhKiYcOGaf/+/dq0aZMiIyPVvn17SbfSxWvXrqXpPmkUAQCAy3tU7swyYMAA+fn5qUOHDho+fLh69+6tunXrSpKCgoK0Zs2atD0uVqvVmqZbzACyBL7t7BLwEP26dqyzS8BD5NdljrNLwEN0fmk3Z5eAh8jLiTMnfjlxyWn7Lv10Vqft+36YzAIAAFye86ayZGwMPQMAAMCIRhEAAABGDD0DAAAw9mxEoggAAAAjEkUAAODynHlnloyMRBEAAABGJIoAAMDlOXrha1dBoggAAAAjGkUAAAAYMfQMAABcHiPPZiSKAAAAMCJRBAAAIFI0IlEEAACAEY0iAAAAjBh6BgAALo87s5iRKAIAAMCIRBEAALg87sxiRqIIAAAAIxJFAADg8ggUzUgUAQAAYESjCAAAACOGngEAABh7NiJRBAAAgBGJIgAAcHlccNuMRBEAAABGNIoAAAAwYugZAAC4PO7MYkaiCAAAACMSRQAA4PIIFM1IFAEAAGBEowgAAAAjhp4BAAAYezYiUQQAAIARiSIAAHB53JnFjEQRAAAARiSKAADA5XHBbTMSRQAAABjRKAIAAMCIoWcAAODyGHk2I1EEAACAEYkiAAAAkaIRiSIAAACMaBQBAABgxNAzAABwedyZxYxEEQAAAEYkigAAwOVxZxYzEkUAAAAYkSgCAACXR6BoRqIIAAAAIxpFAAAAGDH0DAAAXB6TWcxIFAEAAGBEoggAAMB0FiMSRQAAABjRKAIAAMCIoWcAAODymMxiRqIIAAAAIxJFAADg8ggUzUgUAQAAYESiCAAAXB7nKJqRKAIAAMCIRhEAAABGDD0DAACXZ2E6ixGJIgAAAIxIFAEAAAgUjUgUAQAAYESjCAAAACOGngEAgMtj5NmMRBEAAABGJIoAAMDlcWcWMxJFAAAAGJEoAgAAl8cFt81IFAEAAGBEowgAAAAjhp4BAAAYeTYiUQQAAIARiSIAAHB5BIpmJIoAAAAwolEEAACAEUPPAADA5XFnFjMSRQAAABiRKAIAAJfHnVnMSBQBAABgRKIIAABcHucompEoAgAAwIhGEQAAAEY0igAAADCiUQQAAIARk1kAAIDLYzKLGYkiAAAAjGgUAQAAYMTQMwAAcHncmcWMRBEAAABGJIoAAMDlMZnFjEQRAAAARiSKAADA5REompEoAgAAwIhGEQAAAEYMPQMAADD2bESiCAAAACMSRQAA4PK44LYZiSIAAACMaBQBAABgxNAzAABwedyZxYxEEQAAAEYkigAAwOURKJqRKAIAAMCIRhEAAABGDD0DAAAw9mxEoggAAAAjGkUAAODyLE78zxHXr1/XwIEDFRgYqKCgIEVGRt513d9++00tW7ZU2bJl9fLLL+vAgQMOHxcaRQAAgEfEhAkTdODAAc2ZM0dDhw5VeHi41q1bl2K9K1eu6PXXX1dgYKCioqLk7++vbt266cqVKw7tj0YRAAC4PIvFeY/UunLlipYsWaJBgwbJz89P//nPf9SlSxctWLAgxbpr1qyRp6en3n33Xfn4+GjQoEF68sknjU3lvdAoAgAAPAKio6OVmJgof39/27KAgADt27dPSUlJduvu27dPAQEBsvz/TtRisah8+fLau3evQ/ukUQQAAHCihIQEXbp0ye6RkJCQYr34+HjlypVLHh4etmV58+bV9evXdeHChRTr5s+f325Znjx5dPr0aYdqeywvj3N15xRnlwAgnVz9qpuzSwDwGPJyYkc0bdpMhYeH2y3r1auXevfubbfs6tWrdk2iJNvzOxvLu61rakDv5bFsFAEAAB4V3bp1U8eOHe2W3dnkSZKnp2eKRi/5uZeXV6rWvXO9+6FRBAAAcCIPDw9jY3inAgUK6Pz580pMTJS7+60WLj4+Xl5eXsqePXuKdf/3v//ZLfvf//6XYjj6fjhHEQAA4BFQsmRJubu7201I2bVrl0qXLi03N/uWrmzZstqzZ4+sVqskyWq1avfu3SpbtqxD+6RRBAAAeARkyZJFISEhGjZsmPbv369NmzYpMjJS7du3l3QrXbx27ZokqV69evr77781evRoHT58WKNHj9bVq1dVv359h/ZpsSa3mgAAAMjQrl69qmHDhmnDhg3KmjWrOnfurNDQUEmSr6+vxo4dq+bNm0uS9u/fr6FDhyo2Nla+vr4aPny4XnjhBYf2R6MIAAAAI4aeAQAAYESjCAAAACMaRQAAABjRKKajqKgo+fr6asmSJem6n7Nnz2rt2rXpuo+7OX78uLZs2eKUfT/qgoOD5evrK19fX5UoUUL+/v5q3bq1vv/+e2eXhjQSHBysqKioFMujoqIUHBzshIrwMNz+3U7+flesWFHdu3fXn3/+6ezyAIfQKKaj1atX65lnntFXX32Vrvt5//33ndasDRw4UPv373fKvh8HAwcO1A8//KAtW7Zo0aJFKl++vLp166atW7c6uzQA/0Lydzv5+z1lyhQdOnRIYWFhzi4NcAiNYjo5e/astm3bpp49e2rnzp06fvx4uu2LieuPrmzZsilfvnwqUKCAihcvrnfffVcNGzbU2LFjnV0agH8h+bud/P2uVq2a3nzzTW3fvl3//POPs8sDUo1GMZ2sW7dO2bJlU5MmTZQ/f367VDE4OFizZ89W48aNVa5cOb3++uuKj4+3vb5582aFhISodOnSCgwM1DvvvKPLly9LkqZNm6YePXro1VdfVcWKFdWuXTstW7ZMy5Ytsw1l+fr6au3atapfv77Kli2rd955R8ePH1f79u1VtmxZtW3bVmfOnLHtb+PGjWrQoIHKli2rFi1aaMeOHbbX2rVrp4iICHXu3FllypTRSy+9ZBsa7d+/v3bs2KHw8HC1a9cuXY+nK2nVqpV+//13/fHHH7p48aKGDBmiqlWrKiAgQP369dPFixclSdu3b1dwcLCGDh2qgIAAffzxx+rfv78mTpyoPn36qGzZsmrQoIF+++03TZkyRYGBgapevbrTTlOA2enTp/XWW2+pYsWKqlSpkkaNGmW7P6tpiLpdu3aaNm2aJOnUqVPq1KmT/P39VaVKFY0cOVI3btyQdOsfkNOnT1dQUJACAwP1xhtv6NSpUw/3w8FO8i3a3Nzc7vndlqTY2Fh17txZ5cuX14svvqjw8HAlJSVJuvV74N1339XIkSPl7++v4OBg/fDDD5o/f76qVq2qypUra+7cuU75jHj80Cimk9WrV6tmzZpyc3NTcHCwli9fbpf8TZs2TV26dNGiRYt09epV9e7dW5J07NgxvfXWW2rbtq3Wrl2rDz74QFu3btXixYtt7928ebMaNWqkOXPmKCIiQvXr11f9+vX15Zdf2taZOnWqxo0bp5kzZ2rDhg1q06aN2rRpo4ULFyo+Pl6ffPKJJCk6OlphYWHq3r27VqxYoSZNmqhr1676448/bNuaMWOGGjZsqFWrVqlEiRIaMmSIkpKSNGjQIPn7+6tTp062X1z493x8fCRJhw8fVq9evXTw4EHNmDFDn332mWJjY9W/f3/buidPnlRCQoKioqLUqFEjSdKcOXNUsWJFrVixQjlz5lSHDh109uxZLVq0yNZYJv/CgXMlJCSoQ4cOunr1qubNm6cPPvhA3377rSZMmJCq948cOVJPPPGEli9frunTp2v9+vW2/1fMnz9fK1eu1KRJk7Ro0SLlyZNHnTp1sjWSeLiOHTumjz/+WC+++KKefPLJe363z507p7Zt2yp//vxasmSJhg4dqvnz59s1f2vWrFG2bNn01VdfqUyZMurTp49++OEHzZs3T+3atdP48eN17tw5Z31cPE6sSHOnTp2y+vr6Wjds2GC1Wq3WH3/80Vq8eHHrzz//bLVardZatWpZR48ebVv/2LFj1uLFi1tjYmKsR44csX7xxRd223v77betAwYMsFqtVuvUqVOtVatWtXs9LCzMGhYWZntevHhx68KFC23PW7RoYe3Xr5/t+YQJE6ydOnWyWq1Wa9++fa1jx461216vXr1sy1577TVr7969ba8dPHjQWrx4cevp06dtr0+dOtWRw4P/r1atWtalS5emWH7jxg1r8eLFrdOmTbMWL17cGhcXZ3vt8OHD1uLFi1tjY2OtP/30k7V48eLWw4cP214PCwuztmrVyvZ8wYIFVj8/P+vVq1ft3n/mzJl0/GRIVqtWLWupUqWs5cqVs3uUKlXKWqtWLeumTZusZcuWtV64cMH2ni1btlhfeOEF66VLl6xLly611qpVy26bt3/nGjdubO3fv781ISHBarVarb/++qv1+PHjVqvVaq1evbp18+bNtvclJiZaK1eubLcM6ePOv/dSpUpZ/f39rX379rWeO3fO9v/Ru32358yZY61Ro4b1xo0bttc///xza7Vq1axW663fA0FBQdakpCSr1Wq1fvvtt9bixYtbjx07ZrVardarV69aixcvbt29e/dD/NR4XLk7u1F9HK1evVqenp4KCgqSJFWsWFE5cuTQsmXLFBgYKEkqX768bf0iRYooZ86cio2NVf369eXh4aGIiAgdOnRIhw4d0uHDh9W0aVPb+oULF75vDUWKFLH92cvLy+49Xl5etqGt2NhYrV27VosWLbK9fuPGDVvtkvTss8/a/pw1a1ZJUmJiYqqOBRx36dIlSbf+nrNnzy5vb2/baz4+PsqRI4fi4uKULVs2SdLTTz9t9/7bn3t5eSlv3rzy8vKSJHl6ekqS7e8f6e/NN99U3bp17ZZt2LBBX3zxhWJjY/Xss88qR44cttfKly+vxMREHTt27L7b7tKliwYOHKiNGzeqevXqatCggV544QVdvnxZp0+f1ttvvy03t/8bOLp27ZqOHj2aZp8Nd5f893758mVNmzZNJ0+e1H//+1/lypVL27Ztu+d3OzY2Vn5+fnJ3/79f0f7+/oqPj9fff/8t6db33GKxSJLt+538//nk53zPkRZoFNPB6tWrde3aNQUEBNiW3bx5U+vWrdOQIUMkye5/AMmvu7m5KTo6Wm3atFFwcLACAwMVGhqqOXPm2K2b/Mv+XjJlymT3/PZfFnfut2vXrgoJCbFbnvw/GknKnDlzivdZmUCTbmJiYiTJ9gvhTjdv3tTNmzdtz+/8ebjzZ+tuf/d4OPLkyaOiRYumWCaZv8vJf7c3b960NQK3u/0faU2aNFGVKlW0adMmffvtt3rzzTfVtWtXde7cWZL04Ycf2jUjkuyaUqSf2//eP/zwQ7Vo0UI9evTQokWLbOcq3in5u236uUg+XST55+PO77nEdx3pg5+qNHbkyBH99ttvGjx4sJYvX257TJkyRZcuXdLGjRsl3To3MNkff/yhf/75R76+vvrqq69UoUIFTZo0SW3btlWZMmX0xx9/3LMxM/0ySS1vb2+dOHFCRYsWtT0WLVqk77777oG3iX9n6dKl8vPzU1BQkP7++2/FxcXZXjt8+LAuXbqU4pc/Hk3e3t46evSoLly4YFu2d+9eubu765lnnlHmzJltE9mkW/9AO3HihO35lClTdPbsWbVp00YzZ85Unz59tGHDBmXPnl158uRRfHy87XtdsGBBTZw4UUeOHHmYHxG6NYll1KhROnjwoGbPni1vb+97fre9vb3166+/2p1PumfPHuXOnVs5c+Z0wieAK6NRTGOrV69Wzpw51apVKxUvXtz2aNCggZ577jktX75ckjR37lxt3rxZ0dHRGjhwoKpVq6Znn31WOXPmVExMjPbv368jR45o3Lhx+uWXX+45hJAlSxadPHnSbiZzaoWGhmrNmjWaO3eujh07ptmzZ2v27Nl2w8338sQTT+jo0aM6e/asw/uG9M8//yg+Pl5//fWXYmJiNHr0aK1Zs0b9+/eXj4+PqlevrrCwMO3fv1/79+9XWFiYKlSooOLFizu7dKSBatWqqUiRInr33XcVExOjn376SSNHjlSjRo2UPXt2lSpVShcuXNC8efN0/PhxjR071m5mbFxcnEaMGKHo6GgdOnRIW7Zs0QsvvCDp1nf7gw8+0Ndff62jR49q8ODB2r17t4oVK+asj+vSypQpoxYtWuijjz5S1qxZ7/ndbty4sRISEvTee+8pNjZWmzZt0rRp09SmTZt/FQwAD4JGMY2tXr1ajRs3Ng4ttGnTRlu3btWZM2fUrFkzTZ48WW3atFG+fPk0ZcoUSbcufVGuXDmFhoaqbdu2OnXqlHr27Knffvvtrvts2rSpjhw5oiZNmjg8JFyuXDlNmDBBn3/+uRo0aKDFixdr0qRJqlChQqre37JlS33//ffq0qWLQ/vFLWPGjFFQUJCqV6+ujh076siRI5o9e7YqVqwoSRo/fryKFCmi0NBQde7cWc8//7ymT5/u5KqRVjJlyqSPPvpIkvTKK6/onXfeUe3atTVixAhJt84PDgsLU0REhEJCQmS1WvXSSy/Z3j9s2DDlzZtX7dq10yuvvKL8+fNr0KBBkqTOnTurRYsWeu+99xQSEqJTp05p1qxZDD070dtvv63MmTNr4sSJ9/xuZ82aVZ9++qmOHTumkJAQjRw5Uh06dFCvXr2c/AngiixWTjZ76IKDg9WrVy81b97c2aUAAADcFYkiAAAAjGgUAQAAYMTQMwAAAIxIFAEAAGBEowgAAAAjGkUAAAAY0SgCAADAiEYRAAAARjSKSJXg4GD5+vrK19dXJUqUkL+/v1q3bq3vv//e2aU98hISErR48WLb83bt2mnatGkOb+f29/Xv31/9+/dPsxqTBQcHKyoqKs23e6dp06apXbt26b6fR93Bgwe1e/dup+x727Ztio2Ndcq+pQf/njyIpKQkTZ8+XbVq1VJgYKC6du2qP/7446HsG3A2GkWk2sCBA/XDDz9oy5YtWrRokcqXL69u3bpp69atzi7tkbZ69WrNmDEjTbc5aNAg263c0tKXX36pBg0apPl28WB69uypo0ePOmXfoaGh+t///ueUfT9sc+fO1ezZszVkyBAtXLhQmTJlUpcuXXTt2jVnlwakOxpFpFq2bNmUL18+FShQQMWLF9e7776rhg0bauzYsc4u7ZGWHpcyzZYtm7Jly5bm282dO7e8vLzSfLtARrZo0SJ16tRJwcHBeu655/T+++/r9OnTTktzgYeJRhH/SqtWrfT777/bhmEuXryoIUOGqGrVqgoICFC/fv108eJF43vr1q2rzz77zG5Z48aNtWTJEknSnj171KZNG5UrV07BwcH64osvbOuZhlZ9fX21fft2477Onj2rPn36qHz58qpWrZomT54sq9WqEydOyNfXVydOnLCte/uwZ1RUlFq3bq2ePXsqICBAK1asULt27TRy5EjVrl1bNWvW1KVLl/Tnn3/qjTfeUNmyZRUcHKzw8HDdvHnTto127dpp6tSpqlSpkgIDAzV27FhZrVZt375dAwYM0MmTJ1PU8eeff6pEiRL69ddf7T7HCy+8cN9hr+Tj888//6h06dL66aefbK9dunRJpUuX1s6dOyVJGzduVIMGDVS2bFm1aNFCO3bsuOt2bx96bteunSIiItS5c2eVKVNGL7300j1PRdi1a5fatGmjsmXLqly5curatav++uuvu65/48YNDR8+XOXLl1fVqlXtflaSkpL06aefqnbt2ipTpozatWunmJgY2+t3/ixERUUpODjY9nzy5MkKCgqyvffQoUO213bu3KnmzZurTJkyaty4sdavX3/XGu905swZvfnmm6pQoYJKlSqlZs2aadeuXZJk+1lbuXKlXnzxRQUGBmrUqFFKTEyUdOsfDDNmzFBwcLBKlSqloKAghYeH27Z9589d8+bNdfLkSQ0YMED9+/fX9u3bFRwcrC+//FLVqlVThQoV9Mknn+jnn39WvXr15O/vr3fffVdJSUm2/U2fPl1BQUEKDAzUG2+8oVOnTtkdw6+++kqNGjVSqVKl1LZtWx0/flySbMeyffv2qR7+jYqKUv369VWmTBk1b95cP//8s+214OBgTZw4UUFBQQoJCTH+42njxo166aWXVK5cOY0YMcL2/XqQ7f/0008OHav+/furefPmtu1lyZJF7u7uunTpUqo+O/Aoo1HEv+Lj4yNJOnz4sCSpV69eOnjwoGbMmKHPPvtMsbGxdz1XrmHDhna/hGNjY3XkyBHVrVtXsbGx6tChgypUqKCoqCj17t1b48eP18aNGx+ozp49eyo+Pl7z58/XBx98oKioKC1YsCBV792zZ4+ee+45LV68WEFBQZJu/VKaOHGiwsPD9eSTT6pXr17KkyePli1bprFjx2rlypV2w8l79uzRkSNH9MUXX2jIkCGaO3eutm7dKn9/fw0cOFBPPfWUfvjhBxUsWND2noIFCyogIMDuGK1fv14lS5ZU0aJFU1V7tmzZ9OKLL9odt2+//Va5c+dWQECAoqOjFRYWpu7du2vFihVq0qSJQ+dfzZgxQw0bNtSqVatUokQJDRkyxPbL9Xb//POPunXrpmrVqmnVqlWaNWuWjh07po8//viu296zZ48yZ86s5cuX6/XXX9e4ceNs58RNnz5dkZGRGjhwoJYtW6bChQurS5cuunLlyn1r3rhxoxYtWqQPPvhAq1atUt68eTVgwABJUnx8vLp166bmzZtr5cqV6tKli/r3729rqu+nb9++unnzphYuXKjly5erQIECGjZsmN064eHhmjJlisLDw7VhwwZbo7V8+XLNmTNHo0eP1rp169SzZ09NmzbN7h8Kt//cRUZG6qmnntLAgQNtpxn89ddf2rRpk+bNm6c33nhDkydP1pgxYzRu3DhNnjxZa9as0ebNmyVJ8+fP18qVKzVp0iQtWrRIefLkUadOnXTjxg3b/qZNm6ZBgwYpKipK58+f1wcffCDp1ikIya936tTpvsclKipKI0eOVLdu3bR8+XJVrVpVr7/+us6cOWNbZ+XKlZo1a5bGjRsni8Vi9/7Dhw+rT58+atOmjZYuXarExERbA/6g23fkWNWoUUMFChSwbevjjz+Wu7u7KlWqdN/PDjzqaBTxryQPb16+fFnR0dHasWOHJk6cqDJlyqhMmTKaOHGivv76a8XFxaV4b6NGjbR3716dPn1akrR27VoFBQUpR44cWrx4sV544QW98847KlasmJo1a6bXXntNn376qcM1RkdHa8+ePRo3bpxeeOEFVahQQcOGDVP27NlT9X6LxaLu3bvLx8dHuXPnliTVrFlT5cuXV6lSpfTTTz/p1KlTGjlypIoVK6ZKlSopLCxMc+fOtW3j5s2bttebNm2qEiVK6JdffpGHh4eyZcumTJkyKV++fMqUKZPdvhs2bKh169bZnq9du1YNGzZ06PM3bNhQGzdutKU069evV/369WWxWDRr1iy98soraty4sYoWLar27durevXqduntvdSoUUPNmzfXM888o+7du+vPP/9UfHx8ivWuXbumHj16qGfPnipSpIgCAgJUt25duyTvTgUKFNCAAQP0zDPPKDQ0VNmzZ1dMTIysVqvmz5+vt956S7Vr15aPj49GjhypTJkyacWKFfet+eTJk8qcObMKFSqkZ555RkOGDLH9Y2bBggWqWrWqXnvtNRUtWlRNmzZVq1atNGfOnPtu12q1qk6dOhoyZIh8fHz03HPP6dVXX7X9IypZv379FBgYqMqVK+utt97S4sWLZbVaVbBgQY0dO1ZVqlTR008/rTZt2ihfvnx2x+j2n7ucOXMqU6ZMdqcZ3LhxQ2FhYSpWrJheffVVJSUl6dVXX1W5cuVUq1YtlSxZ0vZd/PTTT/Xuu++qUqVK8vHx0YgRI3Tx4kW7VLhjx46qUqWKihcvrjZt2ujAgQOSZPse5MiRQ08++eR9j828efPUrl07hYSEqFixYurbt6+KFy+u+fPn29Zp0qSJbbLcnZYuXarAwECFhobKx8dHQ4YMUf78+f/V9h05VrdbvHixwsPDNW7cOOXIkeO+nx141Lk7uwA82pKHXrJmzaq4uDhlz55d3t7ettd9fHyUI0cOxcXFqVixYnbv9fHxka+vr9atW6fQ0FCtXbtW3bp1k3QrXSxTpozd+v7+/lq4cKHDNR45ckQ5c+ZUkSJFbMvq1KkjSXZDvXeTJ0+eFOflFS5c2Pbn2NhYXbhwQQEBAbZlSUlJunbtms6fP2/bRtasWW2vZ82a1TbkeC/16tXT6NGjdfDgQeXLl0+7d+/WxIkT7/u+29WqVUuDBg3Svn375Ovrq++//97WxMbGxmrt2rVatGiRbf0bN27YktP7efbZZ+0+kyTj58qXL59CQkI0e/ZsHTx4UIcPH1ZMTIzKly9/120//fTTdslStmzZdP36dZ09e1YXLlxQ2bJlba9lzpxZpUqVStUs3IYNG2r+/PmqXbu2ypUrpzp16qhFixaSpLi4OH3zzTfy9/e3rX/jxg27n+m7sVgsatOmjdasWaPdu3fryJEjOnDgQIqE9fbPXKpUKZ07d07nz59X5cqVtW/fPk2aNEmxsbE6ePCg4uPj7d5/+8/d3ST/nCf/zN7+Hi8vLyUkJOjy5cs6ffq03n77bbm5/V9ecO3aNbvJMbcn11mzZrVLGx0RGxurnj172i0rV66c3d/XvT5bbGysSpYsaXueOXNmu+cPuv3UHKvbnTx5UiNHjtTgwYNVu3btu9YLPE5oFPGvJJ8X9vzzz9udI3a7mzdvpjifKFnDhg21YcMGvfjiizpx4oTtf76enp4p1k1KSrJtx2Kx2J3HdK+mK3PmzHd97c4hLtO2TLXcviwxMVHFihXTRx99lGK95KTHw8MjxWupmcSSO3duValSRevXr1f+/PlVtmxZPfXUU/d93+2eeOIJ1apVS+vXr9eZM2eUN29eWxN+8+ZNde3aVSEhIXbvSe2EFdOxNX2uM2fO6OWXX5afn5+qVq2qV155Rd9++6327dt3123fma4mb9v09yHd+iymYe/k15Lly5dPa9eu1Y8//qhvvvlGs2bN0uLFi7V8+XIlJiaqcePGeuONN+ze7+5+//9VJiUlqVOnTvr777/VoEEDBQcH68aNG+rVq5fdercfs+R6LRaLlixZojFjxqhly5aqW7euwsLC1L59e7v33u2z36vW2xvBZMnH48MPP0zRBN+ekt3ru+MIU913/n3d77Pd+XN1e20Puv3UHKvbbd26Vfnz51ebNm3uuR7wOGHoGf/K0qVL5efnpyJFisjb21t///233XDN4cOHdenSpbsmMo0aNdK+ffu0fPly1ahRwzaM5e3tnaKJ2LNnj207mTNn1uXLl22vJZ9kb1K0aFFduHBBf/75p23Z3Llz1aNHD9svm9u3lZqU8Xbe3t46deqUcufOraJFi6po0aI6ceKEpk6damxE73S/dRo1aqRvvvlGW7ZscXjYOVnDhg21ZcsWbdq0ye7yNt7e3jpx4oSt7qJFi2rRokX67rvvHmg/d7Nx40blyJFDM2fOVIcOHRQYGKjjx48/0IzvbNmyKW/evNq7d69t2Y0bN/Trr7+m6ufj22+/1ZIlS1SzZk0NHz5cX331lY4eParff/9d3t7e+uOPP+yOx+bNm7Vy5cr71nX48GH9/PPPmj17tt544w3VrFnTNlnn9s958OBB258PHDig/PnzK1euXPriiy/Us2dPDRw4UCEhIcqVK5fOnj2bLrPis2fPrjx58ig+Pt72OQsWLKiJEyfqyJEjab4/0/d53759qUpqpVv/EP3ll19sz5OSkhQdHZ1m20+tJ554QvXr10/TbQIZHY0iUu2ff/5RfHy8/vrrL8XExGj06NFas2aN7fwuHx8fVa9eXWFhYdq/f7/279+vsLAwVahQQcWLFzdus1ChQipTpozmzJlj1wS1bdtWBw8e1OTJk3XkyBEtW7ZMn3/+uV599VVJUunSpfXjjz9q27Zt+v333zVixIi7ph/PP/+8KleurEGDBikmJkbbt2/Xxx9/rGrVqilv3rwqWLCgZs2apePHjysqKkrffvutQ8clKChIhQsXVr9+/RQTE6OdO3dqyJAhypIlizEVu1OWLFl08eJFHT161JiM1qlTR0ePHtWOHTtUr149h2pLVr16ddvJ+7c3iqGhoVqzZo3mzp2rY8eOafbs2Zo9e7bdkHJayJkzp06dOqVt27bp+PHj+vjjj7Vhw4YUQ3upFRoaqqlTp+rrr79WbGyshgwZouvXr9s+W+nSpTV//nwdPXpUmzdvtrtIeFJSkiZMmKCNGzfqxIkTioqKUpYsWfTss8+qbdu2OnDggKZMmaKjR49q5cqVmjx5sgoVKiTp1sXR4+PjjQl59uzZ5ebmptWrV+vkyZNat26dbaLK7Z9z9OjR+uWXX7R161Z9+OGHtp/pXLlyadu2bbYh67fffls3bty45zF64oknFBcXpwsXLjzQMfzggw/09ddf6+jRoxo8eLB2796d4hSRe+370KFD+ueffyTdmgh0t+sKhoaGav78+Vq+fLmOHDmi999/X9HR0bYh//t55ZVXdODAAUVERCguLk7jx4+3m6H9b7efWjVr1kzV5B3gcUKjiFQbM2aMgoKCVL16dXXs2FFHjhzR7NmzVbFiRds648ePV5EiRRQaGqrOnTvr+eef1/Tp0++53QYNGsjd3V01a9a0LStUqJBmzpyp77//Xo0bN1ZERIT69++vl19+WZLUtGlTvfTSS+rRo4e6dOmiRo0a2Z3cfqeJEycqS5YsatWqlf773/+qVatWatu2rdzc3DR69Gjt379fDRo00Lp161IMO95PpkyZFBERoaSkJL3yyivq3bu3atSoocGDB6fq/ZUrV1bRokXVuHFju7QpWdasWVW9enWVK1dOefLkcai2ZB4eHqpTp46eeuopu8kC5cqV04QJE/T555+rQYMGWrx4sSZNmqQKFSo80H7upn79+mrSpInefPNNvfzyy9q+fbvCwsIUGxv7QM1ip06d1LJlSw0ZMkTNmzfX6dOnNW/ePNskiyFDhujChQtq1KiRPv30U7355pu29wYHB+vNN9/U2LFjVb9+fa1Zs0YfffSRcuTIocKFC2vGjBn6/vvv1ahRI33wwQfq37+/mjRpIulWqh0UFGSXTid76qmnNGzYMH3yySdq1KiRPv74Yw0ePFju7u767bffbOs1aNBA3bp10zvvvKOWLVvq9ddfl3TrgvaXLl1S06ZN1bt3b/n6+uo///mP8WciWZs2bbRgwYJU/6zdrnPnzmrRooXee+89hYSE6NSpU5o1a1aqJ2i0a9dOEyZMsDXDQUFBWrNmjXHdBg0a6O2339bUqVPVpEkT7dixQ5GRkbarJtxP0aJFFRERodWrVyskJETx8fGqUaNGmm0/tSIjI9O8+QQyOos1PcY1AAdMmTJFp0+f1vjx451dSobVunVrtWzZ0tYow3nCwsLUt29f5cuXz6H3JZ+Du3nzZj399NPpVJ3zLF26VF5eXg98egSAjInJLHCa6OhoHTx4UJ9//rkiIiKcXU6G9NNPP2n37t2KjY194GFnpJ1jx47p3LlzDjeJj7ukpCQtW7bMdp1FAI8PGkU4zYEDBzRq1Ci1bdtWgYGBzi4nQ/rqq6+0efNmjRgxIlXXq0P6KlKkCP+oMXBzc9Nnn32WZrOkAWQcDD0DAADAiMksAAAAMKJRBAAAgBGNIgAAAIxoFAEAAGBEowgAAAAjGkUAAAAY0SgCAADAiEYRAAAARv8P3q1irbcPvj4AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2dhist = pd.crosstab(df.loc[:, 'Do you currently work?'],\n",
    "                        df.loc[:, 'Do you currently live in a house, apartment, or dorm?'],\n",
    "                        normalize='index')\n",
    "\n",
    "# Plot heatmap\n",
    "plt.subplots(figsize=(8, 8))\n",
    "sns.heatmap(df_2dhist, cmap=\"Blues\")\n",
    "plt.xlabel('Do you currently live in a house, apartment, or dorm?')\n",
    "_ = plt.ylabel('Do you currently work?')\n",
    "df_2dhist"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:39.852958Z",
     "start_time": "2024-02-24T06:34:39.628331Z"
    }
   },
   "id": "c533e52f7d64a4df",
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "For both working & non-working participants, the proportion who live in an apartment are equivalent (50%).\n",
    "\n",
    "However, 13% of non-working participants live in a dorm while only 6% of working participants live in a dorm.\n",
    "This 7% drop is matched in participants who live a house, with 44% of working participants living in a house compared to 36% of non-working participants.\n",
    "\n",
    "This indicates that working participants tend to live off-campus and in living situations that have a higher cost of living."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6294dcc03fa9f516"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Do you currently live in a house, apartment, or dorm?').size().plot(kind='barh',\n",
    "                                                                                color=sns.palettes.mpl_palette(\n",
    "                                                                                    'Dark2'))\n",
    "plt.gca().spines[['top', 'right', ]].set_visible(False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:39.957772Z",
     "start_time": "2024-02-24T06:34:39.855743Z"
    }
   },
   "id": "450665f2272bb3a2",
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "Most participants live in either an Apartment or a House. This would indicate that most students either live off-campus or on-campus apartments."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4d64d891924f201e"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Average GPA:  3.6520247933884296\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataTable1 = pd.pivot_table(data=df, values='What was your GPA your very first quarter at UCR?',\n",
    "                            index='How many hours do you work per week on average?', aggfunc='mean')\n",
    "_ = dataTable1.plot(kind='bar')\n",
    "print(\"Total Average GPA: \", df['What was your GPA your very first quarter at UCR?'].mean())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.102959Z",
     "start_time": "2024-02-24T06:34:39.964444Z"
    }
   },
   "id": "1a704a4702ea3f9c",
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "source": [
    "The average GPA seems to be independent in respect to working hours per week.\n",
    "Most students who work less than 20 hours have an equivalent average GPA to the total average GPA of all participants (3.65).\n",
    "\n",
    "There is a small drop in GPA associated with students who work more than 20 hours (3.5 GPA), which may mean some of those students may struggle maintaining balance between work and school.  \n",
    "\n",
    "This would indicate that most students seem to be able to balance work with school. However, it would also indicate that\n",
    "students who work full-time jobs may struggle slightly in school."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cb1fd8e56d403466"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 3000x1500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stkbar_df = df.dropna()\n",
    "_ = pd.crosstab(\n",
    "    df['Do you have family members who have careers related to your career aspirations?'],\n",
    "    df['What are your career plans right after graduation?'],\n",
    "    normalize='index'\n",
    ").plot(kind=\"bar\", stacked=True, rot=0, figsize=(30, 15))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.466605Z",
     "start_time": "2024-02-24T06:34:40.108432Z"
    }
   },
   "id": "ae23070caa8a3c88",
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "Most students across all groups are looking to \"get into the job industry\".\n",
    "Across all groups, the proportions of \"Attending Grad School\" and \"Get into the job industry\" are similar, except for students who have extended family in their career.\n",
    "They are more unsure about their future compared to other groups.\n",
    "Also, students with no family in their field are more diversified in their career plans."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5f7faaa1acde66ef"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Do you currently work?                      No       Yes\nWhat is your current class standing?                    \nFreshman                              0.923077  0.076923\nGraduate                              0.000000  1.000000\nJunior                                0.696078  0.303922\nSenior                                0.725490  0.274510\nSophomore                             0.693182  0.306818",
      "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>Do you currently work?</th>\n      <th>No</th>\n      <th>Yes</th>\n    </tr>\n    <tr>\n      <th>What is your current class standing?</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Freshman</th>\n      <td>0.923077</td>\n      <td>0.076923</td>\n    </tr>\n    <tr>\n      <th>Graduate</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>Junior</th>\n      <td>0.696078</td>\n      <td>0.303922</td>\n    </tr>\n    <tr>\n      <th>Senior</th>\n      <td>0.725490</td>\n      <td>0.274510</td>\n    </tr>\n    <tr>\n      <th>Sophomore</th>\n      <td>0.693182</td>\n      <td>0.306818</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = pd.crosstab(\n",
    "    df['What is your current class standing?'],\n",
    "    df['Do you currently work?'],\n",
    "    normalize='index'\n",
    ").plot(kind='bar', stacked=True)\n",
    "pd.crosstab(\n",
    "    df['What is your current class standing?'],\n",
    "    df['Do you currently work?'],\n",
    "    normalize='index'\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.577484Z",
     "start_time": "2024-02-24T06:34:40.487032Z"
    }
   },
   "id": "464b4dec962ea0ae",
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "The class standing most likely to work are graduate students, where 100% of participants work. \n",
    "The freshman class is the least likely to work, where 92% of participants do not work.\n",
    "\n",
    "For Sophomore, Junior, and Senior participants, all 3 groups have similar proportions working with 30% of participants working.  "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d041734386169b98"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
    "\n",
    "_ = pd.crosstab(\n",
    "    w_df['What is your current class standing?'],\n",
    "    w_df['Do you work in a department related to your major?'],\n",
    ").plot(kind='bar', stacked=True, ax=axes[0])\n",
    "\n",
    "_ = pd.crosstab(\n",
    "    w_df['What is your current class standing?'],\n",
    "    w_df['Do you work in a department related to your major?'],\n",
    "    normalize='index',\n",
    ").plot(kind='bar', stacked=True, ax=axes[1])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.735256Z",
     "start_time": "2024-02-24T06:34:40.578565Z"
    }
   },
   "id": "101f55892c052d46",
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "source": [
    "Of the students who responded \"yes\" to currently working, the above graphs show the proportions of participants who work in a department related to their major.\n",
    "Most students do not work in a department related to their major, indicating that they are working for money rather than job experience. \n",
    "his holds true for all groups except for Graduate students, who all work in a department of their major.\n",
    "\n",
    "Interestingly, Juniors have a higher rate of working in their major, perhaps indicating internships or students seeking to gain major-related work experience."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a77e6367ee785afb"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Hypotheses"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f573ee142b931496"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Hypothesis 2: Students who live on-campus are more likely to have roommates of the same major.\n",
    "\n",
    "Null Hypothesis: There is no relationship between students who live on-campus and students who have roommates of the same major.\n",
    "\n",
    "Significance value: 0.1\n",
    "Degrees of Freedom: 2"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dcc6d91b3e660c2e"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "How many hours do you work per week on average?    0  1 - 5  10 - 20  20 - 40  \\\nWho do you live with?                                                           \nBoth                                              22      1        2        0   \nFamily                                            61      6       10        4   \nFriends                                           57      2       11        2   \nNeither                                           42      0        5        0   \nTotal                                            182      9       28        6   \n\nHow many hours do you work per week on average?  5 - 10  Total  \nWho do you live with?                                           \nBoth                                                  2     27  \nFamily                                               13     94  \nFriends                                              12     84  \nNeither                                               7     54  \nTotal                                                34    259  ",
      "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>How many hours do you work per week on average?</th>\n      <th>0</th>\n      <th>1 - 5</th>\n      <th>10 - 20</th>\n      <th>20 - 40</th>\n      <th>5 - 10</th>\n      <th>Total</th>\n    </tr>\n    <tr>\n      <th>Who do you live with?</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Both</th>\n      <td>22</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>Family</th>\n      <td>61</td>\n      <td>6</td>\n      <td>10</td>\n      <td>4</td>\n      <td>13</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>Friends</th>\n      <td>57</td>\n      <td>2</td>\n      <td>11</td>\n      <td>2</td>\n      <td>12</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>Neither</th>\n      <td>42</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>7</td>\n      <td>54</td>\n    </tr>\n    <tr>\n      <th>Total</th>\n      <td>182</td>\n      <td>9</td>\n      <td>28</td>\n      <td>6</td>\n      <td>34</td>\n      <td>259</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n",
    "roommates_major_table"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.749833Z",
     "start_time": "2024-02-24T06:34:40.736572Z"
    }
   },
   "id": "24d1f01fdd4ca1d6",
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-squared value: 10.845786899856222\n"
     ]
    }
   ],
   "source": [
    "num_rows, num_cols = roommates_major_table.shape\n",
    "# Initialize expected frequencies\n",
    "expected_frequencies = []\n",
    "chi_squared = 0\n",
    "for i in range(num_rows - 1):\n",
    "    row_totals = roommates_major_table.iloc[i, -1]\n",
    "    for j in range(num_cols - 1):\n",
    "        col_totals = roommates_major_table.iloc[-1, j]\n",
    "        expected_frequency = (row_totals * col_totals) / roommates_major_table.iloc[-1, -1]\n",
    "        expected_frequencies.append(expected_frequency)\n",
    "        chi_squared += ((roommates_major_table.iloc[i, j] - expected_frequency) ** 2) / expected_frequency\n",
    "\n",
    "print(\"Chi-squared value:\", chi_squared)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.753936Z",
     "start_time": "2024-02-24T06:34:40.750695Z"
    }
   },
   "id": "fd3e73d9f461afd1",
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "source": [
    "With a significance value of 0.1 and 2 degrees of freedom, chi-squared must be greater than 4.61.\n",
    "Since chi-squared of `4.18 < 4.61`, we accept the null hypothesis:\n",
    "\n",
    "There is no relationship between students who live on-campus and students who have roommates of the same major."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "32eff09a99bfb3c7"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Hypothesis 3: People who live with more people will have a higher GPA on average."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "56a4b6a6c1e05d81"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Household Size: 3.826923076923077\n",
      "Average GPA: 3.6520247933884296\n",
      "Pearson Correlation Coefficient: -0.2010052294084673\n"
     ]
    }
   ],
   "source": [
    "hyp3_major_table = pd.crosstab(df.iloc[:, 5], df.iloc[:, 6], margins=True, margins_name='Total')\n",
    "average_household_size = df.iloc[:, 5].mean(skipna=True)\n",
    "average_gpa = df.iloc[:, 6].mean(skipna=True)\n",
    "\n",
    "print(\"Average Household Size:\", average_household_size)\n",
    "print(\"Average GPA:\", average_gpa)\n",
    "numerator = 0\n",
    "denom_x = 0\n",
    "denom_y = 0\n",
    "for i in range(260):\n",
    "    x_i = df.iloc[i, 5]\n",
    "    y_i = df.iloc[i, 6]\n",
    "    if not pd.isna(x_i) and not pd.isna(y_i):  # Check for NaN values\n",
    "        numerator += (x_i - average_household_size) * (y_i - average_gpa)\n",
    "        denom_x += (x_i - average_household_size)**2\n",
    "        denom_y += (y_i - average_gpa)**2\n",
    "\n",
    "# Calculate Pearson correlation coefficient\n",
    "pearson_coefficient = (numerator / ((denom_x * denom_y)**0.5))\n",
    "print(\"Pearson Correlation Coefficient:\", pearson_coefficient)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.780079Z",
     "start_time": "2024-02-24T06:34:40.755414Z"
    }
   },
   "id": "b513f8e8241e86e5",
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "source": [
    "With a Pearson Correlation Coefficient of -0.2, there is a slight negative correlation between household size and average GPA.\n",
    "Students who live alone or with fewer people perform slightly better than those with more roommates.\n",
    "\n",
    "This goes against our hypothesis that people living with more people will have a higher GPA."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "79b83b5ec3c1f9c5"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-24T06:34:40.782502Z",
     "start_time": "2024-02-24T06:34:40.780918Z"
    }
   },
   "id": "55fb116c79c479a1",
   "execution_count": 14
  }
 ],
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