Merge pull request #3 from ansg191/formatting

Adds some formatting
author: Anshul Gupta <ansg191@anshulg.com> 2024-02-22 17:14:01 -0800
committer: GitHub <noreply@github.com> 2024-02-22 17:14:01 -0800
commit: 7d8e90875c0d5cc42b247c121b1baac08eb20fd9 (patch)
tree: 5ff7f598821fde0df31d7580be8549874dadfb0e /CS105MiniProject.ipynb
parent: e70c4343c56137100a786b2d1f1b7f8b5487c3da (diff)
parent: 717bd5c3abbc775694a3afa582edacdd8482cdea (diff)
download: CS105MiniProject-7d8e90875c0d5cc42b247c121b1baac08eb20fd9.tar.gz
CS105MiniProject-7d8e90875c0d5cc42b247c121b1baac08eb20fd9.tar.zst
CS105MiniProject-7d8e90875c0d5cc42b247c121b1baac08eb20fd9.zip
1 files changed, 111 insertions, 57 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb
index ffb4fa4..2f86c48 100644
--- a/CS105MiniProject.ipynb
+++ b/CS105MiniProject.ipynb
@@ -1,21 +1,31 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "daa13044",
+   "cell_type": "markdown",
+   "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",
+    "    <p>By: <b>NAMES HERE</b></p>\n",
+    "</div>"
+   ],
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 614
-    },
-    "id": "daa13044",
-    "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a",
-    "ExecuteTime": {
-     "end_time": "2024-02-23T01:01:41.396867Z",
-     "start_time": "2024-02-23T01:01:40.758392Z"
-    }
+    "collapsed": false
+   },
+   "id": "845bdbd833f03cba"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Data Loading & Preprocessing"
+   ],
+   "metadata": {
+    "collapsed": false
    },
+   "id": "d720609d765d221b"
+  },
+  {
+   "cell_type": "code",
    "outputs": [
     {
      "data": {
@@ -32,22 +42,35 @@
     "import pandas as pd\n",
     "import numpy as np\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, 7, 8, 9, 58, 59, 60, 61, 26]]\n",
     "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "29889175",
+   ],
    "metadata": {
-    "id": "29889175",
+    "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T01:01:41.409516Z",
-     "start_time": "2024-02-23T01:01:41.398267Z"
+     "end_time": "2024-02-23T01:12:49.045312Z",
+     "start_time": "2024-02-23T01:12:48.152070Z"
     }
    },
+   "id": "3bea6ea662d6c063",
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Preprocessing"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "7e69a5a21a9de4ee"
+  },
+  {
+   "cell_type": "code",
    "outputs": [
     {
      "data": {
@@ -60,6 +83,10 @@
     }
    ],
    "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",
@@ -70,23 +97,25 @@
     "                                                 .replace('North District 4 bed 2 bath', '4')\n",
     "                                                 .replace('3 (room), 8 (hall), ~70 (building)', '3')\n",
     "                                                 .astype(int))\n",
-    "df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\n",
     "df['Who do you live with? '] = df['Who do you live with? '].replace('Family, Friends', 'Both').replace('Family, Friends, Both', 'Both')\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": "de4448fd64205d85",
+   ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T01:01:41.418974Z",
-     "start_time": "2024-02-23T01:01:41.410787Z"
+     "end_time": "2024-02-23T01:12:49.066644Z",
+     "start_time": "2024-02-23T01:12:49.047827Z"
     }
    },
+   "id": "f71f8085d5f66b0",
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
    "outputs": [
     {
      "data": {
@@ -104,15 +133,24 @@
     "# Not working DataFrame\n",
     "nw_df = df[df['Do you currently work?'] == 'No']\n",
     "w_df"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-23T01:12:49.084475Z",
+     "start_time": "2024-02-23T01:12:49.068965Z"
+    }
+   },
+   "id": "6c1d9ee7948e6b9a",
+   "execution_count": 3
   },
   {
    "cell_type": "code",
    "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    Who do you live with?   \\\n1                     Both   \n2                  Friends   \n3                  Neither   \n5                     Both   \n6                  Friends   \n..                     ...   \n253                 Family   \n254                 Family   \n256                 Family   \n257                 Family   \n259                Friends   \n\n    Do you currently live in a house, apartnment, 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? Do you currently work?  \\\n1                                          4                     No   \n2                                          4                     No   \n3                                          1                     No   \n5                                          4                     No   \n6                                          4                     No   \n..                                       ...                    ...   \n253                                        6                     No   \n254                                        5                     No   \n256                                        4                     No   \n257                                        9                     No   \n259                                        3                     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 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[176 rows x 10 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>Who do you live with?</th>\n      <th>Do you currently live in a house, apartnment, or dorm?</th>\n      <th>How many people live in your household?</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 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>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Friends</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>253</th>\n      <td>2/14/2024 13:45:45</td>\n      <td>Senior</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>6</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Off-campus</td>\n      <td>NaN</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>Friends</td>\n      <td>Dorm</td>\n      <td>3</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>176 rows × 10 columns</p>\n</div>"
+      "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    Who do you live with?   \\\n1                     Both   \n2                  Friends   \n3                  Neither   \n5                     Both   \n6                  Friends   \n..                     ...   \n253                 Family   \n254                 Family   \n256                 Family   \n257                 Family   \n259                Friends   \n\n    Do you currently live in a house, apartnment, 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? Do you currently work?  \\\n1                                          4                     No   \n2                                          4                     No   \n3                                          1                     No   \n5                                          4                     No   \n6                                          4                     No   \n..                                       ...                    ...   \n253                                        6                     No   \n254                                        5                     No   \n256                                        4                     No   \n257                                        9                     No   \n259                                        3                     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 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 10 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>Who do you live with?</th>\n      <th>Do you currently live in a house, apartnment, or dorm?</th>\n      <th>How many people live in your household?</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 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>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Friends</td>\n      <td>House</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Neither</td>\n      <td>Apartment</td>\n      <td>1</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Both</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Friends</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>253</th>\n      <td>2/14/2024 13:45:45</td>\n      <td>Senior</td>\n      <td>Family</td>\n      <td>House</td>\n      <td>6</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Family</td>\n      <td>House</td>\n      <td>5</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Family</td>\n      <td>Apartment</td>\n      <td>4</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</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>Family</td>\n      <td>House</td>\n      <td>9</td>\n      <td>No</td>\n      <td>0</td>\n      <td>Off-campus</td>\n      <td>NaN</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>Friends</td>\n      <td>Dorm</td>\n      <td>3</td>\n      <td>No</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Yes</td>\n    </tr>\n  </tbody>\n</table>\n<p>183 rows × 10 columns</p>\n</div>"
      },
      "execution_count": 4,
      "metadata": {},
@@ -125,26 +163,38 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T01:01:41.427847Z",
-     "start_time": "2024-02-23T01:01:41.419852Z"
+     "end_time": "2024-02-23T01:12:49.104996Z",
+     "start_time": "2024-02-23T01:12:49.089572Z"
     }
    },
-   "id": "5fe8ec7f22878e60",
+   "id": "34f69a756f513fb7",
    "execution_count": 4
   },
   {
    "cell_type": "markdown",
    "source": [
-    "<div>\n",
-    "    <h1>CS105 Project</h2>\n",
-    "    <p>Ali Naqvi, ...</p>\n",
-    "    <p>Topic: Does who a student is living with effect if and how they work jobs?</p>\n",
-    "</div>\n"
+    "# Analysis"
    ],
    "metadata": {
     "collapsed": false
    },
-   "id": "899d85626b77db20"
+   "id": "d5c1424ddd30ca97"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-23T01:12:49.110581Z",
+     "start_time": "2024-02-23T01:12:49.107274Z"
+    }
+   },
+   "id": "39571411a9ea92e0",
+   "execution_count": 5
   },
   {
    "cell_type": "code",
@@ -152,17 +202,13 @@
     {
      "data": {
       "text/plain": "<Figure size 800x800 with 1 Axes>",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Assuming 'df' is your DataFrame\n",
-    "\n",
     "# Count the number of people who work and don't work\n",
     "work_counts = df['Do you currently work?'].value_counts()\n",
     "\n",
@@ -170,17 +216,17 @@
     "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()\n"
+    "plt.show()"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T01:01:41.526696Z",
-     "start_time": "2024-02-23T01:01:41.430135Z"
+     "end_time": "2024-02-23T01:12:49.355506Z",
+     "start_time": "2024-02-23T01:12:49.112753Z"
     }
    },
-   "id": "bfa40c9e9693481d",
-   "execution_count": 5
+   "id": "da1811cc63b41845",
+   "execution_count": 6
   },
   {
    "cell_type": "code",
@@ -189,12 +235,20 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T01:01:41.532148Z",
-     "start_time": "2024-02-23T01:01:41.528825Z"
+     "end_time": "2024-02-23T01:12:49.360434Z",
+     "start_time": "2024-02-23T01:12:49.357193Z"
     }
    },
-   "id": "9c830283e9b26466",
-   "execution_count": 5
+   "id": "201db70188d3e778",
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "8d65fec230193b72"
   }
  ],
  "metadata": {
author	Anshul Gupta <ansg191@anshulg.com>	2024-02-22 17:14:01 -0800
committer	GitHub <noreply@github.com>	2024-02-22 17:14:01 -0800
commit	7d8e90875c0d5cc42b247c121b1baac08eb20fd9 (patch)
tree	5ff7f598821fde0df31d7580be8549874dadfb0e /CS105MiniProject.ipynb
parent	e70c4343c56137100a786b2d1f1b7f8b5487c3da (diff)
parent	717bd5c3abbc775694a3afa582edacdd8482cdea (diff)
download	CS105MiniProject-7d8e90875c0d5cc42b247c121b1baac08eb20fd9.tar.gz CS105MiniProject-7d8e90875c0d5cc42b247c121b1baac08eb20fd9.tar.zst CS105MiniProject-7d8e90875c0d5cc42b247c121b1baac08eb20fd9.zip