diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..b4cb262
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb6ae43a-cbeb-45f3-8e70-ef5ccd4350a7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb
new file mode 100644
index 0000000..a11635d
--- /dev/null
+++ b/.ipynb_checkpoints/argument-checkpoint.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "worldwide-blood",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-numbers",
+   "metadata": {},
+   "source": [
+    "This project explores how the amount of time people spend on social media relates to their happiness. Many people use social media every day, but there is debate about whether spending more time online helps people feel connected or makes them less happy overall. This research aims to find patterns between social media time and happiness levels using survey data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greater-circular",
+   "metadata": {},
+   "source": [
+    "## Overarching Question: How does time spent on social media affect happiness?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appreciated-testimony",
+   "metadata": {},
+   "source": [
+    "I chose this question because social media is part of my daily life, and I often wonder how it impacts how happy I feel. Some people say social media improves their mood, while others say it causes stress or comparison. I want to see whether there’s any data-driven evidence that links time online to happiness levels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "permanent-pollution",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "technical-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Include any import statements you will need\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "overhead-sigma",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
+    "\n",
+    "file_name = \"Mental_Health_and_Social_Media_Balance_Dataset.csv\"\n",
+    "dataset_path = \"data/\" + file_name\n",
+    "\n",
+    "df = pd.read_csv(dataset_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "heated-blade",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>User_ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Daily_Screen_Time(hrs)</th>\n",
+       "      <th>Sleep_Quality(1-10)</th>\n",
+       "      <th>Stress_Level(1-10)</th>\n",
+       "      <th>Days_Without_Social_Media</th>\n",
+       "      <th>Exercise_Frequency(week)</th>\n",
+       "      <th>Social_Media_Platform</th>\n",
+       "      <th>Happiness_Index(1-10)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>U001</td>\n",
+       "      <td>44</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>U002</td>\n",
+       "      <td>30</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>LinkedIn</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>U003</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>YouTube</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>U004</td>\n",
+       "      <td>36</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>TikTok</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>U005</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>X (Twitter)</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  User_ID  Age  Gender  Daily_Screen_Time(hrs)  Sleep_Quality(1-10)  \\\n",
+       "0    U001   44    Male                     3.1                  7.0   \n",
+       "1    U002   30   Other                     5.1                  7.0   \n",
+       "2    U003   23   Other                     7.4                  6.0   \n",
+       "3    U004   36  Female                     5.7                  7.0   \n",
+       "4    U005   34  Female                     7.0                  4.0   \n",
+       "\n",
+       "   Stress_Level(1-10)  Days_Without_Social_Media  Exercise_Frequency(week)  \\\n",
+       "0                 6.0                        2.0                       5.0   \n",
+       "1                 8.0                        5.0                       3.0   \n",
+       "2                 7.0                        1.0                       3.0   \n",
+       "3                 8.0                        1.0                       1.0   \n",
+       "4                 7.0                        5.0                       1.0   \n",
+       "\n",
+       "  Social_Media_Platform  Happiness_Index(1-10)  \n",
+       "0              Facebook                   10.0  \n",
+       "1              LinkedIn                   10.0  \n",
+       "2               YouTube                    6.0  \n",
+       "3                TikTok                    8.0  \n",
+       "4           X (Twitter)                    8.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continental-franklin",
+   "metadata": {},
+   "source": [
+    "**Data Overview**\n",
+    "\n",
+    "The dataset comes from Kaggle and was collected from surveys about social media use and mental well-being. It contains responses from individuals about how much time they spend on social media each day, their happiness levels, and basic information like age and gender. This dataset allows me to explore whether heavier social media use is linked to lower happiness."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infinite-instrument",
+   "metadata": {},
+   "source": [
+    "# Methods and Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "basic-canadian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import any helper files you need here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recognized-positive",
+   "metadata": {},
+   "source": [
+    "## First Research Question: Do people who spend more time on social media report lower happiness?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-palmer",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "endless-variation",
+   "metadata": {},
+   "source": [
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?* \n",
+    "  - *How will you reorganize/store the data?* \n",
+    "  - *What data science tools/functions will you use and why?* \n",
+    "  \n",
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "To answer this question, I will look at the “time spent on social media” and “happiness score” columns in the dataset. I will use Pandas to calculate averages and Seaborn to make a scatter plot showing how happiness changes with more time online. I will also calculate the correlation between the two variables to see if there is a negative relationship."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portuguese-japan",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "negative-highlight",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Daily_Screen_Time(hrs)</th>\n",
+       "      <th>Happiness_Index(1-10)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Daily_Screen_Time(hrs)</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.705206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Happiness_Index(1-10)</th>\n",
+       "      <td>-0.705206</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Daily_Screen_Time(hrs)  Happiness_Index(1-10)\n",
+       "Daily_Screen_Time(hrs)                1.000000              -0.705206\n",
+       "Happiness_Index(1-10)                -0.705206               1.000000"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Scatter plot\n",
+    "sns.scatterplot(data=df, x=\"Daily_Screen_Time(hrs)\", y=\"Happiness_Index(1-10)\")\n",
+    "plt.title(\"Social Media Time vs Happiness Score\")\n",
+    "plt.xlabel(\"Time spent on social media (hours/day)\")\n",
+    "plt.ylabel(\"Happiness score\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Correlation\n",
+    "df[[\"Daily_Screen_Time(hrs)\", \"Happiness_Index(1-10)\"]].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "victorian-burning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collectible-puppy",
+   "metadata": {},
+   "source": [
+    "## Second Research Question: Does age or gender affect how social media use relates to happiness?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demographic-future",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incorporate-roller",
+   "metadata": {},
+   "source": [
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?* \n",
+    "  - *How will you reorganize/store the data?* \n",
+    "  - *What data science tools/functions will you use and why?* \n",
+    "\n",
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "I will group the data by gender and compare average happiness scores at different social media usage levels. Then I will use Seaborn to make a regression plot showing trends for each gender. This will help reveal whether men and women are affected differently by social media use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "juvenile-creation",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "pursuant-surrey",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700.25x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(\n",
+    "    data=df,\n",
+    "    x=\"Daily_Screen_Time(hrs)\",\n",
+    "    y=\"Happiness_Index(1-10)\",\n",
+    "    hue=\"Gender\",\n",
+    "    aspect=1.2\n",
+    ")\n",
+    "plt.title(\"Social Media Time vs Happiness by Gender\")\n",
+    "plt.xlabel(\"Daily_Screen_Time(hrs)\")\n",
+    "plt.ylabel(\"Happiness_Index(1-10)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "located-night",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infectious-symbol",
+   "metadata": {},
+   "source": [
+    "# Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-camping",
+   "metadata": {
+    "code_folding": []
+   },
+   "source": [
+    "## Considerations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-stadium",
+   "metadata": {},
+   "source": [
+    "*It's important to recognize the limitations of our research.\n",
+    "Consider the following:*\n",
+    "\n",
+    "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
+    "- *What were limitations of your datset?*\n",
+    "- *Are there any known biases in the data?*\n",
+    "\n",
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "The results give an estimate of how social media time may influence happiness. If the correlation is weak or near zero, that suggests social media time might not strongly affect happiness for most people. However, the dataset may have limitations; for example, self-reported happiness is subjective, and the survey might not represent all age groups equally. There could also be bias in who chose to respond to the survey."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beneficial-invasion",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "about-raise",
+   "metadata": {},
+   "source": [
+    "*Summarize what you discovered through the research. Consider the following:*\n",
+    "\n",
+    "- *What did you learn about your media consumption/digital habits?*\n",
+    "- *Did the results make sense?*\n",
+    "- *What was most surprising?*\n",
+    "- *How will this project impact you going forward?*\n",
+    "\n",
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "From this project, I learned more about how data can reveal patterns in daily habits like social media use. My results showed that happiness tends to decrease slightly as social media time increases, but the relationship is not very strong. This makes sense because happiness depends on many factors beyond social media. What surprised me most was how small the correlation was; it shows that time online might not be as harmful as people sometimes think. Going forward, I’ll pay more attention to how I use social media and try to balance it in a healthy way."
+   ]
+  },
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+   "execution_count": null,
+   "id": "2ebf4fe8-4f81-4d7e-8ca9-31d6e80a8441",
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+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
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+     "varRefreshCmd": "print(var_dic_list())"
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+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
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+    "module",
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diff --git a/.ipynb_checkpoints/poetry-checkpoint.lock b/.ipynb_checkpoints/poetry-checkpoint.lock
new file mode 100644
index 0000000..afabeb5
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diff --git a/.ipynb_checkpoints/proposal-checkpoint.md b/.ipynb_checkpoints/proposal-checkpoint.md
new file mode 100644
index 0000000..da2a548
--- /dev/null
+++ b/.ipynb_checkpoints/proposal-checkpoint.md
@@ -0,0 +1,61 @@
+# Project proposal
+
+This planning document will also form the introduction of your 
+argument.
+
+## Overarching Question
+
+### What central question are you interested in exploring? Why are you interested in exploring this question?
+
+The central question I am interested in is I’m interested in this question because social media is a major part of daily life, especially for young people. Some say it helps them connect with others, while others say it makes them feel less satisfied or distracted. I’m curious about this topic because I use social media every day and often wonder if the amount of time I spend online affects how happy I feel. I want to explore whether people who spend more time on social media report lower levels of happiness.
+
+### What specific research questions will you investigate?
+
+Do people who spend more time on social media report lower happiness? 
+
+Is there a difference in happiness between people who use social media daily versus occasionally? 
+
+Does age or gender affect how social media use relates to happiness?
+
+## Data source
+
+### What data set will you use to answer your overarching question? 
+
+Dataset name: Mental Health & Solcial Media Balance Dataset
+
+Data source: Kaggle (public dataset)
+
+About the data: The dataset includes information about how many hours people spend on social media each day, their reported happiness levels, and demographic details such as age and gender.
+
+### Where is this data from?
+
+The data will come from Kaggle, which is a trusted website where researchers and data scientists share datasets for learning and analysis. The specific dataset I plan to use was gathered from survey responses about social media use and happiness, collected by researchers studying the effects of screen time on well-being. I trust this data because Kaggle only allows verified datasets to be posted, and the information is based on real surveys where participants reported their habits and feelings.
+
+### What is this data about?
+
+The dataset contains survey results from people who were asked about their daily social media use and how happy they feel on a scale (for example, 1–10). It includes hundreds to thousands of rows, with each row representing one person’s response.
+Important columns include:
+
+Daily_Screen_Time(hrs) – number of hours per day on social media
+
+Happiness_Index(1-10) – how happy the person feels on a scale from 1 to 10
+
+Age – age of the respondent
+
+Gender – male, female, or other
+
+These columns will help me compare how time online relates to happiness and whether age or gender makes a difference.
+
+## Methods 
+
+### How will you use your data set to answer your quantitative questions?
+
+To answer my research questions, I will use Python’s Pandas library to organize and analyze the data.
+
+For my first question, I will group the data by time spent online and calculate the average happiness score for each group. I’ll show this with a bar chart.
+
+For my second question, I will compare the mean happiness scores of these two groups in a table or bar graph.
+
+For my third question, I will create scatter plots or grouped bar charts to see if certain age groups or genders show stronger effects.
+
+I will use Seaborn to make clear, easy-to-read charts and provide a short written explanation for each result.
diff --git a/.ipynb_checkpoints/pyproject-checkpoint.toml b/.ipynb_checkpoints/pyproject-checkpoint.toml
new file mode 100644
index 0000000..85318b8
--- /dev/null
+++ b/.ipynb_checkpoints/pyproject-checkpoint.toml
@@ -0,0 +1,23 @@
+[project]
+name = "project-argument"
+version = "0.1.0"
+description = ""
+authors = [
+    {name = "Chris Proctor",email = "chris@chrisproctor.net"}
+]
+license = {text = "MIT"}
+readme = "README.md"
+requires-python = ">=3.10,<4.0"
+dependencies = [
+    "jupyter (>=1.1.1,<2.0.0)",
+    "seaborn (>=0.13.2,<0.14.0)",
+    "pandas (>=2.2.3,<3.0.0)"
+]
+
+
+[build-system]
+requires = ["poetry-core>=2.0.0,<3.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+package-mode = false
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..b4cb262
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb6ae43a-cbeb-45f3-8e70-ef5ccd4350a7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/argument.ipynb b/argument.ipynb
index 4ed27b4..a11635d 100644
--- a/argument.ipynb
+++ b/argument.ipynb
@@ -13,7 +13,7 @@
    "id": "understanding-numbers",
    "metadata": {},
    "source": [
-    "*✏️ Write 2-3 sentences describing your research.*"
+    "This project explores how the amount of time people spend on social media relates to their happiness. Many people use social media every day, but there is debate about whether spending more time online helps people feel connected or makes them less happy overall. This research aims to find patterns between social media time and happiness levels using survey data."
    ]
   },
   {
@@ -21,7 +21,7 @@
    "id": "greater-circular",
    "metadata": {},
    "source": [
-    "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
+    "## Overarching Question: How does time spent on social media affect happiness?"
    ]
   },
   {
@@ -29,7 +29,7 @@
    "id": "appreciated-testimony",
    "metadata": {},
    "source": [
-    "*✏️ Write 2-3 sentences explaining why this question.*"
+    "I chose this question because social media is part of my daily life, and I often wonder how it impacts how happy I feel. Some people say social media improves their mood, while others say it causes stress or comparison. I want to see whether there’s any data-driven evidence that links time online to happiness levels."
    ]
   },
   {
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "technical-evans",
    "metadata": {},
    "outputs": [],
@@ -54,14 +54,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "overhead-sigma",
    "metadata": {},
    "outputs": [],
    "source": [
     "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
     "\n",
-    "file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
+    "file_name = \"Mental_Health_and_Social_Media_Balance_Dataset.csv\"\n",
     "dataset_path = \"data/\" + file_name\n",
     "\n",
     "df = pd.read_csv(dataset_path)"
@@ -69,10 +69,141 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "heated-blade",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>User_ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Daily_Screen_Time(hrs)</th>\n",
+       "      <th>Sleep_Quality(1-10)</th>\n",
+       "      <th>Stress_Level(1-10)</th>\n",
+       "      <th>Days_Without_Social_Media</th>\n",
+       "      <th>Exercise_Frequency(week)</th>\n",
+       "      <th>Social_Media_Platform</th>\n",
+       "      <th>Happiness_Index(1-10)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>U001</td>\n",
+       "      <td>44</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>U002</td>\n",
+       "      <td>30</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>LinkedIn</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>U003</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>YouTube</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>U004</td>\n",
+       "      <td>36</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>TikTok</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>U005</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>X (Twitter)</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  User_ID  Age  Gender  Daily_Screen_Time(hrs)  Sleep_Quality(1-10)  \\\n",
+       "0    U001   44    Male                     3.1                  7.0   \n",
+       "1    U002   30   Other                     5.1                  7.0   \n",
+       "2    U003   23   Other                     7.4                  6.0   \n",
+       "3    U004   36  Female                     5.7                  7.0   \n",
+       "4    U005   34  Female                     7.0                  4.0   \n",
+       "\n",
+       "   Stress_Level(1-10)  Days_Without_Social_Media  Exercise_Frequency(week)  \\\n",
+       "0                 6.0                        2.0                       5.0   \n",
+       "1                 8.0                        5.0                       3.0   \n",
+       "2                 7.0                        1.0                       3.0   \n",
+       "3                 8.0                        1.0                       1.0   \n",
+       "4                 7.0                        5.0                       1.0   \n",
+       "\n",
+       "  Social_Media_Platform  Happiness_Index(1-10)  \n",
+       "0              Facebook                   10.0  \n",
+       "1              LinkedIn                   10.0  \n",
+       "2               YouTube                    6.0  \n",
+       "3                TikTok                    8.0  \n",
+       "4           X (Twitter)                    8.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()"
    ]
@@ -84,7 +215,7 @@
    "source": [
     "**Data Overview**\n",
     "\n",
-    "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
+    "The dataset comes from Kaggle and was collected from surveys about social media use and mental well-being. It contains responses from individuals about how much time they spend on social media each day, their happiness levels, and basic information like age and gender. This dataset allows me to explore whether heavier social media use is linked to lower happiness."
    ]
   },
   {
@@ -97,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "basic-canadian",
    "metadata": {},
    "outputs": [],
@@ -110,7 +241,7 @@
    "id": "recognized-positive",
    "metadata": {},
    "source": [
-    "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## First Research Question: Do people who spend more time on social media report lower happiness?\n"
    ]
   },
   {
@@ -132,7 +263,8 @@
     "  - *What data science tools/functions will you use and why?* \n",
     "  \n",
     "✏️ *Write your answer below:*\n",
-    "\n"
+    "\n",
+    "To answer this question, I will look at the “time spent on social media” and “happiness score” columns in the dataset. I will use Pandas to calculate averages and Seaborn to make a scatter plot showing how happiness changes with more time online. I will also calculate the correlation between the two variables to see if there is a negative relationship."
    ]
   },
   {
@@ -145,16 +277,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "negative-highlight",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Daily_Screen_Time(hrs)</th>\n",
+       "      <th>Happiness_Index(1-10)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Daily_Screen_Time(hrs)</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.705206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Happiness_Index(1-10)</th>\n",
+       "      <td>-0.705206</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Daily_Screen_Time(hrs)  Happiness_Index(1-10)\n",
+       "Daily_Screen_Time(hrs)                1.000000              -0.705206\n",
+       "Happiness_Index(1-10)                -0.705206               1.000000"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
-    "### \n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Scatter plot\n",
+    "sns.scatterplot(data=df, x=\"Daily_Screen_Time(hrs)\", y=\"Happiness_Index(1-10)\")\n",
+    "plt.title(\"Social Media Time vs Happiness Score\")\n",
+    "plt.xlabel(\"Time spent on social media (hours/day)\")\n",
+    "plt.ylabel(\"Happiness score\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Correlation\n",
+    "df[[\"Daily_Screen_Time(hrs)\", \"Happiness_Index(1-10)\"]].corr()"
    ]
   },
   {
@@ -172,7 +372,7 @@
    "id": "collectible-puppy",
    "metadata": {},
    "source": [
-    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## Second Research Question: Does age or gender affect how social media use relates to happiness?"
    ]
   },
   {
@@ -193,7 +393,9 @@
     "  - *How will you reorganize/store the data?* \n",
     "  - *What data science tools/functions will you use and why?* \n",
     "\n",
-    "✏️ *Write your answer below:*\n"
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "I will group the data by gender and compare average happiness scores at different social media usage levels. Then I will use Seaborn to make a regression plot showing trends for each gender. This will help reveal whether men and women are affected differently by social media use."
    ]
   },
   {
@@ -206,16 +408,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "pursuant-surrey",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700.25x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
-    "###\n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "sns.lmplot(\n",
+    "    data=df,\n",
+    "    x=\"Daily_Screen_Time(hrs)\",\n",
+    "    y=\"Happiness_Index(1-10)\",\n",
+    "    hue=\"Gender\",\n",
+    "    aspect=1.2\n",
+    ")\n",
+    "plt.title(\"Social Media Time vs Happiness by Gender\")\n",
+    "plt.xlabel(\"Daily_Screen_Time(hrs)\")\n",
+    "plt.ylabel(\"Happiness_Index(1-10)\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -258,7 +477,9 @@
     "- *What were limitations of your datset?*\n",
     "- *Are there any known biases in the data?*\n",
     "\n",
-    "✏️ *Write your answer below:*"
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "The results give an estimate of how social media time may influence happiness. If the correlation is weak or near zero, that suggests social media time might not strongly affect happiness for most people. However, the dataset may have limitations; for example, self-reported happiness is subjective, and the survey might not represent all age groups equally. There could also be bias in who chose to respond to the survey."
    ]
   },
   {
@@ -281,8 +502,18 @@
     "- *What was most surprising?*\n",
     "- *How will this project impact you going forward?*\n",
     "\n",
-    "✏️ *Write your answer below:*"
+    "✏️ *Write your answer below:*\n",
+    "\n",
+    "From this project, I learned more about how data can reveal patterns in daily habits like social media use. My results showed that happiness tends to decrease slightly as social media time increases, but the relationship is not very strong. This makes sense because happiness depends on many factors beyond social media. What surprised me most was how small the correlation was; it shows that time online might not be as harmful as people sometimes think. Going forward, I’ll pay more attention to how I use social media and try to balance it in a healthy way."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ebf4fe8-4f81-4d7e-8ca9-31d6e80a8441",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -310,7 +541,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.13.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/data/.ipynb_checkpoints/Mental_Health_and_Social_Media_Balance_Dataset-checkpoint.csv b/data/.ipynb_checkpoints/Mental_Health_and_Social_Media_Balance_Dataset-checkpoint.csv
new file mode 100644
index 0000000..7457ea2
--- /dev/null
+++ b/data/.ipynb_checkpoints/Mental_Health_and_Social_Media_Balance_Dataset-checkpoint.csv
@@ -0,0 +1,501 @@
+User_ID,Age,Gender,Daily_Screen_Time(hrs),Sleep_Quality(1-10),Stress_Level(1-10),Days_Without_Social_Media,Exercise_Frequency(week),Social_Media_Platform,Happiness_Index(1-10)
+U001,44,Male,3.1,7.0,6.0,2.0,5.0,Facebook,10.0
+U002,30,Other,5.1,7.0,8.0,5.0,3.0,LinkedIn,10.0
+U003,23,Other,7.4,6.0,7.0,1.0,3.0,YouTube,6.0
+U004,36,Female,5.7,7.0,8.0,1.0,1.0,TikTok,8.0
+U005,34,Female,7.0,4.0,7.0,5.0,1.0,X (Twitter),8.0
+U006,38,Male,6.6,5.0,7.0,4.0,3.0,LinkedIn,8.0
+U007,26,Female,7.8,4.0,8.0,2.0,0.0,TikTok,7.0
+U008,26,Female,7.4,5.0,6.0,1.0,4.0,Instagram,7.0
+U009,39,Male,4.7,7.0,7.0,6.0,1.0,YouTube,9.0
+U010,39,Female,6.6,6.0,8.0,0.0,2.0,Facebook,7.0
+U011,18,Female,2.8,7.0,6.0,2.0,0.0,Instagram,7.0
+U012,37,Other,5.4,5.0,7.0,3.0,2.0,Instagram,9.0
+U013,17,Female,7.0,7.0,10.0,7.0,1.0,YouTube,8.0
+U014,39,Female,5.7,5.0,7.0,4.0,0.0,Facebook,8.0
+U015,45,Male,6.3,7.0,7.0,4.0,3.0,X (Twitter),9.0
+U016,17,Female,5.1,7.0,6.0,2.0,5.0,LinkedIn,10.0
+U017,36,Female,7.5,5.0,8.0,4.0,4.0,Facebook,7.0
+U018,48,Male,5.4,6.0,4.0,3.0,4.0,TikTok,10.0
+U019,27,Male,4.7,6.0,6.0,0.0,2.0,Instagram,9.0
+U020,37,Male,7.1,6.0,6.0,5.0,4.0,TikTok,10.0
+U021,40,Female,3.0,8.0,5.0,5.0,4.0,X (Twitter),10.0
+U022,42,Other,6.4,7.0,6.0,5.0,3.0,TikTok,10.0
+U023,43,Male,3.6,8.0,5.0,3.0,1.0,Facebook,9.0
+U024,31,Female,7.8,5.0,8.0,1.0,2.0,TikTok,7.0
+U025,30,Male,5.0,6.0,6.0,0.0,5.0,X (Twitter),8.0
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+U028,36,Female,2.0,8.0,4.0,5.0,0.0,Facebook,10.0
+U029,24,Male,4.3,6.0,6.0,5.0,2.0,X (Twitter),10.0
+U030,33,Male,7.4,6.0,10.0,3.0,4.0,Instagram,8.0
+U031,19,Male,7.1,5.0,8.0,5.0,2.0,X (Twitter),9.0
+U032,40,Male,6.0,6.0,7.0,5.0,4.0,TikTok,7.0
+U033,29,Female,3.1,8.0,4.0,2.0,2.0,LinkedIn,10.0
+U034,24,Female,6.6,6.0,7.0,4.0,0.0,TikTok,7.0
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+U037,35,Female,7.7,4.0,8.0,0.0,3.0,Facebook,8.0
+U038,43,Female,4.3,8.0,4.0,1.0,1.0,TikTok,10.0
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+U048,33,Female,4.1,8.0,7.0,6.0,3.0,LinkedIn,9.0
+U049,41,Male,3.7,8.0,6.0,3.0,1.0,TikTok,9.0
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+U051,25,Female,2.0,9.0,4.0,5.0,3.0,X (Twitter),10.0
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+U053,46,Female,3.6,7.0,5.0,3.0,3.0,YouTube,10.0
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+U057,38,Female,9.7,3.0,9.0,3.0,2.0,Facebook,4.0
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+U064,44,Female,4.4,6.0,7.0,1.0,3.0,LinkedIn,9.0
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+U428,20,Male,3.2,7.0,5.0,5.0,1.0,X (Twitter),9.0
+U429,38,Male,7.6,6.0,9.0,4.0,3.0,X (Twitter),8.0
+U430,44,Male,5.0,6.0,5.0,3.0,1.0,X (Twitter),10.0
+U431,26,Female,5.0,6.0,6.0,3.0,1.0,TikTok,10.0
+U432,33,Female,6.9,6.0,7.0,0.0,4.0,Instagram,7.0
+U433,27,Male,6.8,5.0,8.0,5.0,4.0,TikTok,7.0
+U434,24,Male,4.2,8.0,7.0,5.0,3.0,X (Twitter),7.0
+U435,25,Female,6.9,7.0,7.0,0.0,2.0,TikTok,9.0
+U436,32,Other,4.5,8.0,5.0,5.0,0.0,X (Twitter),10.0
+U437,22,Female,8.8,2.0,9.0,4.0,2.0,Facebook,7.0
+U438,28,Male,4.0,7.0,5.0,1.0,3.0,LinkedIn,9.0
+U439,24,Male,5.9,7.0,6.0,1.0,2.0,Instagram,8.0
+U440,42,Male,4.1,6.0,6.0,4.0,2.0,Facebook,9.0
+U441,17,Female,9.5,4.0,9.0,2.0,2.0,YouTube,4.0
+U442,20,Male,3.3,8.0,4.0,7.0,2.0,TikTok,10.0
+U443,44,Female,6.4,5.0,6.0,5.0,3.0,YouTube,6.0
+U444,34,Male,4.2,8.0,4.0,4.0,2.0,TikTok,10.0
+U445,23,Female,4.9,7.0,7.0,4.0,5.0,LinkedIn,7.0
+U446,16,Female,6.3,6.0,7.0,6.0,0.0,Facebook,8.0
+U447,37,Female,7.7,4.0,8.0,5.0,2.0,LinkedIn,8.0
+U448,32,Male,8.4,5.0,9.0,4.0,1.0,X (Twitter),7.0
+U449,22,Male,2.7,6.0,6.0,1.0,3.0,LinkedIn,8.0
+U450,40,Male,6.0,4.0,6.0,9.0,3.0,LinkedIn,8.0
+U451,19,Female,8.1,5.0,9.0,1.0,5.0,X (Twitter),7.0
+U452,21,Female,2.5,9.0,3.0,2.0,5.0,TikTok,10.0
+U453,46,Female,6.1,6.0,8.0,0.0,1.0,X (Twitter),6.0
+U454,34,Female,5.5,6.0,5.0,4.0,3.0,Facebook,10.0
+U455,42,Female,6.2,6.0,6.0,3.0,3.0,LinkedIn,6.0
+U456,25,Male,4.1,6.0,5.0,5.0,1.0,X (Twitter),9.0
+U457,41,Female,2.9,8.0,3.0,0.0,3.0,LinkedIn,10.0
+U458,34,Male,2.1,9.0,4.0,5.0,2.0,Facebook,10.0
+U459,18,Male,7.3,5.0,8.0,1.0,1.0,YouTube,7.0
+U460,28,Female,7.5,6.0,8.0,3.0,2.0,Facebook,6.0
+U461,43,Male,6.0,6.0,5.0,5.0,1.0,Instagram,9.0
+U462,35,Male,4.5,7.0,8.0,4.0,4.0,Instagram,8.0
+U463,43,Female,2.6,9.0,5.0,4.0,5.0,Instagram,10.0
+U464,23,Male,6.5,5.0,10.0,5.0,3.0,LinkedIn,5.0
+U465,16,Female,3.5,8.0,5.0,4.0,3.0,TikTok,10.0
+U466,18,Male,4.2,7.0,7.0,2.0,3.0,TikTok,10.0
+U467,28,Male,4.4,7.0,7.0,3.0,4.0,TikTok,8.0
+U468,43,Male,5.1,6.0,6.0,3.0,4.0,LinkedIn,9.0
+U469,40,Male,4.1,6.0,7.0,1.0,2.0,Facebook,10.0
+U470,48,Female,5.6,6.0,6.0,5.0,3.0,TikTok,8.0
+U471,21,Male,4.4,5.0,6.0,4.0,3.0,YouTube,7.0
+U472,47,Female,6.8,5.0,4.0,3.0,1.0,Instagram,10.0
+U473,36,Female,7.4,3.0,8.0,5.0,4.0,Facebook,5.0
+U474,31,Female,5.7,7.0,5.0,3.0,5.0,Instagram,10.0
+U475,36,Female,6.3,7.0,7.0,3.0,4.0,TikTok,8.0
+U476,26,Female,9.3,3.0,8.0,2.0,1.0,Facebook,6.0
+U477,34,Male,3.4,8.0,5.0,4.0,0.0,X (Twitter),10.0
+U478,35,Male,3.3,9.0,6.0,4.0,4.0,YouTube,10.0
+U479,33,Male,6.8,6.0,8.0,2.0,1.0,TikTok,9.0
+U480,29,Female,9.0,4.0,10.0,3.0,3.0,TikTok,4.0
+U481,30,Male,5.0,7.0,8.0,2.0,5.0,YouTube,7.0
+U482,46,Female,8.6,4.0,8.0,2.0,2.0,YouTube,4.0
+U483,16,Female,5.7,6.0,6.0,0.0,2.0,X (Twitter),8.0
+U484,18,Male,4.3,7.0,7.0,3.0,3.0,TikTok,10.0
+U485,31,Female,3.7,7.0,7.0,0.0,1.0,LinkedIn,9.0
+U486,38,Male,6.6,6.0,8.0,2.0,1.0,TikTok,7.0
+U487,26,Male,3.9,8.0,5.0,2.0,3.0,Instagram,10.0
+U488,27,Female,5.6,7.0,7.0,5.0,2.0,Facebook,8.0
+U489,25,Female,8.4,5.0,9.0,1.0,3.0,Facebook,7.0
+U490,47,Female,4.9,7.0,7.0,1.0,7.0,Instagram,10.0
+U491,31,Male,7.9,5.0,8.0,2.0,3.0,Facebook,6.0
+U492,23,Male,3.3,10.0,4.0,2.0,1.0,YouTube,10.0
+U493,27,Female,4.5,5.0,7.0,6.0,5.0,Facebook,9.0
+U494,39,Female,3.0,7.0,2.0,1.0,0.0,Facebook,10.0
+U495,43,Female,5.6,8.0,6.0,2.0,0.0,Instagram,10.0
+U496,23,Male,6.9,5.0,7.0,4.0,2.0,X (Twitter),10.0
+U497,43,Female,5.6,7.0,6.0,5.0,2.0,Facebook,9.0
+U498,41,Male,7.7,5.0,7.0,2.0,2.0,LinkedIn,8.0
+U499,23,Male,4.2,9.0,7.0,0.0,2.0,Facebook,9.0
+U500,43,Female,5.9,5.0,8.0,3.0,3.0,X (Twitter),7.0
diff --git a/data/Mental_Health_and_Social_Media_Balance_Dataset.csv b/data/Mental_Health_and_Social_Media_Balance_Dataset.csv
new file mode 100644
index 0000000..7457ea2
--- /dev/null
+++ b/data/Mental_Health_and_Social_Media_Balance_Dataset.csv
@@ -0,0 +1,501 @@
+User_ID,Age,Gender,Daily_Screen_Time(hrs),Sleep_Quality(1-10),Stress_Level(1-10),Days_Without_Social_Media,Exercise_Frequency(week),Social_Media_Platform,Happiness_Index(1-10)
+U001,44,Male,3.1,7.0,6.0,2.0,5.0,Facebook,10.0
+U002,30,Other,5.1,7.0,8.0,5.0,3.0,LinkedIn,10.0
+U003,23,Other,7.4,6.0,7.0,1.0,3.0,YouTube,6.0
+U004,36,Female,5.7,7.0,8.0,1.0,1.0,TikTok,8.0
+U005,34,Female,7.0,4.0,7.0,5.0,1.0,X (Twitter),8.0
+U006,38,Male,6.6,5.0,7.0,4.0,3.0,LinkedIn,8.0
+U007,26,Female,7.8,4.0,8.0,2.0,0.0,TikTok,7.0
+U008,26,Female,7.4,5.0,6.0,1.0,4.0,Instagram,7.0
+U009,39,Male,4.7,7.0,7.0,6.0,1.0,YouTube,9.0
+U010,39,Female,6.6,6.0,8.0,0.0,2.0,Facebook,7.0
+U011,18,Female,2.8,7.0,6.0,2.0,0.0,Instagram,7.0
+U012,37,Other,5.4,5.0,7.0,3.0,2.0,Instagram,9.0
+U013,17,Female,7.0,7.0,10.0,7.0,1.0,YouTube,8.0
+U014,39,Female,5.7,5.0,7.0,4.0,0.0,Facebook,8.0
+U015,45,Male,6.3,7.0,7.0,4.0,3.0,X (Twitter),9.0
+U016,17,Female,5.1,7.0,6.0,2.0,5.0,LinkedIn,10.0
+U017,36,Female,7.5,5.0,8.0,4.0,4.0,Facebook,7.0
+U018,48,Male,5.4,6.0,4.0,3.0,4.0,TikTok,10.0
+U019,27,Male,4.7,6.0,6.0,0.0,2.0,Instagram,9.0
+U020,37,Male,7.1,6.0,6.0,5.0,4.0,TikTok,10.0
+U021,40,Female,3.0,8.0,5.0,5.0,4.0,X (Twitter),10.0
+U022,42,Other,6.4,7.0,6.0,5.0,3.0,TikTok,10.0
+U023,43,Male,3.6,8.0,5.0,3.0,1.0,Facebook,9.0
+U024,31,Female,7.8,5.0,8.0,1.0,2.0,TikTok,7.0
+U025,30,Male,5.0,6.0,6.0,0.0,5.0,X (Twitter),8.0
+U026,18,Other,5.7,6.0,7.0,3.0,2.0,X (Twitter),8.0
+U027,22,Female,6.2,5.0,8.0,5.0,0.0,X (Twitter),6.0
+U028,36,Female,2.0,8.0,4.0,5.0,0.0,Facebook,10.0
+U029,24,Male,4.3,6.0,6.0,5.0,2.0,X (Twitter),10.0
+U030,33,Male,7.4,6.0,10.0,3.0,4.0,Instagram,8.0
+U031,19,Male,7.1,5.0,8.0,5.0,2.0,X (Twitter),9.0
+U032,40,Male,6.0,6.0,7.0,5.0,4.0,TikTok,7.0
+U033,29,Female,3.1,8.0,4.0,2.0,2.0,LinkedIn,10.0
+U034,24,Female,6.6,6.0,7.0,4.0,0.0,TikTok,7.0
+U035,41,Other,6.8,8.0,7.0,2.0,2.0,LinkedIn,9.0
+U036,17,Other,5.6,6.0,7.0,4.0,3.0,TikTok,7.0
+U037,35,Female,7.7,4.0,8.0,0.0,3.0,Facebook,8.0
+U038,43,Female,4.3,8.0,4.0,1.0,1.0,TikTok,10.0
+U039,22,Other,4.6,4.0,6.0,5.0,2.0,Facebook,9.0
+U040,23,Female,1.0,9.0,5.0,5.0,7.0,Facebook,10.0
+U041,29,Male,7.2,5.0,7.0,4.0,1.0,X (Twitter),10.0
+U042,32,Male,6.1,5.0,6.0,3.0,3.0,Instagram,7.0
+U043,19,Male,2.5,9.0,3.0,0.0,0.0,LinkedIn,10.0
+U044,17,Other,2.3,9.0,3.0,3.0,4.0,X (Twitter),10.0
+U045,21,Female,3.9,6.0,6.0,4.0,1.0,X (Twitter),8.0
+U046,19,Male,6.9,5.0,7.0,2.0,3.0,Facebook,9.0
+U047,44,Female,5.0,4.0,7.0,2.0,1.0,Facebook,8.0
+U048,33,Female,4.1,8.0,7.0,6.0,3.0,LinkedIn,9.0
+U049,41,Male,3.7,8.0,6.0,3.0,1.0,TikTok,9.0
+U050,49,Male,3.6,7.0,5.0,4.0,1.0,TikTok,9.0
+U051,25,Female,2.0,9.0,4.0,5.0,3.0,X (Twitter),10.0
+U052,29,Female,3.3,7.0,5.0,1.0,2.0,YouTube,10.0
+U053,46,Female,3.6,7.0,5.0,3.0,3.0,YouTube,10.0
+U054,30,Female,6.7,6.0,8.0,6.0,2.0,TikTok,7.0
+U055,23,Female,5.2,6.0,6.0,7.0,3.0,YouTube,8.0
+U056,29,Female,4.0,7.0,3.0,3.0,3.0,TikTok,10.0
+U057,38,Female,9.7,3.0,9.0,3.0,2.0,Facebook,4.0
+U058,36,Female,4.3,7.0,6.0,4.0,1.0,X (Twitter),9.0
+U059,31,Male,7.6,5.0,10.0,3.0,2.0,Instagram,6.0
+U060,33,Male,8.4,4.0,8.0,6.0,4.0,X (Twitter),7.0
+U061,39,Male,2.7,9.0,5.0,1.0,3.0,TikTok,10.0
+U062,41,Female,5.6,6.0,7.0,2.0,1.0,TikTok,9.0
+U063,40,Female,4.6,7.0,7.0,1.0,3.0,X (Twitter),10.0
+U064,44,Female,4.4,6.0,7.0,1.0,3.0,LinkedIn,9.0
+U065,30,Male,6.5,4.0,9.0,4.0,3.0,TikTok,5.0
+U066,16,Male,4.4,8.0,4.0,5.0,1.0,YouTube,10.0
+U067,40,Male,4.6,7.0,6.0,2.0,1.0,YouTube,10.0
+U068,22,Male,5.2,9.0,7.0,5.0,1.0,TikTok,10.0
+U069,24,Female,4.8,6.0,7.0,5.0,2.0,TikTok,7.0
+U070,39,Male,3.4,8.0,6.0,7.0,3.0,LinkedIn,10.0
+U071,16,Male,6.9,5.0,8.0,3.0,3.0,YouTube,5.0
+U072,23,Female,7.4,6.0,7.0,2.0,3.0,TikTok,8.0
+U073,39,Male,6.1,5.0,5.0,1.0,5.0,TikTok,10.0
+U074,26,Female,4.8,6.0,6.0,1.0,4.0,LinkedIn,8.0
+U075,32,Female,4.2,8.0,8.0,5.0,2.0,LinkedIn,8.0
+U076,23,Male,4.2,7.0,5.0,0.0,1.0,X (Twitter),10.0
+U077,48,Female,9.7,4.0,8.0,5.0,1.0,LinkedIn,5.0
+U078,20,Female,7.2,6.0,9.0,1.0,2.0,Instagram,6.0
+U079,43,Other,4.0,7.0,7.0,2.0,4.0,YouTube,9.0
+U080,22,Male,5.8,6.0,8.0,3.0,2.0,X (Twitter),7.0
+U081,24,Female,4.8,6.0,7.0,2.0,2.0,Instagram,8.0
+U082,23,Female,6.6,6.0,8.0,5.0,1.0,TikTok,8.0
+U083,27,Male,6.5,7.0,9.0,7.0,2.0,TikTok,8.0
+U084,49,Male,7.5,4.0,7.0,2.0,4.0,X (Twitter),8.0
+U085,48,Other,4.0,7.0,5.0,2.0,5.0,LinkedIn,10.0
+U086,38,Female,5.7,5.0,6.0,3.0,2.0,TikTok,9.0
+U087,39,Male,2.4,7.0,4.0,4.0,2.0,Facebook,10.0
+U088,37,Female,3.2,8.0,7.0,3.0,1.0,X (Twitter),10.0
+U089,42,Male,7.1,3.0,8.0,6.0,5.0,Instagram,6.0
+U090,16,Male,3.8,10.0,6.0,5.0,0.0,X (Twitter),10.0
+U091,29,Male,4.0,6.0,4.0,1.0,4.0,YouTube,9.0
+U092,18,Male,5.5,6.0,6.0,4.0,2.0,LinkedIn,9.0
+U093,16,Male,6.8,7.0,6.0,6.0,2.0,LinkedIn,9.0
+U094,20,Male,3.6,7.0,7.0,2.0,1.0,X (Twitter),8.0
+U095,41,Female,1.0,7.0,5.0,2.0,6.0,TikTok,10.0
+U096,29,Male,3.9,7.0,6.0,2.0,3.0,TikTok,10.0
+U097,42,Male,7.7,4.0,7.0,2.0,3.0,Facebook,8.0
+U098,24,Female,6.1,4.0,9.0,0.0,4.0,Facebook,6.0
+U099,30,Female,7.9,5.0,8.0,0.0,2.0,LinkedIn,8.0
+U100,30,Female,3.5,8.0,4.0,3.0,3.0,Facebook,10.0
+U101,41,Male,2.5,9.0,4.0,6.0,3.0,Instagram,10.0
+U102,28,Male,6.3,5.0,7.0,2.0,2.0,Facebook,7.0
+U103,47,Male,5.9,4.0,7.0,2.0,4.0,Instagram,7.0
+U104,47,Male,6.0,6.0,7.0,6.0,2.0,Facebook,9.0
+U105,19,Male,7.2,5.0,6.0,1.0,0.0,LinkedIn,9.0
+U106,45,Male,6.3,7.0,8.0,0.0,2.0,LinkedIn,6.0
+U107,38,Male,6.2,7.0,7.0,1.0,3.0,Instagram,9.0
+U108,30,Male,6.8,7.0,7.0,2.0,1.0,X (Twitter),10.0
+U109,44,Male,6.4,6.0,8.0,0.0,1.0,LinkedIn,7.0
+U110,28,Male,7.0,6.0,6.0,3.0,2.0,X (Twitter),8.0
+U111,47,Male,7.3,4.0,6.0,5.0,4.0,TikTok,8.0
+U112,22,Female,8.3,5.0,10.0,0.0,2.0,Instagram,6.0
+U113,37,Female,4.3,6.0,8.0,3.0,5.0,LinkedIn,9.0
+U114,43,Male,6.4,6.0,7.0,4.0,2.0,YouTube,7.0
+U115,17,Female,4.8,7.0,6.0,3.0,3.0,TikTok,10.0
+U116,21,Female,4.0,9.0,5.0,3.0,2.0,X (Twitter),10.0
+U117,43,Male,4.3,7.0,6.0,3.0,4.0,LinkedIn,9.0
+U118,43,Female,6.4,6.0,7.0,1.0,1.0,Instagram,9.0
+U119,35,Female,6.3,6.0,7.0,6.0,4.0,Facebook,9.0
+U120,45,Male,6.2,5.0,8.0,5.0,3.0,YouTube,8.0
+U121,26,Male,5.1,6.0,5.0,5.0,2.0,X (Twitter),9.0
+U122,43,Female,5.5,8.0,6.0,5.0,2.0,TikTok,10.0
+U123,40,Female,7.0,4.0,6.0,0.0,5.0,Instagram,8.0
+U124,48,Male,2.8,7.0,5.0,5.0,3.0,YouTube,10.0
+U125,16,Male,5.5,7.0,7.0,4.0,1.0,Instagram,9.0
+U126,42,Male,5.7,7.0,7.0,0.0,1.0,LinkedIn,9.0
+U127,28,Female,6.9,5.0,8.0,3.0,3.0,Facebook,8.0
+U128,18,Female,3.1,7.0,6.0,4.0,3.0,TikTok,9.0
+U129,21,Male,8.2,5.0,7.0,6.0,4.0,Instagram,7.0
+U130,23,Male,5.9,5.0,5.0,4.0,0.0,X (Twitter),9.0
+U131,42,Female,6.0,5.0,8.0,1.0,1.0,LinkedIn,7.0
+U132,24,Male,4.8,8.0,4.0,6.0,4.0,LinkedIn,10.0
+U133,48,Male,6.5,6.0,8.0,3.0,3.0,TikTok,8.0
+U134,39,Female,5.6,8.0,6.0,2.0,3.0,LinkedIn,9.0
+U135,30,Female,7.7,4.0,10.0,2.0,1.0,X (Twitter),8.0
+U136,47,Female,2.9,8.0,5.0,0.0,1.0,Instagram,10.0
+U137,47,Female,5.8,6.0,7.0,3.0,4.0,TikTok,10.0
+U138,39,Male,6.2,5.0,8.0,4.0,4.0,LinkedIn,7.0
+U139,27,Male,5.2,7.0,5.0,2.0,1.0,LinkedIn,10.0
+U140,17,Female,4.8,8.0,5.0,4.0,1.0,X (Twitter),10.0
+U141,18,Male,4.8,6.0,5.0,1.0,2.0,Facebook,10.0
+U142,32,Male,5.8,6.0,6.0,3.0,1.0,Facebook,10.0
+U143,17,Male,2.2,10.0,5.0,4.0,1.0,LinkedIn,10.0
+U144,17,Male,7.2,7.0,8.0,3.0,1.0,Instagram,8.0
+U145,43,Male,4.6,8.0,7.0,0.0,2.0,TikTok,9.0
+U146,38,Male,5.7,7.0,5.0,2.0,1.0,LinkedIn,10.0
+U147,47,Male,6.2,5.0,7.0,6.0,1.0,YouTube,9.0
+U148,48,Male,4.6,7.0,5.0,2.0,3.0,TikTok,10.0
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diff --git a/proposal.md b/proposal.md
index da2a548..3f059fa 100644
--- a/proposal.md
+++ b/proposal.md
@@ -5,9 +5,11 @@ argument.
 
 ## Overarching Question
 
+How does time spent on social media affect happiness?
+
 ### What central question are you interested in exploring? Why are you interested in exploring this question?
 
-The central question I am interested in is I’m interested in this question because social media is a major part of daily life, especially for young people. Some say it helps them connect with others, while others say it makes them feel less satisfied or distracted. I’m curious about this topic because I use social media every day and often wonder if the amount of time I spend online affects how happy I feel. I want to explore whether people who spend more time on social media report lower levels of happiness.
+The central question I am interested in is I’m interested in this question because social media is a major part of daily life, especially for young people. Some say it helps them connect with others, while others say it makes them feel less satisfied or distracted. I’m curious about this topic because I use social media every day and often wonder if the amount of time I spend online affects how happy I feel. I aim to investigate whether individuals who spend more time on social media report lower levels of happiness.
 
 ### What specific research questions will you investigate?
 
@@ -21,11 +23,11 @@ Does age or gender affect how social media use relates to happiness?
 
 ### What data set will you use to answer your overarching question? 
 
-Dataset name: Mental Health & Solcial Media Balance Dataset
+Dataset name: Mental Health & Social Media Balance Dataset
 
 Data source: Kaggle (public dataset)
 
-About the data: The dataset includes information about how many hours people spend on social media each day, their reported happiness levels, and demographic details such as age and gender.
+About the data: The dataset includes information on the number of hours people spend on social media each day, their reported happiness levels, and demographic details such as age and gender.
 
 ### Where is this data from?