Files
project_argument/.ipynb_checkpoints/pyproject-checkpoint.toml
angelotr 9af355b539 I successfully uploaded my dataset into Jupyter Lab and created visualizations to explore my research question about social media and happiness. I used Seaborn to make a regression plot showing the relationship between daily screen time and happiness, separated by gender. The plot shows a clear negative trend—people who spend more time on social media tend to report lower happiness levels—and this pattern is consistent across genders.
After completing this project my recent success that I am proud of is
that I was able to create visualizations with the data and topic I
chose. This project has sparked the idea of how I could use this
for sport statistics like one of my peers did. A new skill I learned
doing this project was creating visualizations for the data I chose.
2025-11-04 14:24:09 -05:00

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TOML

[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