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.

.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