[Regression] Add Seaborn data visualization examples to the Data lesson

[Copilot]

Description

The 2-Regression/2-Data lesson taught Matplotlib and its assignment asked learners to compare Matplotlib with Seaborn — but Seaborn was never actually demonstrated anywhere in the curriculum. This adds a hands-on Seaborn section to that lesson, closing the gap using the existing pumpkin dataset.

• README — New "experiment with Seaborn" section covering the visualization types from the issue:
  • Scatter/relational plots (sns.relplot) for correlations
  • Categorical bar plots (sns.catplot) for distributions
  • Correlation heatmap (sns.heatmap), noting the same chart backs confusion matrices in classification
  • A short "Matplotlib or Seaborn?" comparison; updated learning objectives
• Solution notebook — Added import seaborn as sns plus matching runnable cells kept in sync with the README
• Images — Four plots (relplot, lineplot, catplot, heatmap) generated from the actual lesson data to match existing lesson style

import seaborn as sns

sns.relplot(x="Price", y="Month", data=new_pumpkins)          # correlations
sns.catplot(x="Month", y="Price", data=new_pumpkins, kind="bar")  # distributions
correlations = new_pumpkins[['Month', 'Low Price', 'High Price', 'Price']].corr()
sns.heatmap(correlations, annot=True, cmap="coolwarm")        # correlation matrix

The heatmap discussion intentionally highlights that Month shows weak linear correlation despite the visible seasonal peak — reinforcing that the coefficient misses non-linear patterns. translations/ untouched (auto-generated).

Track translation progress by opening a draft PR using this template and checking off the translations completed

Each lesson includes a translation of the README.md and the Assignment.md file, if available. Only mark the lesson complete if both those files are translated per lesson, please.

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• 2

• 2-1

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• 3

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• 7-1

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• Quiz (add a file in the quiz-app with all localizations)

[Copilot]

Pull request overview

This PR updates the 2-Regression/2-Data lesson to include hands-on Seaborn visualization examples, addressing the prior gap where the lesson referenced Seaborn in the assignment without demonstrating it in the curriculum.

Changes:

• Added a new Seaborn exercise section to the lesson README (relational plots, categorical bar plots, correlation heatmap, and a Matplotlib vs Seaborn comparison).
• Updated learning objectives and the challenge prompt to include Seaborn alongside Matplotlib.
• Updated the solution notebook to import Seaborn and include matching Seaborn plotting cells aligned with the README.

Show a summary per file

File	Description
2-Regression/2-Data/README.md	Adds a Seaborn exercise section and updates objectives/challenge to include Seaborn visualization.
2-Regression/2-Data/solution/notebook.ipynb	Adds import seaborn as sns and Seaborn plotting cells mirroring the README instructions.

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• Files reviewed: 2/6 changed files
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• Review effort level: Low