DataFun-04-EDA 📌 Project Overview

This repository is for CC4.3: Create a Jupyter Notebook and Select a Kernel. The project shows how to create and organize a Jupyter Notebook, run Python and Markdown cells, and perform a simple Exploratory Data Analysis (EDA) using the Iris dataset.

📂 Files in this Repo

cc4_3_notebook.ipynb → The main Jupyter Notebook with code, Markdown, charts, and insights.

README.md → Project description and workflow notes.

requirements.txt → List of Python packages used.

.gitignore → Files and folders excluded from Git.

🛠️ What I Did

Created a new notebook and selected the correct .venv kernel in VS Code.

Added Markdown cells for structure and explanations.

Imported libraries: pandas, seaborn, matplotlib.

Loaded the Iris dataset and explored rows, columns, and dtypes.

Generated descriptive statistics and distributions.

Created new features (Sepal Area, Petal Area).

Built visualizations (histograms, scatter plots, pairplots).

Added written observations after each chart.

📊 Key Insights

Setosa species is clearly distinct due to smaller petal size.

Versicolor and Virginica overlap but still show noticeable differences.

Feature engineering (Sepal Area & Petal Area) provides extra ways to analyze the flowers.

🚀 Tools Used

Python

Jupyter Notebook (VS Code)

Pandas

Seaborn

Matplotlib

🔗 Repository

➡️ GitHub Repo – datafun-04-eda