Iris Dataset - Exploratory Data Analysis

This repository contains a small exploratory analysis of the classic Iris dataset using Python, pandas, matplotlib, and seaborn.
The project focuses on a reproducible, script-based workflow (no Jupyter required).

Quick Preview

Example scatter plot generated by the script:

Project Structure

• iris_analysis.py → Python script: load dataset, run basic analysis and (optionally) export plots
• iris_analysis.ipynb → Colab notebook with interactive exploration and visualizations
• images/ → Sample visualization images (committed for preview)
• requirements.txt → Python dependencies
• README.md → Project documentation

Requirements

Create a virtual environment and install dependencies:

python -m venv venv
# Windows PowerShell
venv\Scripts\Activate.ps1
# Linux / macOS
# source venv/bin/activate

pip install -r requirements.txt

How to Run

Run the analysis script (prints dataset information and summary to console):

python iris_analysis.py

Generate and save example plots to images/:

python iris_analysis.py --save-figs

Analysis Performed

• Dataset information and missing-value checks
• Descriptive statistics: (df.describe())
• Group-by species: mean measurements per class
• Visualizations:
  • Line chart: Sepal length and width trends
  • Bar chart: Average petal length by species
  • Histogram: Distribution of petal width
  • Scatter plot: Sepal length vs petal length (colored by species)

Insights

• Setosa has significantly smaller petal length and width compared to other species
• Virginica generally shows the largest values across features
• No missing values were detected; the dataset is clean and consistent

License

This project is distributed under the MIT License. See LICENSE.

About the Author

• Name: Augusto Mate
• E-mail: mate.augusto.mz@gmail.com

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Version 1.0.0 has been released! See the release page for full details.