This repository contains a Jupyter Notebook (iris_analysis.ipynb) that explores and analyzes the Iris dataset 🌱—a classic dataset in machine learning and statistics. Using Python (pandas, matplotlib, seaborn, scikit-learn), the notebook demonstrates best practices in data analysis, visualization, and insights sharing.
✨ Inspired by Ubuntu’s principle of community, this project emphasizes clarity, simplicity, and shareability.
The Iris dataset includes 150 samples of iris flowers, each described by:
- 4 numerical features: sepal length, sepal width, petal length, petal width
- 1 categorical label: species (setosa, versicolor, virginica)
The notebook walks through:
- ✅ Loading and exploring the dataset
- 📈 Computing descriptive statistics & grouping by species
- 🎨 Creating four visualizations (line chart, bar chart, histogram, scatter plot)
- 🔎 Summarizing key findings (e.g. petal length is a strong discriminator between species)
- Python 3.10+
- Libraries:
pandas matplotlib seaborn scikit-learn jupyter