Iris Insights

Iris Insights is a compact yet polished data science project designed to highlight end-to-end workflow skills for a portfolio-ready GitHub repository. The project uses the classic Iris dataset to demonstrate exploratory data analysis (EDA), feature engineering, model training, and automated reporting.

Project Structure

.
├── notebooks/
│   └── iris_analysis.ipynb      # Narrative analysis and visualisations
├── reports/
│   └── figures/                 # Automatically generated plots
├── scripts/
│   └── run_pipeline.py          # End-to-end training and reporting script
├── src/iris_insights/           # Reusable Python package
│   ├── data.py
│   ├── evaluation.py
│   ├── features.py
│   └── model.py
└── tests/
    └── test_pipeline.py

Getting Started

1. Install dependencies

   python -m venv .venv
   source .venv/bin/activate
   pip install -e .

2. Run the automated pipeline

   python scripts/run_pipeline.py

   The script saves a text report summarising the model performance in reports/model_report.txt and exports figures to reports/figures/.

3. Explore the notebook

   Open notebooks/iris_analysis.ipynb to follow the exploratory narrative. The notebook walks through the project motivation, visualisations, and modelling decisions.

Testing

Run the unit tests with:

pytest

Why This Project Stands Out

• Reproducible: Deterministic data loading via scikit-learn with clear preprocessing steps and configuration.
• Well-documented: Modularised code and narrative notebook explain each decision, making it easy for reviewers to follow.
• Visual and analytical: Automated figure generation and detailed performance reporting demonstrate both storytelling and technical depth.

Feel free to extend the project with additional models, experiment tracking, or deployment artifacts to further showcase your skills.