Computational workflow in Python for the iris dataset

In this example we'll load, process, visualize, and train a classifier for the iris dataset, containing examples of different flowers and their physical characteristics.

Setup

1. Install Anaconda for your system.

2. Create a conda environment using conda env create -f env.yml (from this folder).

Run

1. Run jupyter notebook from this folder.

2. Open notebook.ipynb and run the cells.

3. The result, model.png, will be written to disk.

4. Validate your model using validate_hypothesis in the final cell.

5. Optionally save your model to .csv files using the notebook. This can be opened in any language or spreadsheet viewer.