ki Sprint 43c: Predictive models for the BEAN dataset

The code and result figures for sprint 43c are available in this repository. You'll have to bring your own data if you want to rerun the models.

Installation Instructions

The easiest way to manage a Python installation is to use Anaconda.

First install a Python 3.x miniconda on your system: https://docs.conda.io/en/latest/miniconda.html

Once miniconda is installed, open a miniconda shell/terminal, and execute the following commands to install all the necessary packages:

conda create -n 43c python=3.8 -y
conda activate 43c
conda install cython scikit-learn numpy pyarrow scipy pandas matplotlib seaborn ipython jupyterlab -y
pip install lightgbm hyperopt mlxtend scikit-optimize

Data

See the data files referenced in the file named 01_*

Code Organization

Before running any scripts you'll have to activate your conda virtual environment with conda activate 43c.

The files that begin with 01_*, 02_*, etc are the main scripts. The scripts should be run in order:

• 01_import_data.py - Once you stick your data into the data/ folder, you can run this script with python 01_import_data.py. It will convert the raw data into a machine learning ready format.

• 02_run_ml_models.py - This will run all of the models, and save all of the results. It takes about 1 to 2 hours. Run with python 02_run_ml_models.py.

• 03_visualize.ipynb - This is a Jupyter notebook. To run, you'll have to execute jupyterlab in your terminal. You will then see an address pop-up. Copy and paste that into your browser and you'll see the notebook pop-up. You can re-execute all of its cells to update the results. For more on JupyterLab see: https://jupyterlab.readthedocs.io/en/stable/

The rest of the files have various code utilities used by the numbered scripts. They are imported when necessary.