Hcrystalball

Abstract

1. Introduction

A library that unifies the API for most commonly used libraries and modelling techniques for time-series forecasting in the Python ecosystem.

and add convenient layer over whole process (access to results, plots, storage, ...)

See examples, tutorials, contribution, API and more on the documentation site try notebooks on binder or browse example notebooks in docs/examples directly.

2. Objective

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

This project demonstrates how Python, scikit-learn can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/heidelbergcement/hcrystalball.git
cd hcrystalball

pip install hcrystalball

conda install -c conda-forge hcrystalball

# get dependencies file, e.g. using curl
curl -O https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/environment.yml
# check comments in environment.yml, keep or remove as requested, than create environment using
conda env create -f environment.yml
# activate the environment
conda activate hcrystalball
# if you want to see progress bar in jupyterlab, execute also
jupyter labextension install @jupyter-widgets/jupyterlab-manager
# install the library from pip
pip install hcrystalball
# or from conda
conda install -c conda-forge hcrystalball

git clone https://github.com/heidelbergcement/hcrystalball
cd hcrystalball
conda env create -f environment.yml
conda activate hcrystalball
# ensures interactive progress bar will work in example notebooks
jupyter labextension install @jupyter-widgets/jupyterlab-manager
python setup.py develop

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by heidelbergcement and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.