🛠️ Data Science and Machine Learning 🤖

A Data Project Template for Building Robust, Reproducible and Maintainable Predictive Solutions

guane enterprises

About • Features • Life Cycle • Contribute • Authors • License

About

ds-ml-project-template is a Python library for training, testing and reporting of the FTL-Pricing predictive models. This Python library is designed to training and generate the machine and deep learning models that predicts base transportation cost of FTL modality in United States & Canada.

Features

ds-ml-project-template is built on Python 3.9 with pandas, numpy and scikit-learn, matplotlib, seaborn, plotly among others, to preprocess the data, build the machine learning models, and visualize the results.

For development, the library use:

Formatting with black
Import sorting with isort
Linting with flake8
Git hooks that run all the above with pre-commit
Testing with pytest

Life Cycle

As a proposal for the data science life cycle, OSEMN is mainly proposed. Standing for Obtain, Scrub, Explore, Model, and iNterpret, OSEMN is a five-phase life cycle.

Other good option is Microsoft TDSP: The Team Data Science Process combines many modern agile practices with the life cycle. It has five steps: Business Understanding, Data Acquisition and Understanding, Modeling, Deployment, and Customer Acceptance.

The important thing is that if you think they should be combined and form their own life cycle, feel free to do so.

Contribute

First, make sure that before enabling pipenv, you must have Python 3.9 installed. If it does not correspond to the version you have installed, you can create a conda environment with:

# Create and activate python 3.9 virutal environment
$ conda create -n py39 python=3.9
$ conda activate py39

Now, you can managament the project dependencies with Pipenv. To create de virtual environment and install all dependencies follow:

# Install pipx if pipenv and cookiecutter are not installed
$ python3 -m pip install pipx
$ python3 -m pipx ensurepath

# Install pipenv using pipx
$ pipx install pipenv

# Create pipenv virtual environment
$ pipenv shell

# Install dependencies
$ pipenv install --dev

Once the dependencies are installed, we need to notify Jupyter of this new Python environment by creating a kernel:

$ ipython kernel install --user --name KERNEL_NAME

Finally, before making any changes to the library, be sure to review the GitFlow guide and make any changes outside of the master branch.

Authors

👤 guane Data Science and Machine Learning (DS&ML) Team