Linkit Beginner Challenge: Explainable ML using SHAP values

Repository for the Linkit Beginner Challengeon Explainable ML using SHAP values.

Challenge Description

The goal of this challenge is to build a model that predicts whether a customer will subscribe to a term deposit or not and to explain the model using SHAP Values.

The dataset is from the UCI Machine Learning Repository and contains information about customers of a Portuguese bank. The target variable is deposit and has two possible values: yes and no. For more information refer to the challenge.ipynb notebook.

Evaluation

Your submissions will be evaluated based on the following criteria:

1. Accuracy on a hold-out test set
2. Explanation of the predictions: For a few selected test points, you will have to explain with SHAP values why the model made the prediction it did.

Submissions

Instructions

You can either develop locally by cloning the repo or work in a cloud environment like Google Colab.

Google Colab (Recommended)

You can use Google Colab to run the notebook.

To get the data inside colab, use the following commands inside a cell (make sure to prefix the line with ! as it is shown here):

!pip install dvc
!dvc get https://github.com/MoritzM00/Linkit-Beginner-Challenge-Explainable-ML data/bank_train.csv -o data/bank_train.csv
!dvc get https://github.com/MoritzM00/Linkit-Beginner-Challenge-Explainable-ML data/bank_test.csv -o data/bank_test.csv

This installs dvc and downloads the data from the repository.

You may have to install some missing dependencies, that are not pre-installed in the colab environment like shap:

!pip install shap

Local Development

For local development, you can fork this repository and run the following commands to set up a virtual environment and install the required dependencies. You must have python installed on your machine. I used Python 3.10.7 for this project.

You can use your preferred method of creating a virtual environment, but we recommend using the builtin venv:

python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

Pull the data from the remote storage by running:

dvc pull

Optionally, install development dependencies and pre-commit hooks:

pip3 install -r requirements-dev.txt
pre-commit install

These hooks will run on every commit and will check for linting errors and formatting issues. Optionally, you can remove the output of all cells to make it easier for version control. This part is commented out at the moment.