marcusinthesky / zindi_loan_default

SuperLender is a local digital lending company, which prides itself in its effective use of credit risk models to deliver profitable and high-impact loan alternative. Its assessment approach is based on two main risk drivers of loan default prediction:. 1) willingness to pay and 2) ability to pay. Since not all customers pay back, the company invests in experienced data scientist to build robust models to effectively predict the odds of repayment

zindi_load_default

Environment

This repository makes use of Git version control. Precommit hooks have been used to provide checks to the developer on the heath and conformity of their code. Here, we make use of Black for automatic formatting, flake8 for linting and jupytext to automatically convert our notebooks to py:percent files for better version control. Our environment has been setup using Anaconda. You can find an explicit environment file in the src directory. We have provideded a Dockerfile and compose to aid in portabilit of our environment. This project makes use of kedro for data versioning, project templating, data pipelining, automatic documentation, data catalogingm packaging and context management. Please refer to conf/base/catalog for speific details on how our data and models were extracted, serialized and versioning. The majority of our analysis has relied on consumer hardware with 16GB of Micron MT53E1G32D4NQ-046 memory clocked at 4267MHz and a Quadcore Intel R Core TM i7-1065G7 processor clocked at 1.30GHz, running Ubuntu 20.04 using linux kernel 5.4.0-48-generic. At the release of our work, we are unaware of any known issues in the software and hardware used by our analysis which may affect the reliability of our results.

Model Serving

We have relied on MLFLOW (https://www.mlflow.org/docs/latest/models.html) and kedro-mlflow (https://github.com/Galileo-Galilei/kedro-mlflow) for model serving, which provide a simple specification for model serving on Azure, Sagemaker and Databricks. You can launch our baseline model as a REST API locally using mlflow models serve -m mlruns/1/. and navigate tracked artefacts using kedru mlflow ui.

Overview

This is your new Kedro project, which was generated using Kedro 0.16.5.

Take a look at the Kedro documentation to get started.

Rules and guidelines

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.

To install them, run:

kedro install

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:

kedro test

To configure the coverage threshold, go to the .coveragerc file.

Project dependencies

To generate or update the dependency requirements for your project:

kedro build-reqs

This will copy the contents of src/requirements.txt into a new file src/requirements.in which will be used as the source for pip-compile. You can see the output of the resolution by opening src/requirements.txt.

After this, if you'd like to update your project requirements, please update src/requirements.in and re-run kedro build-reqs.

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: context, catalog, and startup_error.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run kedro install you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to convert notebook cells to nodes in a Kedro project

You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.

By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/:

kedro jupyter convert <filepath_to_my_notebook>

Note: The name of the Python file matches the name of the original notebook.

Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:

kedro jupyter convert --all

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Package your Kedro project

Further information about building project documentation and packaging your project