Installing Cookiecutter data science project template

In this post, we will learn how to install the cookie cutter data science template .

1.Login to your local machine Github account using Git-Bash(Windows) or  Git terminal(Mac)

2.Ensure you have Python 3.X in your machine.

3.Create a folder called project.Just type pip install cookiecutter and hit enter.

how to install cookiecutter

So this will install cookiecutter , which we will in turn use to install the cookie cutter data science template.

4.After the command in step 3 is completed, install the  cookiecutter data science template folder structure from GitHub , using the command below.

Hit enter. You will see a couple of questions on the project_name, repo_name, description etc.

cookiecutter data science project set up

It will ask for a project name. We will just call it “demo”.
We will not give any repo_name for now.

5.Just enter the author name as demo , description as “demo purpose”.

Then it will ask for a license, so we will select the first option, namely MIT (Option 1).
Select the python_interpreter option 1, because we are using Python 3.X.
Hit enter. That’s it. The installation is complete.

You can see that it has created a demo folder for us, so let’s just go into demo.

6.Change the current directory to demo , and then  type ls now. We can see the directory structure with all the necessary files and folders created , as shown in this GitHub link.

You can go through the description for each of the directory and file created  here.

directory structure of the cookiecutter data science project

As you can see , at the top level, we’ve got references, models, docs, data, notebooks, reports and src.

So in this post,  we learned how to install the cookie cutter data science template. Then I’ve also showed you the descriptions of the various folders and files created within.

I hope you find this post informative.Please do share the article if you like it 🙂

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