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How To Make Your Data Science Projects Stand Out

Create an effective README

Photo by Markus Winkler on Unsplash
Photo by Markus Winkler on Unsplash

Introduction

So you’ve created some dope modular code, got an extremely accurate model with a small inference time, and you’ve pushed your code to Github – You are sitting on the clouds.

Unfortunately, this is where the majority of open source projects end. Before I tell you why it is so unfortunate, let me explain what open source is.

What is Open Source?

Open source refers to something people can modify and share because its design is publicly available. When you push your project to a public repository on Github you have contributed to open source, and now anyone can inspect, modify and enhance your code.

The motivations as to why people do open source projects vary. Some may want to get some hands on experience of working on a real project, some just love coding and some want to make the world a better place but one thing holds true, no matter the motivation behind the developer.

People must know how to engage with your project!

It’s unfortunate when code is pushed to Github and nobody knows how to engage with your project and Github know this so they made provision with something called a README which is not utilized enough.

Getting 101% accuracy on the Titanic Dataset means nothing if People do not know how to engage with your code!

The solution to this is simple, yet it will put you miles ahead of many other people that think purely building an ensemble of 3000 models to squeeze out an extra 1% of accuracy is enough to get them noticed.


How to Write a README…

We’ve established that a project without a README is not useful as it provides no insight into what has been built – we aren’t making the project accessible to as many people as possible. The question is now, How do we actually write a good README.

If you are anything like me, the reason you didn’t write a README is because you don’t know how to. Hence, I will be showing you exactly what I done to learn… Take a look at the guide below:

Yeah I know it’s a lot! But the next part is simple. What’s 3 popular frameworks used in Data Science:

I’ve linked to the Github profiles of each framework, all that is left for you to do is to visit each one of the Github repositories and read their README – It’s as simple as that (Bare in mind you have the Sample to assist you if you’re unsure)!

Now that you’ve got the swing of what to write on your README, you’ll want to add some formatting to give it that extra nudge, so below I will link to the best resource on the internet telling you about Github formatting.

Basic writing and formatting syntax


Wrap Up

In many situations building a better model is enough to get you noticed for instance on Kaggle. However, many people are shipping tons of source code to Github every single day and even if you have something that looks like it may be interesting, without a README people will be clueless of how to navigate around your work. Your job is to make the task of engaging with your project as simple as possible and this simple change will make your Project stand out.

Let’s continue the conversation on LinkedIn…

Kurtis Pykes – AI Writer – Towards Data Science | LinkedIn


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