美团脱颖而出的经验
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.
因此，您已经创建了一些涂料模块代码，以极短的推理时间获得了极其准确的模型，并且已将代码推到Github上了–您正坐在云上。
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.
不幸的是，这是大多数开源项目结束的地方。 在我告诉您为什么如此不幸之前，让我解释一下什么是开源。
What is Open Source?
什么是开源？
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.
开源是指人们可以修改和共享的东西，因为它的设计是公开可用的。 当您将项目推送到Github上的公共存储库时，您就为开源做出了贡献，现在任何人都可以检查，修改和增强您的代码。
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.
人们为什么进行开源项目的动机各不相同。 有些人可能想获得一些在实际项目上工作的经验，有些人只是喜欢编码，有些人希望使世界变得更美好，但是不管开发人员的动机如何，都有一件事是正确的。
People must know how to engage with your project!
人们必须知道如何参与您的项目！
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.
不幸的是，当代码被推送到Github时，没人知道如何与您的项目进行交互，而Github知道这一点，因此他们使用称为README的东西进行了准备，但使用得不够充分。
Getting 101% accuracy on the Titanic Dataset means nothing if People do not know how to engage with your code!
如果人们不知道如何使用您的代码，那么在泰坦尼克号数据集上获得101％的准确性将毫无意义！
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.
解决方案很简单，但您将比其他许多人领先得多，他们认为，仅建立3000个模型的集合以挤出1％的精度就足以引起他们的注意。
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.
我们已经确定没有自述文件的项目没有用，因为它无法深入了解已构建的内容-我们并没有使尽可能多的人可以访问该项目。 现在的问题是，我们如何实际编写良好的自述文件。
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:
如果您像我一样，之所以没有编写自述文件，是因为您不知道该怎么做。 因此，我将向您确切说明我所做的学习...看下面的指南：
Yeah I know it’s a lot! But the next part is simple. What’s 3 popular frameworks used in Data Science:
是的，我知道很多！ 但是下一部分很简单。 数据科学中使用的3种流行框架是什么：
Pandas Github
熊猫Github
NumPy Github
NumPy Github
Scikit-Learn Github
Scikit-学习Github
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)!
我已经链接到每个框架的Github配置文件，剩下要做的就是访问每个Github存储库并阅读其自述文件-就是这么简单(请记住，示例供您参考)如果您不确定)！
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.
既然您已经在自述文件中写了些什么，现在您将想要添加一些格式来增加一些额外的内容，因此下面我将链接到互联网上最好的资源，告诉您有关Github格式的信息。
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.
在许多情况下，建立一个更好的模型足以使您在Kaggle上受到关注。 但是，许多人每天都会向Github发送大量源代码，即使您有一些看起来很有趣的东西，如果没有README，人们也将不知道如何浏览您的工作。 您的工作是使与项目互动的任务尽可能简单，而这种简单的更改将使您的项目脱颖而出。
Let’s continue the conversation on LinkedIn…
让我们继续在LinkedIn上进行对话…
翻译自: https://towardsdatascience.com/how-to-make-your-data-science-projects-stand-out-b91d3861a885
美团脱颖而出的经验
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