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This article mainly explains "what are the advantages of Kaggle". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn what are the advantages of Kaggle.

Kaggle

On Kaggle, people can share ideas, get inspiration, compete with other data scientists, learn new things and programming skills, and observe many examples of real data science applications. There are many datasets that can be used in a variety of situations, such as video game sales or complex and important data such as air pollution.

Taken from the real world and referenced several times, these data can be used to train and evaluate project models that are really helpful to people. Kaggle also has many useful features, such as data, code, community, inspiration, competitions, and courses.

advantage

Kaggle has many advantages, which are all reasons why you should use Kaggle.

1. data

There are some datasets available on Kaggle. The file formats of most datasets are CSV,JSON, SQLite, archives, and BigQuery. Datasets are less common, but still have a wide range of uses. It is helpful to practice using multiple file formats, which you may encounter at work. Here are three of the hottest datasets today.

COVID-19 Open Research data set Challenge

UNCOVER COVID-19 Challenge

ProZorro. Ukrainian public procurement data set

two。 Code

There is a lot of code on Kaggle. If you want to see countless pieces of code from other Kaggle users, it's easy to search on Notebooks, including the code and most users' comments on the code. This can help us learn and practice, and learn from the way others deal with similar problems. Most people use Python for programming, but some use other programming languages, including R, SQLite, and Julia.

The code is usually in the form of notepad (also known as Jupyter Notebook) and is saved in a file with the suffix .ipynb. There are some examples of end-to-end machine learning models, including data extraction and cleaning, exploratory data analysis, feature engineering, basic model creation, final machine learning model implementation, and output and interpretation of results.

Most data scientists often use these steps in their work because it is a simple and easy-to-follow process for other data scientists and related workers.

Code languages supported on Kaggle: Python, R, SQLite, and Julia

3. Community

Like Medium, GitHub, Stack Overflow, and LinkedIn, Kaggle serves as a community in which data analysts, data scientists, and machine learning engineers can learn, grow, and interact.

You can post your work (such as data, code, and notepad files) on it to share with others to develop your own community. Being a member of the community has its unique advantages, so the author strongly suggests that we should take Kaggle as a starting point to cultivate our own community and establish ties with other communities.

4. Inspiration

You can get great inspiration through the data, code, communities, courses and competitions here. It is really inspiring to watch others participate in competitions that are beneficial to others and enterprises and enjoy their wonderful performance.

If you are thinking about what to learn next, or how to implement a function, maybe you should take a look at how others implement a module, such as a random forest in a specific way. You can find these on Kaggle and eventually motivate yourself to climb another story.

5. Competition

If you want to test your level, know your ranking among your peers, enrich your resume or earn some extra money, competition is the right choice. Kaggle offers countless competitions, and the screenshot above shows the top three contests and their corresponding prizes. These competitions are not only interesting, but also of far-reaching significance (such as helping the cause of health).

The author makes use of the competitions on Kaggle to compare his performance before and after. At first, the author has only a little knowledge of data science; later, I have nearly two years of experience in data science. Compared with the two, the expected progress is remarkable.

The author ranks in the top 50% of nearly 8000 people, which is not bad for newcomers. It is not difficult to see how much contribution can be made to identifying projects with health abnormalities when thousands of data scientists compete for the same goal.

Kaggle can provide a wealth of resources for enterprising excellent data scientists, so don't miss it.

Thank you for your reading, the above is the content of "what are the advantages of Kaggle". After the study of this article, I believe you have a deeper understanding of the advantages of Kaggle, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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