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Table of contents
About Kaggle 
Importance of Kaggle
Kaggle Competitions
Kaggle Datasets
Kaggle Notebooks
Kaggle Public API
Frequently Asked Questions
What is Kaggle?
Is Kaggle free?
Who founded Kaggle?
What are the different types of competitions held in Kaggle?
Last Updated: Mar 27, 2024


Author Aniket Majhi
1 upvote
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


In today’s computing world, Data Science is a consistent and reliable concept for dealing with real-life problems. If you are a Data Science or machine learning enthusiast, then this blog will be helpful for you.

If you want to master Data Science and Machine Learning, you can refer to our Data Science & Machine Learning Course.
In this article, we will be discussing the Kaggle. We will be showing how it helps data scientist enthusiasts practice and compete with fellow data scientists worldwide.

No matter your level of expertise in the data science field, you can use Kaggle to grow yourself, even if you are a beginner.

So, without further ado, let’s start the topic.

About Kaggle 

Kaggle is an online community for data scientists and machine learning enthusiasts. 

Kaggle allows users to collaborate and compete with other users to solve real-world problems. 

Kaggle was founded in 2010 by Anthony Goldbloom and Jeremy Howard. Later it was acquired by Google in 2017. This platform aims to help learners and professionals reach their goals in data science with powerful tools and resources and attract, train, and challenge data scientists worldwide to solve real-world problems.

Now Kaggle is a fast-growing community. It has more than 8 million registered users as of 2021. It also spans over 194 countries.

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Importance of Kaggle

The importance of using Kaggle is shown below:

  1. Most data scientists are generally theorists and rarely get a chance to practice. Kaggle is an essential platform for data scientist enthusiasts to learn, compete and practice real-world problems.
  2. Kaggle makes data scientists and other developers engage in running machine learning contests, writing and sharing code, and hosting the datasets.
  3. Kaggle makes the contests more competitive and exciting by awarding prizes and ranking to the participants.
  4. Kaggle provides powerful resources on the cloud. You can upload your dataset on the Kaggle, and also, you can check other people’s datasets and notebooks and download them.
  5. Kaggle provides insightful discussions from experts and industry leaders. Through these discussions, you can seek advice or advise those dealing with issues that you understand.
  6. Kaggle is suitable for different people, from beginners interested in data science and artificial intelligence to the most experienced data scientists globally.
    For beginners, you can learn from the courses provided by Kaggle. In Kaggle, you can progress in a community of people of various levels of expertise, and you will get a chance to communicate with experienced data scientists.
  7. Kaggle's experience also makes a positive impact if you apply for a data science job.

Kaggle Competitions

Kaggle competitions are the machine learning tasks provided by Kaggle or any other company. Kaggle provides different kinds of competitions. These can be classified into given categories, i.e. Getting StartedPlaygroundFeaturedResearch and Recruitment.

  1. Getting Started: The Getting Started competition is meant to be the new users or beginners just getting their foot in the door in machine learning. It is the easiest and most approachable semi-permanent contest on Kaggle. They offer no prizes or points. There are an ample number of tutorials for this contest.
  2. Playground: The Playground competitions in Kaggle are one step above the Getting Started in terms of difficulty. Here prizes range from kudos to small cash prizes.
  3. Featured: The Featured competitions in Kaggle are full-scale machine learning challenges. Kaggle is best known for its Featured competition. It consists of complex prediction problems, generally with a commercial purpose. Featured competitions attract some of the most formidable experts and offer prize pools around million dollars.
  4. Research: The Research competitions are one of the common competitions in Kaggle. It involves problems that are most experimental that feature competition problems. They do not offer prizes or points.
  5. Recruitment: In a Recruitment competition, a team of size one compete to build machine learning models for corporation-curated challenges. Interested participants can upload their resumes at the competition’s close for consideration by the host. The prize for this competition is a job interview from the host.

Kaggle Datasets

Kaggle host a huge number of datasets. Kaggle supports various dataset publication formats, including 

  1. comma-separated values(CSV): It is the simplest and best-supported file type. It is mainly used for tabular data.
  2. JavaScript Object Notation(JSON): It is used for “tree-like” data that potentially has multiple layers.
  3. SQLite: Kaggle supports database files containing multiple tables, each containing data in tabular format.
  4. ZIP and 7z archives:  Kaggle has first-class support for compressed files using the ZIP file format or 7z. If the dataset is large enough, is made up of many smaller files, or is organised into subfolders, you can only upload your data in archives. 
  5. BigQuery: Bigquery is a “big-data” SOL store invented by Google. They are multi-terabyte datasets hosted on Google’s servers. Many massive public datasets are available publicly through the Google Bigquery Public Datasets. 

Kaggle Notebooks

Kaggle supports three different types of notebooks: Scripts, RMarkdown Scripts and Jupyter Notebook.

  1. Scripts: Scripts are files that execute everything as code sequentially. You can write scripts in Python or R
  2. RMarkdown Scripts: RMarkdown scripts are the particular type of scripts executed in R and RMarkdown code.  RMarkdown code is the combination of R code and Markdown editing syntax.
  3. Jupyter Notebooks: Jupyter Notebooks simply consist of a sequence of cells. Each cell is formatted in either Markdown or a programming language. You can write Notebooks in either R or Python.

If you want to search for notebooks, you can use the site search on the website or by browsing the Kaggle homepage.

Kaggle Public API

You can easily interact with the Kaggle from your local machine using the Kaggle command-line tool(CLI) implemented in Python. It calls the Kaggle public API.

You can install the Kaggle CLI using the below command: pip install kaggle. Before running this command, ensure that you have Python and the package manager pip installed.

After that, authenticate your machine by downloading an API token from the Kaggle site.
The Kaggle API and CLI tool provide an easy way to interact with competitions, datasets and notebooks.

Frequently Asked Questions

What is Kaggle?

kaggle is an online community for data scientists and machine learning enthusiasts.


Is Kaggle free?

Yes, everything on Kaggle is free. In Kaggle notebooks, you can activate a GPU  at any time, but you are allowed to use the GPU actively only for a maximum of 30 hours per week.


Who founded Kaggle?

Kaggle was founded in 2010 by Anthony Goldbloom and Jeremy Howard. Later it was acquired by Google in 2017. 


What are the different types of competitions held in Kaggle?

Kaggle provides different kinds of competitions. These can be classified into given categories, i.e. Getting Started, Playground, Featured, Research and Recruitment.


In this article, we have extensively discussed the Kaggle.

We started by giving an elementary introduction to our article. Then we discussed

  • What Kaggle is.
  • Importance of Kaggle.
  • Different competition in Kaggle.
  • Kaggle Datasets
  • Kaggle Notebooks
  • Kaggle public API

We hope that this blog has helped you enhance your knowledge regarding the Kaggle and if you would like to learn more, check out our articles on What is Machine LearningData Science VS Artificial Intelligence VS Machine Learning VS Deep Learning. Do upvote our blog to help other ninjas grow.

You can also consider our Competitive Programming Course to give your career an edge over others!

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