Home

Awesome

Hello Kaggle!:wave:

I summarized the definitions of Kaggle and basic usage after reading Kaggle's Official Document and Kaggle Guide <br>

I hope it will help those who are just introduced to Kaggle like me. <br>

If there is anything that needs to be corrected, please leave it in Issue. <br>

FYI, the Hello Kaggle' document rarely deals with Python programming or machine learning theory<br> and focuses on Kaggle usage. <br>

For those of you who are looking for programming, data science, and machine learning materials, I'll leave you with some links that I've been helped with.<br>

Table of contents

  1. What is Kaggle?

  2. How is Kaggle used?

  3. Kaggle Competition?

  4. Getting Started with Kaggle

  5. Getting to know Notebook

  6. Competitions and Notebooks

  7. Competitions Progress Flow

  8. Rule of Competitions

  9. Flow of Technology in Kaggle

  10. Kaggle Dataset and API

  11. Finished!

    <br>

<br>

What is Kaggle?

Kaggler? Kaggling?

Kaggle Service and Features

English

Required Kaggling Knowledge

<br>

Prepare before becoming Kaggler


<br>

How is Kaggle used?

Infrastructure for data analytics

Notebook

Dataset

image

Company Training

Discussion


<br>

Kaggle Competition?

Refer to Competitions Documentation. <br>

Featured, the most common Competition

image

Research

image

Getting Started for New Kaggler

image

Playground for data scientists and engineers

image

Recruitment for job opportunities

image

Annual Competition held regularly

Analytics to effectively explain the results


<br>

Getting Started with Kaggle

Sign Up

Take a look at Kaggle Courses

Refer to Kaggle Progression System.<br> Before I explain how to become a Contributor, I will explain about Kaggle Tiers and Medal.

Kaggle Tiers

Medal

Being Contributor

1. Adding User Profile Information

2. SMS Verification

3. Run Script

4. Participate in the Competition

5. Comment to other people's posts or comments and cast upvote (Make 1 comment & Cast 1 upload)

6. Now you are a Contributor!

<br>

Wait!


<br>

Getting to know Notebook

Please re-read here for a brief introduction to your Notebook!

<br>

What can you do with your Notebook?

Create and Use Notebook

Various settings for Notebook

How to import Data from Notebook

Use external packages in Notebook

<br>

Use Source Code from Dataset in Notebook

<br>
<br>

Competitions and Notebooks

What else can the Notebook be used for besides data analysis Competition?

How to handle Data File to use in Competition Notebook?


<br>

Competitions Progress Flow

Baseline implementing the general-purpose algorithm

Data Analysis Notebook

Fork Notebook

Merge, Blending, Stacking, Ensemble Notebook

Conclusion of Competitions Progress Flow

Untitled Diagram


<br>

Rule of Competitions

What rules should I check?


<br>

Flow of Technology in Kaggle

Exploring in Closed Competition

<br>

Winner Solutions at a Glance


<br>

Kaggle Dataset and API

Use public Dataset

Use it as a Data Repository

Kaggle API

Install Kaggle API

Use Kaggle API

Finished!

First of all, thank you for reading Hello Kaggle!<br> I studied Python for the first time in April 2020 and was unable to concentrate fully on my studies as I've started military service in July of the same year.<br> That's why I couldn't study data science in depth, and I still need more knowledge to understand it.<br> Now finally I'm stepping into machine learning and Kaggle.<br> At this moment to write Hello Kaggle!, I've improved my understanding of Kaggle and I'm going to start with Getting Started Competition.<br>Also eager to keep up with the latest technology by looking at other outstanding Kaggler's Notebook.<br>Hopefully, everyone who reads Hello Kaggle! will get the best time in 2021. Let's Keep Going! <br>