When, Why & How — to get started with Kaggle?
Tags: Kaggle, Getting Started, Data Science, Machine Learning

Are you the one who aspires to be a data scientist or the one who wants to enhance his Machine Learning Skills.

But you are confused with…

Question 1: When to start with kaggle?

“Do I have the necessary skills to take part in Kaggle Competitions?”

As a beginner, you always live in a situation where you have a feeling of not being skilled enough and just because of that you procrastinate your tasks but that’s not the case of Kaggle. By the time you learn your First Machine Learning Algorithm, you can start with Kaggle competitions.

Over Kaggle you came across multiple kernels which creates a roadmap for your Machine Learning Journey which further turns you in an excerpt from a novice.

So Get started as soon as you can.

Question 2: Why start with Kaggle?

Kaggle brings data science industrial knowledge at your fingertip here you get a chance to

Learn and Compete with the best in the world.

Work with industry datasets.

By winning the competition, you can make money too.

Consistent practice is the only way to improve and enhance your data science skills — the best way to become better at data sciences is by doing it as often as you can.

Question 3: How to get started with Kaggle?.

Before we get started here are some basics to keep in mind: every single Kaggle competition is self-contained — this means it is not necessary for you to scope other projects for data. This is incredibly freeing as you can focus all your energy on other necessary tasks.

“ So Before we delve deeper into Kaggle things make sure you do follow all the steps as it is. This blog consists of two parts Part1(this one) We discuss all theoretical aspects and in Part2 is more code-oriented. ”

Step by step guide on how to get started on Kaggle

This step by step action plan will help you know how to navigate this platform even if you are a beginner with no prior knowledge:

Step 1: Choose a Programming Language:

Programming Language

It is advisable to choose a single programming language and stick with it. Two of the most popular programming languages on the Kaggle data community are R and Python. For the beginners who are completely starting with a blank slate, it is advisable to go with python. This is because Python is essentially a general-purpose programming language that is easy to use from end-to-end. Although both programming languages come in handy for Kaggle competitions, each is specifically suited for certain problems. While R is the right choice for data analysis, Python is suitable when you are dealing with statistics code or data integrated with web apps.

Step 2: Focus on Learning:

Now you have a good foundation to build from, so you have the confidence to start working on the featured competitions. The key to success, if you are a beginner, is patience and learning from your mistakes. It will take a lot of effort and time to get a good ranking. To avoid getting frustrated and discourage choose your battles wisely. While prize money is great but it is not the main focus, the most valuable benefit is learning skills that prepare you for the real-world. A research project is a great choice for a long-term project where you can exercise your data science skills and stimulate your creativity.

All the above-explained steps get you started with the kaggle but before you start participating you need to build your Kaggle profile.

Step 3: How to create a Kaggle Profile?

Kaggle Profile

Here is a sequential guide on how to register an account and create a profile on Kaggle. Please remember that Kaggle only allows one profile per user.

(1) Go to the Kaggle website and click on the Register section.

(2) Setup your Kaggle profile by providing a brief bio, picture, location, title, and current workplace. You can also add your LinkedIn, Github and Facebook account so people can contact you when needed.

Joining Kaggle is a straightforward process that requires very little time and effort. As a Kaggler, you have access to unlimited datasets, numerous Kaggle competitions, and the Kaggle forum.

You can also follow strong Kaggle users to learn from.

Once you are done with Register and sign-in time to go ahead with the competition section, Once you click there you came across a question.

Question: Which Kaggle Competition Category to choose to get started:

Kaggle Competition Category

Common Types of Kaggle Competitions

Featured competitions: are the types of competitions that Kaggle is probably best known for. They are usually sponsored by companies, organizations, or even governments. They offer prize pools going as high as a million dollars.

are the types of competitions that Kaggle is probably best known for. They are usually sponsored by companies, organizations, or even governments. They offer prize pools going as high as a million dollars. Research competitions: feature problems that are more experimental than featured competition problems. They do not usually offer prizes or points due to their experimental nature.

feature problems that are more experimental than featured competition problems. They do not usually offer prizes or points due to their experimental nature. Getting Started competitions: are structured like featured competitions, but they have no prize pools. They feature easier datasets, plenty of tutorials, and have no deadline — just what a newcomer needs to get started! 😃. One example of Getting Started competitions is:

are structured like featured competitions, but they have no prize pools. They feature easier datasets, plenty of tutorials, and have no deadline — just what a newcomer needs to get started! 😃. One example of Getting Started competitions is: Recruitment competitions: are the competition that is posted by different companies for there hiring process. Winners or top competitors get for direct interview.

are the competition that is posted by different companies for there hiring process. Winners or top competitors get for direct interview. Playground competitions are a for fun type of Kaggle competition that is one step above Getting Started in difficulty. Prizes range from kudos to small cash prizes. Eg: Dog Bread recognization using image data.

And a few more.

To every beginner, it is suggested to start with Getting Started Competitions Because “Getting Started” are the most basic competitions they demand a very basic skill set to get started and a lot much work is done over these competitions. In case you got stuck then somewhere 1000s of kernels are onboard to help you out.

They are always available and have no deadline 😃.

Step 4: Getting Started Competition to start with:

From the Getting Started competitions choose “Titanic: Machine Learning from Disaster” — Predict survival on the Titanic.

Titanic: Passenger Survival Prediction

Now time to make your first submission But before that how to make your first Kaggle Notebook(Kernel).

Question: What is this Kaggle kernel?

Note: Here Kernel is not SVM Kernel Function, so don’t confuse with it

Kaggle Kernels are essentially Jupyter notebooks in the browser. These kernels are entirely free to run. Are available for multiple languages like Python, R, etc. All your work saves over the web which means no hassle for environment setup.