Kaggle is a community and site for hosting machine learning competitions. a popular platform for data science competitions can be intimidating for beginners to get into. But how do you get started? It can be overwhelming with so many competitions, data sets, and kernels (notebooks where people share their code). One kernel may contain over ten new concepts, so if you’re new to machine learning (or even if you’re not), you may feel a bit out of your depth at first.
Competitive machine learning can be a great way to develop and practice your skills, as well as demonstrate your capabilities.
It’s no surprise that most of the beginners hesitate to get started on Kaggle. When I started, I too struggled a bit.
Some reasonable concerns while starting:
- How do I even start?
- Will I be up against teams of experienced Ph.D. researchers?
- Is it worth competing? if I don’t have a realistic chance of winning?
- Will, I ever won any competition, I don’t think so I will.
- Is this what data science is all about? (If I don’t do well on Kaggle, do I have future in data science?)
These were the few things that haunt beginners when they starting.
Well, if you’ve ever had any of those questions, you’re in the right place.
In this guide, we’ll break down everything you need to know about getting started, improving your skills, and enjoying your time on Kaggle.
There are many ways to learn and practice applied machine learning. Kaggle has some specific benefits that you should seriously consider:
- The problems are well defined and all of the available data is provided directly.
- It is harder to fool yourself with a bad test setup given the harsh truth of the public and private leaderboards.
- There is often great discussion and sharing around each competition that you can learn from and to which you can contribute.
- You can build up a portfolio of projects on difficult real-world datasets that can demonstrate your skill.
- It is a complete meritocracy where the ability to deliver and skill is the defining factor, not where you went to school, the math you know, or how many degrees you have.
- Can build your portfolio, Where you can show you have some projects under your belt. Which really help you regarding the job prospects.
- The recruiter gave preference a to kagglers
Let’s get started
Types of Competitions
Kaggle Competitions are designed to provide challenges for competitors at all different stages of their machine learning careers. As a result, they are very diverse, with a range of broad types.
Common competition types
Featured competitions are the types of competitions that Kaggle is probably best known for. These are full-scale machine learning challenges which pose difficult, generally commercially-purposed prediction problems. For example, past featured competitions have included:
- Allstate Claim Prediction Challenge – Use customers’ shopping history to predict which insurance policy they purchase
- Jigsaw Toxic Comment Classification Challenge – Predict the existence and type of toxic comments on Wikipedia
- Zillow Prize – Build a machine learning algorithm that can challenge Zestimates, the Zillow real estate price estimation algorithm
Featured competitions attract some of the most formidable experts, and offer prize pools going as high as a million dollars. However, they remain accessible to anyone and everyone. Whether you’re an expert in the field or a complete novice, featured competitions are a valuable opportunity to learn skills and techniques from the very best in the field.
Research competitions are another common type of competition on Kaggle, research competitions feature problems which are more experimental than featured competition problems. For example, some past research competitions have included:
- Google Landmark Retrieval Challenge – Given an image, can you find all the same landmarks in a dataset?
- Right Whale Recognition – Identify endangered right whales in aerial photographs
- Large Scale Hierarchical Text Classification – Classify Wikipedia documents into one of ~300,000 categories
Research competitions do not usually offer prizes or points due to their experimental nature. But they offer an opportunity to work on problems which may not have a clean or easy solution and which are integral to a specific domain or area in a slightly less competitive environment.
Joining a Competition over kaggle
Kaggle runs a variety of different kinds of competitions, each featuring problems from different domains and having different difficulties. Before you start, navigate to the Competitions listing. It lists all of the currently active competitions.
The first element worth calling out is the Rules tab. This contains the rules that govern your participation in the sponsor’s competition. You must accept the competition’s rules before downloading the data or making any submissions. It’s extremely important to read the rules before you start. This is doubly true if you are a new user. Users who do not abide by the rules may have their submissions invalidated at the end of the competition or banned from the platform. So please make sure to read and understand the rules before choosing to participate.
If anything is unclear or you have a question about participating, the competition’s forums are the perfect place to ask.
The information provided in the Overview tabs will vary from Competition to Competition. Five elements which are almost always included and should be reviewed are the “Description,” “Data”, “Evaluation,” “Timeline,” & “Prizes” sections.
The description gives an introduction to the competition’s objective and the sponsor’s goal in hosting it.
The data tab is where you can download and learn more about the data used in the competition. You’ll use a training set to train models and a test set for which you’ll need to make your predictions. In most cases, the data or a subset of it is also accessible in Kernels.
The evaluation section describes how to format your submission file and how your submissions will be evaluated. Each competition employs a metric that serves as the objective measure for how competitors are ranked on the leaderboard.
The timeline has detailed information on the competition timeline. Most Kaggle Competitions include, at a minimum, two deadlines: a rules acceptance deadline (after which point no new teams can join or merge in the competition), and a submission deadline (after which no new submissions will be accepted). It is very, very important to keep these deadlines in mind.
The prizes section provides a breakdown of what prizes will be awarded to the winners if prizes are relevant. This may come in the form of monetary, swag, or other perks. In addition to prizes, competitions may also award ranking points towards the Kaggle progression system. This is shown at the bottom of the Overview page.
Once you have chosen a competition, read and accepted the rules, and made yourself aware of the competition deadlines, you are ready to submit!
Making a Submission
You will need to submit your model predictions in order to receive a score and a leaderboard position in a Competition. How you go about doing so depends on the format of the competition.
Either way, remember that your team is limited to a certain number of submissions per day. This number is five, on average, but varies from competition to competition.
One of the most important aspects of Kaggle Competitions is the Leaderboard: The Competition leaderboard has two parts.
The public leaderboard provides publicly visible submission scores based on a representative sample of the submitted data. This leaderboard is visible throughout the competition.
Competitions you begin first Getting Started:
- The Getting Started Competitions are specifically targeted at new users getting their feet wet with Kaggle and/or machine learning:
This is all about kaggle, Now let’s talk about what you need to get on kaggle.
Learn a programming language for data science: If you don’t have experience with Python or R, you should learn one of them or both.
Like to read more about which language to choose for AI:5 Best programming language for AI Development
There are numerous online courses/tutorials that can help you like.
Learn about machine learning. Andrew NG machine learning course is, no doubt, perfect place to start.
This will surely help you in getting started with AI: A Beginners Guide to Artificial Intelligence: The Learning Curve of AI
Now, you are ready to start doing actual stuff on Kaggle. I would recommend you read this Getting Started | Kaggle article about how to get Started, then you can go ahead and join a competition. Competitions on Kaggle are classified into different types according to their reward: Knowledge, Jobs, money. Knowledge competitions are meant for beginners who are looking to get started. These are a good fit for a beginner because you can find a lot of articles and sample solutions explaining how to get a good score.
Tips To remember while working over kaggle:
- Some beginners never start because they’re worried about low ranks showing up in their profile. Of course, competition anxiety is a real phenomenon, and it isn’t limited to Kaggle.
- However, low ranks are really not a big deal. No one else will judge you because they were all beginners at one point.
- And once you get comfortable and learn more about kaggle your rank will slowly start improving, But this all takes time, So don’t be so eager to focus on your learning and practicing.
In this guide, we shared 5 steps for getting started on Kaggle:
- Pick a programming language.
- Learn the basics of exploring data.
- Train your first machine learning model.
- Tackle the ‘Getting Started’ competitions.
- Compete to maximize learnings, not earnings.
Remember that Kaggle can be a stepping stone. Don’t worry about low ranks.