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Tips to begin with Kaggle

Tips to begin with Kaggle

Kaggle is a prominent community website for data scientists to participate in machine learning challenges. Competitive machine learning can be a great way to sharpen your skills, just as exhibit your skills. In this article, I will provide you a step-by-step action plan for gently competing on Kaggle and get perfect at machine learning with Kaggle. Let’s dive right in!


Step 1: Choose a programming language.


To begin with, we suggest picking one programming language and sticking with it. Both Python and R are well known on Kaggle and in the more extensive data science community.

In case you’re beginning with a blank slate, we recommend Python since it’s a general-purpose programming language that you can use from end-to-end.


Step 2: Get familiar with the basics of exploring data.


The capacity to load, navigate, and plot your data (i.e. exploratory analysis) is the initial phase in data science because it informs the different decisions, you’ll make through model training.

On the off chance if you go through route of Python, then we suggest the Seaborn library, which was designed explicitly for this purpose. It has significant high-level functions for plotting many of the most well-known and useful charts.


Step 3: Train your first machine learning model.


Prior to jumping into Kaggle, we recommend training a model on a simpler, more reasonable dataset. This will permit you to become comfortable with machine learning libraries and the lay of the land.

The key is to begin developing good habits, such as splitting your dataset into separate training and testing sets, cross-validating to avoid overfitting, and utilizing proper performance metrics.

For Python, the best broadly useful machine learning library is Scikit-Learn.


Step 4: Make use of the forum.


The Kaggle client forums represent an outstanding learning asset. Just browsing through the conversations can prompt experiences. Don’t hesitate to pose questions, and you’ll be amazed at all the well-crafted answers you’ll get. Ensure you utilize competition threads in order to understand winning solutions.


Step 5: Build your own Kaggle toolbox.


Build an exceptional Kaggle toolbox with a variety of tools comprising of normally used code sequences. With practice, you’ll become proficient when using these tools. Also, try your hand at building a data pipeline that loads data, changes it, and reliably assesses a model. Design the pipeline that is reusable so you can deploy it on future competitions. A learner will make the mistake of reinventing the similar processes over and over again. Or you should work to streamline your Kaggle challenge process with techniques of reuse.


Step 6: Practice on past Kaggle challenges. 


Since that you possess a good level of knowledge with your tools and how to use them, it’s time to practice on past Kaggle challenges. You can likewise post candidate solutions so they’ll be assessed on the public and private leaderboard. It behooves you to work through a various Kaggle challenges from the most recent couple of years. This tip is intended to assist you learn how top performers approach serious competitive machine learning and to learn how to coordinate their methods into your own methodologies. Attempt to get into the head of past competition winners and utilize their strategies and tools. It’s a smart idea to pick a wide variety of different problem types that encourage you to acquire new methods. Try to accomplish a score in the top 10% or better in the public or private leaderboards.


Step 7: Compete on Kaggle


Now you are ready to compete on Kaggle.

Get after it.

  • Consider dealing with one problem at a time until you top-out or get stuck.
  • Aim for achieving a top 25% or top 10% outcome on the private leaderboard for each competition you challenge.
  • Share generously on the forum; this will prompt collaborations.
  • Minimize the time between finding out about or thinking about a smart idea and implementing it (e.g. minutes).


Have fun.

They might be competitions, yet you’re participating to learn and share.Be inventive, think outside of the box, and have fun!


Tips for Enjoying Kaggle


Here are 6 favorite tips for making the most out of your time on Kaggle.


Tip #1: Set incremental goals.


Most Kaggle members will never win a solitary competition, and that’s totally fine. In the event that you set as your very first milestone, you may feel discouraged and lose inspiration after a few tries.

Steady targets make the journey more charming.


Tip #2: Review most voted kernels.


Kaggle has a cool element in which participants can submit “kernels,” which are short scripts that investigate a concept, showcase a technique, or even offer a solution.

Reviewing well known kernels can spark more thoughts.


Tip #3: Ask questions on the forums.


Don’t be hesitant to ask “stupid” questions.

All things considered, what’s the worst thing that could happen? Possibly you get ignored… and that’s all.

On the other hand, you have plenty to pick up, including guidance and coaching from more experienced data scientists.


Tip #4: Work solo to create core skills.


In the beginning, we suggest working alone. This will constrain you to tackle every step of the applied machine learning process, including exploratory analysis, data cleaning, feature engineering, and model training.


Tip #5: Team up to push your boundaries.


Many past winners have been groups who joined forces to combine their insight.

In addition, once after you master the technical skills of machine learning, you can team up with others who may have more domain knowledge than you do, further expanding your opportunities.


Tip #6: Remember that Kaggle can be a venturing stone.


Remember, you’re not really committing to be a long-term Kaggler. On the off chance that you dislike the format, then it’s no big deal.

Indeed, many individuals use Kaggle as a stepping stone before moving onto their own undertakings or becoming full-time data scientists.

This is another reason to focus on learning as much as possible. For the long run, it’s smarter to target competitions that will give you important experience than to chase the biggest prize pools.




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