How to get started with kaggle?

Have you ever thought about participating in kaggle competitions? But hesitated as you might have feared the level of difficulty.

Well we all face similar questions and wonder if we have enough skillset to join competitions that kaggle offers. It is normal to be overwhelmed by the level of difficulty that these competitions offer, But believe me it’s not as scary as it sounds. It is all about being confident and approaching each competition as a learning experience and improvising your skills.

Kaggle is a platform for predictive modelling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users.

Kaggle is the largest and most diverse data community in the world. There are many competitions in Kaggle which provide real datasets and prizes up to million dollars. It is a great platform to learn machine learning.

Do i have sufficient skills to Enter a kaggle competition?

Although there’s no particular list of skills as such, but it is always good to have knowledge of the following topics

Programming Language: python or R (beginner’s guide to R)
Machine Learning Fundamentals

“You don’t have to be good to start … you just have to start to be good!”
― Joe Sabah

How do I start?

create an account heresign-up.png

In competition section you’ll see plenty of competitions along with their deadlines and prize money. Due to the complexity and diversity of competitions, it makes a challenging task to pick which competition to participate in for a beginner.

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For a beginner, this is perhaps the most challenging and overwhelming part since there are a plethora of options to choose from. Start with the “Getting started” section where you will find easier challenges. I highly recommend starting with “Titanic: Machine learning from disaster” problem (here you can find step by step guide to Titanic competition).

getting start titanic.JPG

What is a kernel in Kaggle?

Well Kaggle kernels can be considered as a script/notebook with environment/data already setup. It supports scripts in R and Python, Jupyter Notebooks, and RMarkdown reports. Briefly they are code snippets, sometime complete scripts that you can run on your machines to reproduce the results achieved by the uploader. They help you understand how things are happening, it’s actually a wonderful community building initiative and you can learn a lot from them.

Kaggle is a great platform to learn Machine Learning from practice. Participating in competitions will help you to learn a lot about algorithms and the various approaches to data. Here you can find the complete guide on how to be in the top 0.3% on the leaderboard for Titanic contest. I hope you’ll be more confident about participating in Kaggle’s contests after reading this article.

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