The Most Comprehensive List of Kaggle Solutions and Ideas

Learn from top-performing teams in the competition to get better at understanding machine learning techniques.

The Most Comprehensive List of Kaggle Solutions and Ideas
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The community known as Kaggle is the go-to platform for machine learning data science buffs who want to collaborate with like-minded users. There's a lot to do on the platform: aside from collaboration, users can search for and post their datasets, make use of GPU-integrated laptops and, perhaps most excitingly, enter into competitions between other users to tackle data science and ML challenges. 

These Kaggle competitions are a big reason why the platform is so popular today; the competitions encourage companies to disseminate an assortment of difficult-to-solve data science-based tasks whose prizes can be won by beginners and experts alike. In fact, pretty much anyone can take part in a Kaggle competition -- from students who want to dive deeper into AI to world-renowned data scientists who are experts in their field, Kaggle facilitates a unique blend of collaboration and places its users on a live leaderboard. 

Thanks to one gentleman's efforts, users now also have a website that catalogs and organizes solutions to Kaggle competitions. Let's quickly dive into Kaggle a bit further, explore an overview of this website, check out a few of the solutions it's gathered, and learn more about how you can get better at your own practice by learning how other data scientists have solved Kaggle problems. 


A Quick Intro to Kaggle


You can think of Kaggle as the AirBnB for AI and data science enthusiasts. The platform is crowd-sourced and designed to attract data scientists who want to hone their problem-solving skills by taking on ML, data science, and predictive analytics challenges. Its active members hail from more than 190 countries, and Kaggle gets nearly 150,000 monthly submissions from its users. 

As we mentioned, Kaggle's big draw is its competitive scene. The platform posts challenges that can relate to anything from analyzing patient data to predicting cancer to performing sentiment analysis. The one thing all these challenges have in common is that competitors must apply data science to obtain solutions.

Challenges posted on Kaggle are provided from different sources, and while some exist simply to give members a chance to nurture their skills, others are posted by businesses that have legit problems for which they need immediate solutions. Kaggle encourages as many users as possible to compete by offering prize money to competition winners or those who solve a challenge and claim a top X position. Prizes may also come in the form of a company-provided product or job, as well.


What are Kaggle Solutions?


The Kaggle Solutions site is dedicated to gathering a list of nearly every available solution that top-performing competitors have devised during previous Kaggle competitions. The site is updated each time a new competition closes with a title and description of the competition, the important details (the prize type, the number of team members who took part in creating the solution, the type of competition and its metrics, as well as the year that the competition occurred). 

The site is the handiwork of a single data scientist and researcher named Farid Rashidi. Rashidi currently serves as a National Cancer Institute fellow, where he conducts data science-driven research into vaccines for cancer. Rashidi previously attended Indiana university, where he received his doctorate in computer science and created computational tools that used single-cell sequencing data to work out tumor heterogeneity and evolution.


Sample Kaggle solutions and ideas


Now that you've got a quick overview of Kaggle Solutions, let's check out a few recent solutions that have been published on the site.


American Express - Default Prediction


This competition was published by American Express, which solicited default prediction solutions that would predict whether customers would default on their payments at some point in the future. The competition had an impressive prize of $100,000 and asked that competitors use ML to accurately predict customer credit defaults. American Express specifically asked that competitors use an industrial-scale set of data to create an ML model that challenged an existing production model. 

The first-place solution was published by a user named "daishu,” a first-time solo Kaggle competition winner. Daishu used a heavy ensemble with NN and LGB and released his solution's code on Github.


Ubiquant Market Prediction


Another recent competition with a prize money award of $100,000 was posted by Ubiquant Investment Co., Ltd, a Chinese quantitative hedge fund. Ubiquant asked that competitors create a model to forecast the return rate of financial investment and train and test their algorithms against previous real-world prices. 

Winning solutions would ideally improve the way in which quantitative researchers forecast investment returns in the future. The first-place solution is posted on Kaggle and provides a summary and detailed description. 


Tabular Playground Series - Aug 2022


The most recent competition is part of Kaggle's 'Playground Series' and Kaggle members to improve a fictional company's main product. 'Playground Series' competitions encourage members of all skill levels to model a tabular dataset. Kaggle notes that these competitions are mainly suited for members who want an intermediate-level challenge. The top-placing solution was posted on Kaggle by a user named 'Sawaimilert.’


Benefits of Tackling a Kaggle Competition


It can initially be pretty daunting to take part in a Kaggle competition; you may feel like your chances of winning are pretty slim, and it may not be obvious whether you'll actually learn anything valuable that can nurture your skills. Plus, the top-placing performers typically leverage ensemble methods that are pretty complicated in response to datasets that are artificial and, ostensibly, unrealistic. 

With that said, even if you have some misgivings when it comes to competing, participating in at least a few Kaggle competitions can be worthwhile -- there's something to be said about holding an opinion on something data-science related that you've tried versus having an opinion about something you've never taken part in.

You may, for example, discover a competition that focuses on a new and evolving topic that you're interested in and hasn’t taken part in in a data science-related context. New and evolving topics like blockchain, for instance, may be the focal point of future competitions. This is particularly likely considering the fact that Kaggle as a platform is rapidly evolving thanks to its acquisition by Google. It may be worth your time to periodically check in on new competitions that pique your interest.




We encourage you to stay up to date on the ever-evolving list of Kaggle solutions and ideas that have been developed by top-performing Kaggle competitors on Kaggle solutions. If you'd like to get your feet wet in data science's competitive space, you can also consider participating in other non-Kaggle-related competitions before diving into Kaggle itself.
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.