Here at Kaggle we’re excited to showcase the work of our Grandmasters. This post was written by Vladimir Iglovikov, and is filled with advice that he wishes someone had shared when he was active on Kaggle. The original post can be found on Vlad’s Ternaus Blog. Introduction I participated in machine learning (ML) competitions at Kaggle and other platforms to build machine learning muscles. I was 19th in the global rating, got Kaggle Grandmaster title. Every ML challenge ended with new knowledge, code, and model weights. I loved new learnings but ignored the value that old ML pipelines ..read more

Recruit Coupon Purchase Winner’s Interview: 2nd place, Halla Yang Recruit Ponpare is Japan’s leading joint coupon site, offering huge discounts on everything from hot yoga, to gourmet sushi, to a summer concert bonanza. The Recruit Coupon Purchase Prediction challenge asked the community to predict which coupons a customer would buy in a given period of time using past purchase and browsing behavior. Halla Yang finished 2nd ahead of 1,191 other data scientists. His experience working with time series data helped him use unsupervised methods effectively in conjunction with gradient boosti ..read more

With sports (and everything else) cancelled, this data scientist decided to take on COVID-19 | A Winner’s Interview with David Mezzetti When his hobbies went on hiatus, Kaggler David Mezzetti made fighting COVID-19 his mission. Photo by Clay Banks on UnsplashLet’s learn about David! https://www.kaggle.com/davidmezzetti David Mezzetti is the founder of NeuML, a data analytics and machine learning company that develops innovative products backed by machine learning. He previously co-founded and built Data Works into a 50+ person well-respected software services company. In Au ..read more

How one Kaggler took top marks across multiple Covid-related challenges. Photo by Markus Spiske on Unsplash Today we interview Daniel, whose notebooks earned him top marks in Kaggle’s CORD-19 challenges. Kaggle hosted multiple challenges that worked with the Kaggle CORD-19 dataset, and Daniel won 1st place three times, including by a huge margin in the TREC-COVID challenge. (He had a score of 0.9, 2nd place overall had a score of 0.75, and 2nd place on Kaggle had a score of 0.6.) Let’s meet Daniel! Daniel, tell us a bit about yourself. Daniel: I’m Daniel Wolffram, a graduate stu ..read more

Kaggler, deoxy takes first-place and sets the stage for his next competition. Please join us in congratulating Linsho Kaku (aka deoxy) on his solo first-place win in our Bengali.AI Handwritten Grapheme Classification challenge! Read the winning solution here: 1st Place Solution with Code Random Ink by Sankarshan Mukhopadhyay @FlickrLet’s meet Linsho! Linsho, what would you like to share about yourself? Linsho: I am a student in the Rio Yokota Laboratory at the Tokyo Institute of Technology. The main theme of the lab is high performance computing with advanced architectures including ..read more

I am Yuanchen He, a senior engineer in McAfee lab. I have been working on large data analysis and classification modeling for network security problems. Method Many thanks to Kaggle for setting up this competition. And congratulations to the winners! I enjoyed it and learned a lot from working on this challenging data and reading the winners’ posts. I am sorry I didn’t find free time last week to write this report. The data came with a lot of categorical features with a high number of values. At the very beginning, I removed useless features (by weka.filters.unsupervised.attribute.Remove ..read more

I’m a PhD student of the Machine Learning Group in the University of Waikato, Hamilton, New Zealand. I’m also a part-time software developer for 11ants analytics. My PhD research focuses on meta-learning and the full model selection problem. In 2009 and 2010, I participated the UCSD/FICO data mining contests. What I tried and What ended up working I tried many different algorithms (mainly weka and matlab implementations) and feature sets in nearly 80 submissions. This report will briefly introduce two approaches that worked for this competition. Each of them will be discussed sequentiall ..read more

Because I have recently started employment with Kaggle, I am not eligible to win any prizes. Which means the prize-winner for this comp is Quan Sun (team ‘student1’)! Congratulations! My approach to this competition was to first analyze the data in Excel pivottables. I looked for groups which had high or low application success rates. In this way, I found a large number of strong predictors — including by date (new years day is a strong predictor, as are applications processed on a Sunday), and for many fields a null value was highly predictive. I then used C# to normalize the data into Grant ..read more

I participated in the R package recommendation engine competition on Kaggle for two reasons. First, I use R a lot. I cannot learn statistics without R. This competition is my chance to give back to the community a R package recommendation engine. Second, during my day job as an engineer behind a machine learning service in the cloud, product recommendation is one of the most popular applications our early adopters want to use the web service for. This competition is my opportunity to stand in users’ shoes and identify their pain points associated with building a recommendation system. I treat ..read more

I graduated on Warsaw University of Technology with master thesis about text mining topic (intelligent web crawling methods). I work for Polish IT consulting company (Sollers Consulting), where I develop and design various insurance industry related stuff, (one of them is insurance fraud detection platform). From time to time I try to compete in data mining contests (Netflix, competitions on Kaggle and tunedit.org) — from my perspective it is a very good way to get real data mining experience. What I tried As far as I remember, the basis of the solution I defined at the very beginning: t ..read more