
Reflecting back on one year of Kaggle contests - dpmehta02
http://mlwave.com/reflecting-back-on-one-year-of-kaggle-contests/
======
prlin
How much time on average did you spend on each competition? Did that time
increase or decrease (or was not affected?) as you progressed in skill?

Also what was your background before starting to compete?

~~~
Triskelion
The tricky part is setting up your pipeline: from .csv files to submission.
This can take a day, or 30 minutes, depending on the contest. If feature
engineering is required then this adds a lot of time to this process.

Time to create the first submission dropped drastically after a few
competitions. I now have a small library of munging and ensembling scripts
that I can quickly adapt to suit the needs. On the other hand, time spend on
optimizing and staying inside top 10, increased too. For the KDD-cup I'ddo
weekend long sprints for a few measly improvements. All in all I'd say I spend
at least 8 hours per competition.

My background was front-end developer growing into analytics and dataviz more
and more. I think it was on HN that I saw a link to learnpythonthehardway.org
and I started from there. After reading "programming collective intelligence"
I got more serious.

~~~
shekhar101
And your post is certainly an inspiration to me :) Thanks for documenting your
journey. I'll make sure, one year down the line, I document something like
this and hopefully will get some success on kaggle.

------
crishoj
Have you made the transition from playing with ML in your spare time to
actually sustaining a living from Kaggle income?

~~~
Triskelion
No, not yet. I'd love for Kaggle to be a place where analysts can earn a
steady income, but I think that is something for the future. Right now even
the top performers do not consistently win prize money, so they'll need a job
on the side. If they are top competitors then this is usually a wellpaying job
and so they are competing for fame and bragging rights, not prize money.

At my startup I do predictive analytics on whatever data I can get my hands
on. There is an industry demand for fast and scalable solutions, which I fancy
working on. Also Kaggle indirectly gives a lot of commercial opportunities and
exposure. Perhaps in a year I'll reflect back on a modest start with ML as a
career.

