RAPIDMINER DATA SCIENCE COMPETITION: FARMING ON "MARS" – SEPTEMBER 12 TO OCTOBER 13, 2017
Hello all community members -
Welcome to the 2nd RapidMiner Data Science Competition: Farming on "Mars"!
Our sponsor and we are super excited to bring this open competition to our 270,000+ users and we hope that you have a great time exploring this unique use case. Below is a brief summary and rules of the competition; complete documentation can be found in the attachments below. PLEASE READ all the attached documentation before beginning the competition and let the best model win!
One of the major challenges of the human colonization of “Mars” is the introduction of Earth-independent food production facilities, i.e. farming. A key element to farming on “Mars” will be the fertilization of available soil, which in its current state is not farmable due to a lack of nutrients. In order to address this, an experimental setup has been created under “Martian” environmental conditions to produce bio-fertilizer made from algae and measure the usable yield after each production run. This yield varies based on the exact quantities of certain base nutrients and the optional addition of one of two possible additional nutrients, α or β, inserted into the bio-fertilizer at some time t during the production run. The research facility has already done 1653 production runs, each one lasting 36 hours with 41 sensors recording data every hour, and recorded the potential yield of each one. These are your data to work with during this challenge.
The goal of the challenge is to build a model that will classify which additional nutrient, α or β, and at what time t, will be most likely to boost yield during a production run. The metric to be optimized is the cumulative score value of the same 178 production runs in the test set; the baseline example above has a cumulative score value of 1000.
Submission and Evaluation
All submissions in this competition need to be posted in this thread with the entire XML of the process and the score. This includes the finished models, as well as the entire training process and all pre-processing steps. The deadline for submissions is October 13, 2017 at 23:59:59 UTC.
RapidMiner Server Instance
In order to increase the efficiency of model training and to demonstrate RapidMiner’s powerful parallel processing capabilities with its new SaaS on Amazon AWS EC2 , RapidMiner has agreed to provide a free Server EC2 instance for all participants for the duration of this competition. This server instance can be used by any participant free of charge, as often as desired, for the duration of the competition as long as all use is restricted to this competition only. Participants wishing to use this server must @sgenzer a private message to register and obtain the relevant connection details. The instance URL is https://competitions.rapidminer.com and will be online only for the duration of the competition.
Winner and Prizes
The winner of the competition will be selected based on the highest aggregate score value of the 178 testing production runs ≥ 1000, after applying the test dataset to the submitted models. All submissions will be validated by RapidMiner and the competition’s sponsor within 72 hours after their submission. The winners of this RapidMiner Data Science Challenge will be announced by October 17, 2017 in the competition’s thread.
RapidMiner and the competition sponsor will award the following prices to the winners:
1st place: US$1000
2nd place: US$250
3rd place: US$100
PLUS all participants who submit a valid entry in the thread prior to the deadline will be eligible to win one or more amazing RapidMiner “swag” items. Supplies are limited and will be awarded on a first come-first served basis.
All participants of the RapidMiner Data Science Competitions must be registered users in good standing of the RapidMiner User Community and age 18 or older at the time of entry. Employees, directors, consultants, and any other persons affiliated with RapidMiner, Inc. are not eligible to participate in this competition.
Good luck everyone and reply to this thread with questions and your models!
Links: Training Data Set