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Open Data Science (Hosted by ODSC) Message Board › Wednesday’s Meetup, Hackathon Announcement & Job Offering.

Wednesday’s Meetup, Hackathon Announcement & Job Offering.

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Sheamus M.
user 14106978
Belmont, MA
Hi All,
Looking forward to seeing everyone on Wednesday at hack/reduce. Our Wednesday meetup is finance specific and we have a solid lineup. Dave Kane will present his talk titled "Your Career in Finance. Or, Five Weird Tricks for Getting a Job on Wall Street." For the stock prediction model presentation see the last portion of this email for data and descriptions.
A couple of quick additional announcements
Hackathon:
hack/Reduce is hosting MassDOT Visualizing Transportation Hackathon - Dec 13-14th, 2013. Per their announcement "explore data sets from the Massachusetts Department of Transportation (MassDOT), in partnership with Mass Tech Collaborative and Mass Big Data Initiative.  There are $6K in cash prizes to be given away for the most visually compelling and best use of the data, as well as a crowd voted favorite winner. Register here to attend.
Please contact Adrienne at hack/reduce for any follow up questions ( adrienne@hackreduce.org­ )

Job Listing:
Rifiniti, a local startup and Mass Challenge winner, is seeking a Senior Machine Learning Developer. http://www.rifiniti.com/careers/senior-machine-learning-developer/­

Stock Prediction Hackathon Data and Description:

https://inclass.kaggle.com/c/boston-data-festival-hackathon­ - link to the competition.

Data:

Data consists of two files:

training.csv - time series for 94 stocks (94 rows). First number in each row is the stock ID. Then data for 500 days. Data for each day contain - day opening price, day maximum price, day minimum price, day closing price, trading volume for the day. Price data normalised to the first day opening price.

test.csv - data to create prediction. Data provided for 25 time segments. Each segment contains data for the same 94 stocks. Each segment has opening, max, min, closing, volume data for 9 days and opening for day #10. Each line of the file starts with segment number following by stock ID and then price and volume data organized by day the same way as training set.  Price data normalised to the first day opening price.

Each line in train.csv and test.csv contains consecutive trading days. Days when market was closed were excluded. Thus day N may be Friday and day N+1 may be Monday or even Tuesday if Monday was a holiday.

Value to predict - probability of stock moving up from  opening of day 10 to closing of day 10. Prediction should be in 0-1 range, where 1 - "stock surely will go up", 0- "stock surely will go down".

Test set is randomly sampled without overlapping from year following training data time period.

File descriptions

train.csv - the training set
test.csv - the test set

Data fields

StID - Stock ID
O1, O2,... - Opening prices
MA1, MA2,... - Maximum daily prices
MI1, MI2... - Minimum daily prices
C1,C2,... - Closing daily prices
V1,V2,... - Daily trading volume
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