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This repository contains the code and my submission for the ZS Young data Scientist Challenge 2018 which got me a final leaderboard Rank 30 among 7500 participants

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prakharpartha/zs_data_science_challenge

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Problem Description :->

To forecast the sales for different products in different regions. We were provided with the sales 
data of different products from different merchants in a given reason, country wise. Along with this, 
organizer also provided us the holiday and promotional expense data, where holiday data consists of
holiday dates in a given country and promotional data consists of expense that was made to promote
a given product in a given reason at a given time (year month).

Data : train, test, holiday data, promotinal data

Evaluation metric : SMAPE (Symmetric Mean absolute Percent error) SMAPE self-limits to an error rate
of 200%, reducing the influence of these low volume items. Low volume items are problematic because 
they could otherwise have infinitely high error rates that skew the overall error rate. 
SMAPE = (2/N) sum((|f-a|)/(f+a))

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This repository contains the code and my submission for the ZS Young data Scientist Challenge 2018 which got me a final leaderboard Rank 30 among 7500 participants

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