Problem Statement

HackerEarth wants to improve its customer experience by suggesting tags for any idea submitted by a participant for a given hackathon. Currently, tags can only be manually added by a participant. HackerEarth wants to automate this process with the help of machine learning. To help the machine learning community grow and enhance its skills by working on real-world problems, HackerEarth challenges all the machine learning developers to build a model that can predict or generate tags relevant to the idea/ article submitted by a participant.

You are provided with approximately 1 million technology-related articles mapped to relevant tags. You need to build a model that can generate relevant tags from the given set of articles.

Data Description

The dataset consists of ‘train.csv ’, ‘test.csv’ and ‘sample_submission.csv’. Description of the columns in the dataset is given below:

Download Dataset

Torrent File

Submission

The submission file submitted by the candidate for evaluation has to be in the given format. The submission file is in .csv format. Check sample_submission for details. Remember, incase of multiple tags for a given article, they are seperated by '|'. 

id,tags
HE-efbc27d,java|freemarker
HE-d1fd267,phpunit|pear|osx-mountain-lion
HE-ffd4152,javascript|jquery|ajax|onclick
HE-d3ab268,forms|select|dojo
HE-ed2fa45,php|mysql|login|locking|ip-address

For challenge related queries, discussions and announcements join our Slack channel.

Evaluation Metric

The predicted tags will be evaluated on the metrics $F1\ score$. For each article, $F1\ score$ is calculated as

$F1\ score = \frac{2*recall * precision} {recall + precision}$

where, 

$Precision(u) = \frac{|Recommended(u)\ \cap \ Testing(u)|}{|Recommended(u)|}$

$Recall(u) = \frac{|Recommended(u)\ \cap \ Testing(u)|}{|Testing(u)|}$

The final score is calculated as:

$Leaderboard\ score = \frac{1}{n} \cdot \sum_{i=1}^{n}{(F1\ score)_i}$

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Announcement 

Update: 16th October 2018: Corrections to the dataset have been made, please re-download the dataset.

Update: 21st October 2018 :  The leaderboard metrics has been updated to F1 score.

Update: 10th December 2018 : The final leaderboard has been updated. You can check your final standings.