GSTN Announces Analytics Hackathon to Create AI/ML Predictive Models for GST with Prizes up to INR 50 Lakh

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The Goods and Services Tax Network (GSTN) has launched an Analytics Hackathon focused on developing a predictive model for GST, open to students and researchers. 

Participants have the chance to create AI and ML solutions using GST data, competing for a substantial prize pool exceeding INR 50 lakhs, with INR 25 lakhs awarded to the top entry. 

Click here to register

The online challenge runs from 12:00 PM on 12th August 2024 until 12:00 PM on 26th September 2024.

Participants will work with a dataset of roughly 900,000 anonymised records, each featuring around 21 attributes and target variables. This well-labeled dataset includes training, testing, and a non-validated subset reserved for final evaluations. The objective is to develop and implement innovative AI and ML algorithms to tackle the challenges set forth in the hackathon.

The GSTN Analytics Hackathon will be conducted online, encompassing participant registration, dataset access, and prototype submission. An offline event will be held for shortlisted participants in the final round. 

Teams can consist of up to five members, with at least one team lead. The hackathon will last 45 days, from the start of registration to the final submission date.

Participants must upload their code to a GitHub repository and may optionally provide a demo or product video on YouTube before submitting their solution prototypes. Required fields for online submissions include the project idea, description, source code URL, video URL, GitHub unique source code checksum, and a project report.

Prizes will be awarded to the top three teams whose models are deemed viable by the jury, with a special prize for an all-women team if eligible. Consolation prizes will be awarded at the jury’s discretion if no solution meets the required standards. 



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GSTN Announces Analytics Hackathon to Create AI/ML Predictive Models for GST with Prizes up to INR 50 Lakh


The Goods and Services Tax Network (GSTN) has launched an Analytics Hackathon focused on developing a predictive model for GST, open to students and researchers. 

Participants have the chance to create AI and ML solutions using GST data, competing for a substantial prize pool exceeding INR 50 lakhs, with INR 25 lakhs awarded to the top entry. 

Click here to register

The online challenge runs from 12:00 PM on 12th August 2024 until 12:00 PM on 26th September 2024.

Participants will work with a dataset of roughly 900,000 anonymised records, each featuring around 21 attributes and target variables. This well-labeled dataset includes training, testing, and a non-validated subset reserved for final evaluations. The objective is to develop and implement innovative AI and ML algorithms to tackle the challenges set forth in the hackathon.

The GSTN Analytics Hackathon will be conducted online, encompassing participant registration, dataset access, and prototype submission. An offline event will be held for shortlisted participants in the final round. 

Teams can consist of up to five members, with at least one team lead. The hackathon will last 45 days, from the start of registration to the final submission date.

Participants must upload their code to a GitHub repository and may optionally provide a demo or product video on YouTube before submitting their solution prototypes. Required fields for online submissions include the project idea, description, source code URL, video URL, GitHub unique source code checksum, and a project report.

Prizes will be awarded to the top three teams whose models are deemed viable by the jury, with a special prize for an all-women team if eligible. Consolation prizes will be awarded at the jury’s discretion if no solution meets the required standards. 



Source link

Disclaimer

We strive to uphold the highest ethical standards in all of our reporting and coverage. We StartupNews.fyi want to be transparent with our readers about any potential conflicts of interest that may arise in our work. It’s possible that some of the investors we feature may have connections to other businesses, including competitors or companies we write about. However, we want to assure our readers that this will not have any impact on the integrity or impartiality of our reporting. We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Thank you for your trust and support.

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