MachineHack is back again with another exciting hackathon for this weekend, and this time with a new look and exciting features. 

Problem Statement & Description

The gaming industry is certainly one of the thriving industries of the modern age and one of those that are most influenced by the advancement in technology. With the availability of technologies like AR/VR in consumer products like gaming consoles and even smartphones, the gaming sector shows great potential. In this hackathon, you as a data scientist must use your analytical skills to predict the sales of video games depending on given factors.

Given are 8 distinguishing factors that can influence the sales of a video game. Your objective as a data scientist is to build a machine learning model that can accurately predict the sales in millions of units for a given game.

Data Description:-

The unzipped folder will have the following files.

• Train.csv –  3506 observations.
• Test.csv –  1503 observations.
• Sample Submission – Sample format for the submission.

Target Variable: SalesInMillions

The datasets will be made available for download on June 26th, Friday at 6 pm IST

This hackathon and the bounty will expire on June 29th, Monday at 7 am IST

Below are the file formats for the provided data: –

Train.csv

Test.csv

Sample_Submission.xlsx

Bounties

The top 3 competitors will receive a free pass to the Rising 2020.

Know more about the Rising 2020 here.

Rules

1. One account per participant. Submissions from multiple accounts will lead to disqualification
2. The submission limit for the hackathon is 10 per day after which the submission will not be evaluated
3. All registered participants are eligible to compete in the hackathon
4. This competition counts towards your overall ranking points
5. We ask that you respect the spirit of the competition and do not cheat
6. This hackathon will expire on 29th June, Monday at 7 am IST
7. Use of any external dataset is prohibited and doing so will lead to disqualification

Evaluation

The leaderboard is evaluated using RMSE for the participant’s submission.