MachineHack is back again with another exciting hackathon for this weekend. The 9th of the popular weekend hackathon series, and this time we challenge data scientists to predict the outcome of a cricket match in MachineHack’s ODI Match Winner: Weekend Hackathon #9.
The challenge will start on June 19th Friday at 6 pm IST.
Problem Statement & Description
Our country shares a great deal of history with the game of cricket. Introduced as a royal game by the British during the British Raj, India took on the game as a popular sport even after its Independence. Today cricket is more than just a sport in India. In this hackathon, we challenge data science enthusiasts to predict the winning team of an ODI (One Day International) match.
Given are 9 distinguishing factors that can influence the outcome of a cricket match. Your objective as a data scientist is to build a machine learning model that can accurately predict the winning team of an ODI match.
The unzipped folder will have the following files.
- Train.csv – 2293 observations.
- Test.csv – 983 observations.
- Sample Submission – Sample format for the submission.
Target Variable: MatchWinner
The datasets will be made available for download on June 19th, Friday at 6 pm IST
This hackathon and the bounty will expire on June 22nd, Monday at 7 am IST
Below are the file formats for the provided data
The top 3 competitors will receive a free pass to the Rising 2020.
Click here to participate
- One account per participant. Submissions from multiple accounts will lead to disqualification
- The submission limit for the hackathon is 10 per day after which the submission will not be evaluated
- All registered participants are eligible to compete in the hackathon
- This competition counts towards your overall ranking points
- You will not be able to submit once you click the “Complete Hackathon” button. You may ignore this feature
- We ask that you respect the spirit of the competition and do not cheat
- This hackathon will expire on 22nd June, Monday at 7 am IST
- Use of any external dataset is prohibited and doing so will lead to disqualification
The leaderboard is evaluated using multi-class log loss for the participant’s submission.