MachineHack is back again this weekend with yet another exciting hackathon for the data scientists’ community. This time the hackathon is dedicated to the passion and fervour which a sport creates. The challenge is to predict the outcome of a game based on some important predictor variables.
This challenge is a part of MachineHack Weekend Hackathon Edition #2 — The Last Hacker Standing, where we pose unique problem statements every week, from 30 July to 9 Sept 2021.
PARTICIPATE & STAND A CHANCE TO WIN FREE PASSES TO THE DLDC 2021!!!
Problem Statement and Description
Soccer, aka football, is currently the most popular game in the world. As Maradona once said, “football isn’t a game, nor a sport; it’s a religion.” If a group of people can stop time and make people watch them in awe and reverence, we all can understand its impact on everybody’s lives. Also, it has its own simplicity, where anybody can play soccer — all it requires are four poles, a ground and a ball.
Nelson Mandela very effectively used football as the unifying factor when he was elected as the President of South Africa post the apartheid era. This is because the sport has the ability to cut across all discriminating factors.
As a matter of fact, an entire ecosystem revolves around this beautiful sport — starting from clubs and merchandise to various football clubs and fan clubs. The amount of revenue outcomes involved in this game is just phenomenal, impacting millions of people who depend on it for their livelihood and recreation.
In this hackathon, we challenge the MachineHack community to predict the outcome of a game based on important predictors.
The hackathon will start on 20 Aug 2021 at 8:00 PM (IST)
Click here to participate.
Overview
We live in ambiguity and always need some information to make a decision. Typically, decisions are made based on possible outcomes — win, loss, pass or fail, etc. This week’s problem statement is a classic study for decision-making and understanding the odds stacked against a particular situation.
The participants need to create an ML model that can predict the outcome of a game based on unprocessed raw data.
MachineHack has created a training dataset of 7443 rows with 21 columns, including ‘Outcome’ as the target variable. On the other hand, the dataset for testing consists of 4008 rows with 20 columns.
The prerequisite skills required to participate in this hackathon include binary classification and optimising Log Loss.
Submission Guidelines
The participants must submit a .csv/.xlsx file with exactly 4008 rows and 1 column, including the ‘Outcome’. The submission will return an ‘Invalid Score’ in case of extra columns or rows.
Scikit-learn models support the predict() method to generate the predicted values.
The submission limit for this hackathon is one account per participant.
Click here to participate.
Evaluation Criteria
The evaluation of the hackathon will be done using the Log Loss metric.
The hackathon will also support private and public leaderboards. While the public leaderboard will be evaluated on 30% of the test data, the private leaderboard will be made available at the end of the hackathon and assessed on 100% of the test data.
The final score will be based on the ‘Best Score’ on the public leaderboard.
The hackathon will end on 26 Aug 2021 at 6:00 PM (IST).
The top three winners will get free passes to the Deep Learning DevCon 2021 (DLDC), scheduled to be held on 23-24 Sept 2021. In addition, the winners will also get a chance to improve their Global Leaderboard Rankings and become the ultimate MachineHack Grand Master.
Click here to participate.
Dataset Description:
- Train.csv — 7443 rows x 21 columns
- Test.csv — 4008 rows x 20 columns
Evaluation Metric:
Log Loss
Skills
- Binary Classification
- Optimising Log Loss
