Our online hackathon platform, MachineHack.com is growing by leaps and bounds. The very first hackathon, “Predicting House Prices In Bengaluru” is running successfully and top three winners are going to receive individual passes to Cypher 2018. We have received overwhelming response on the discussion boards and submissions and are happy to build a product for the data science community that is so cherished.
The platform in the near future will be the hub for data science communities. Data science enthusiasts ranging from beginners to experts will find a new home in MachineHack and we are working hard behind the scenes to give amazing challenges rewards to the data science community. We are focussing very hard to build great features into the platform and collaborate with the best in the industry to bring opportunities to budding data scientists out there.
With this in mind we have decided to up the ante at MachineHack. We are launching our second inhouse hackathon called, “How To Choose The Perfect Beer”. Did you know that last year, Indians drank a total of 4.7 million litres of beer and the number is expected to go up to 6.5 billion litres by 2022. Choosing the perfect beer is complicated, therefore here at MachineHack, we have entrusted this very important job to the most trustworthy people in the world (especially when it comes to beer) to you, the data scientists.
This is a perfect competition for persons who have a beginner’s level understanding of various concepts of machine learning and data science, and are looking to polish their understanding and check how they stand against a larger community. We have exciting prizes of ₹50,000 waiting for the winners. Yes, test and apply your machine learning skills to win:
1st Prize: ₹25,000
2nd Prize: ₹15,000
3rd Prize: ₹10,000
The train and test data will consist of various features that describe a beer. In many beer cellars, important factors such as temperature and humidity are maintained by a climate control system. Hence features like Cellar Temperature and Serving Temperature become really important. This is an actual data set that is curated over months of primary and secondary research by our team. Each row contains fixed size object of features. There are nine features and each feature can be accessed by its name. With the given features, build a model to predict the score of the beer and help everyone choose the perfect beer.
We will not let out any more information more, get ready with your beer experience and machine learning skills and click here to participate.
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