When I was new to Data Science, I used to participate in hackathons to learn new techniques for solving the problems in Data Science. Hackathons are meant for a short period of time. We can learn a lot in less time in the process of improving the score/rank. When it comes to real-world problems, it is completely different from the hackathons. The data science enthusiasts who don’t have real-world experience think that both are same. I was also one of them at some point in time. So, I wrote this article to explain the differences between Real-world problems and Hackathons. Hackathons are very structured. The time period for solving the problem is fixed and there will be a leaderboard to check where we stand. The data and the evaluation metric will be provided by them. The participants can focus only on feature engineering and modelling. Most of the times, we follow 5 steps during hackathons. Preprocess data and create features Build a suitable model Make submissi
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