Data Science Competitions - Kaggle & DataHack - Explained in 60 Seconds

Data science competitions are an absolute must for data scientists. They offer a thrilling playground to sharpen skills, showcase expertise while solving real-world problems and competing with top talents worldwide 💪 Icing on the cake is the prize money that's at stake.

Here's your starters guide to get started with Data Science Competitions organised on platforms like: Kaggle, DataHack, etc.

✅ In the beginning 🔥

The competition begins with a captivating problem statement, accompanied by train data, test data, and an evaluation metric.

✅ During the competition 🔥

Participants train their machine learning models using the train data and generate predictions on the test data. During the competition, submissions are evaluated using the public test data, which determines the participants' rank on the public leaderboard. However, this ranking is not final.

Throughout the competition, participants can experiment with various approaches and build multiple models in an effort to improve their rank on the public leaderboard. They strategize and refine their techniques to climb higher.

✅ In the end 🔥

When the competition concludes, participants must select a final submission within the specified deadline, following the competition rules. This final submission undergoes evaluation against the private test data, which is not revealed during the competition. Based on this evaluation, the private leaderboard is published.

The private leaderboard reveals the participants' final rank, which represents their overall performance in the competition. It serves as the ultimate measure of success and determines the winners of the competition.

✅ Save for later ❤️ Happy learning.

[swetasharma]

Very useful info for data science enthusiasts 👏 👏