Welcome to the Machine Learning Challenge for young talents!

Smartphones and mobile applications, an indispensable part of our lives, have made our daily tasks incredibly easy. However, these applications designed to appeal to all users can fall short in personalizing the user experience. Although all users perform their tasks through the same mobile application, each user has certain main menus they primarily use. Personalizing these menus and recommending them to users in a way that meets their needs will not only save time but also significantly enhance the user experience with a personalized interface. Improvements in recommendation systems and machine learning can be made in this regard.

You are expected to develop an artificial intelligence model using usage data from a mobile application to recommend to each user the menu they are most likely to need in that application.
 The dataset includes 9 menus that each user preferred in their past usage, represented as binary values. Your task is to predict which 3 out of these 9 menus should be added to the user interface.

Jaccard Score has been determined as the metric criterion. Your predictions, consisting of 9 binary values indicating whether each menu will be recommended or not, will be compared with this metric. At the end of the competition, the participant who achieves the best prediction score on the Private Leaderboard will be entitled to present the final submission.

An example output is as follows:

Customer A -> 000101010 (Menus 4, 6, and 8 are predicted to be recommended to Customer A.)

Customer B -> 100000011 (Menus 1, 8, and 9 are predicted to be recommended to Customer B.)

*Predictions should contain only 3 menus.