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Second Semester Project for Machine Learning 2020/2021 Course
This is the second semester project for students enrolled in the Machine Learning 2020/2021 course (1000-2N09SUS) at the Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw.
The goal of this competition is to create an efficient forecasting model for predicting sales of products offered by the FitFood company at one of their FitBoxy locations in Poland (FitBoxy - Inteligentna lodówka).
More detailed competition rules are given in Terms and Conditions.
The description of the data and evaluation metric is in the Task description section.
The submission system opens on Tuesday, May 18. The deadline for sending the solutions and reports is Friday, June 11, 23:59 GMT.
Participants of the challenge are obliged to follow the competition rules:
- This challenge is organized by Andrzej Janusz (the Organizer) for students enrolled in the Machine Learning 2020/2021 course (1000-2N09SUS) at the Faculty of Mathematics, Informatics, and Mechanics of the University of Warsaw.
- The provided data sets are the property of the Organizer and the KnowledgePit platform. It is forbidden to share or redistribute provided data sets to any third party without explicit consent from the Organizer.
- Participants should produce the solution individually. No merging into teams composed of two or more persons is allowed.
- Each person has a limited number of submissions - the limit is set to 100.
- The number of submissions per day is limited to 10.
- Participants can use data that was made available in the challenge - using any external resources is possible only after receiving explicit consent from the Organizer. Queries regarding the external resources need to be issued through the competition forum.
- It is strictly forbidden to hack the provided data or to exploit any unfair data leak that can improve the solution score. All attempts at making predictions for any test instance using information extracted from other test instances will result in disqualification.
- The deadline for submitting the solutions is January 27, 2021 (23:59 GMT). Late submissions will not be accepted.
- Each team is obliged to provide a short report describing their final solution. The report must contain information such as the name of the team, the names of all team members, and a brief overview of the used approach. The description should explain all data preprocessing steps and model construction steps. It should be submitted in pdf format using our submission system by June 11, 2021 (23:59 GMT).
- By enrolling in this competition, you grant the Organizer the right to process your submissions and reports for the purpose of evaluation and post-competition research.
- The final project score will depend on the quality of the solution (the score obtained in the final evaluation), and on the quality of the submitted report.
Provided data describe a short-term sales history of products offered in FitBoxy vending machines at various points of sales (PoS). The prediction targets from the SUS_project_training_targets.txt file, correspond to future 7-day sales of a given product at a particular location.
The data is provided in a tabular format, with each row corresponding to a different instance.
The data tables are provided as two CSV files with the ';' separator sign. They can be downloaded after the registration for the challenge. Both files (training and test sets) have exactly the same format. The target values for the training data is provided as a separate file.
The evaluation metric will be R^2. During the challenge, your solutions will be evaluated on a small fraction of the test set (5000 instances), and your best preliminary score will be displayed on the public Leaderboard.
The submission format: the solutions need to be submitted as text files with predictions. The file should have exactly the same number of rows as the test data table (i.e. 70000 rows). In each row, it should contain exactly one real number expressing the predicted sales in the following 7 days.
The competition is sponsored by QED Software
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