| ### Description |
|
|
| Welcome to the 2023 edition of Kaggle's Playground Series! Thank you to everyone who participated in and contributed to Season 3 Playground Series so far! |
|
|
| With the same goal to give the Kaggle community a variety of fairly lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science, we will continue launching the Tabular Tuesday in July every Tuesday 00:00 UTC, with each competition running for 3 weeks. Again, these will be fairly lightweight datasets that are synthetically generated from real-world data, and will provide an opportunity to quickly iterate through various model and feature engineering ideas, create visualizations, etc. |
|
|
| ### Synthetically-Generated Datasets |
|
|
| Using synthetic data for Playground competitions allows us to strike a balance between having real-world data (with named features) and ensuring test labels are not publicly available. This allows us to host competitions with more interesting datasets than in the past. While there are still challenges with synthetic data generation, the state-of-the-art is much better now than when we started the Tabular Playground Series two years ago, and the goal is to produce datasets that have far fewer artifacts. Please feel free to give us feedback on the datasets for the different competitions so that we can continue to improve! |
|
|
| ### Evaluation |
|
|
| Submissions are evaluated on SMAPE between forecasts and actual values. We define SMAPE = 0 when the actual and predicted values are both 0. |
|
|
| ### Submission File |
|
|
| For each id in the test set, you must predict the corresponding num_sold. The file should contain a header and have the following format: |
|
|
| ``` |
| id, num_sold |
| 136950, 100 |
| 136951, 100 |
| 136952, 100 |
| etc. |
| ``` |
|
|
| ### Dataset Description |
|
|
| For this challenge, you will be predicting a full year worth of sales for various fictitious learning modules from different fictitious Kaggle-branded stores in different (real!) countries. This dataset is completely synthetic, but contains many effects you see in real-world data, e.g., weekend and holiday effect, seasonality, etc. You are given the task of predicting sales during the year 2022. |
|
|
| Good luck! |
|
|
| ### Files |
|
|
| #### train.csv |
| - The training set, which includes the sales data for each date-country-store-item combination. |
|
|
| #### test.csv |
| - The test set; your task is to predict the corresponding item sales for each date-country-store-item combination. Note the Public leaderboard is scored on the first quarter of the test year, and the Private on the remaining. |
|
|
| #### sample_submission.csv |
| - A sample submission file in the correct format. |
