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  "source_url": "https://www.kaggle.com/competitions/cuhkml-2025-regression/data",
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      "name": "data-description",
      "content": "Dataframe_train and dataframe_test are the main dataset. The difference is that for the test data set, the “action type” (for the classification task) and “expected use time” (for the regression task) variables are masked, and you are asked to predict them.\n\nThe data was already pre-processed. Below is some brief overview of the variables, the rest can be explored on your own.\n\n## Variables\n*   ’courier_id’, ’wave_index’, ’tracking_id’, ’date’, ’group’, ’id’ - These are the demographic information of the order and its courier. The column names are somewhat self-explanatory.\n\n*  ’courier_wave_start_lng’, ’courier_wave_start_lat’ - These are the starting longitude and latitude of that wave of a certain courier.\n\n*  ’level’, ’speed’, ’max_load’ - These are the courier information: The level of the courier, the speed of the courier and the max load of the courier.\n\n* ’weather_grade’ - This is the weather condition of the order.\n\n* ’aoi_id’ - The id of the Area of Interest (i.e. the delivery destination).\n\n* ’shop_id’ - The id of the shop.\n\n* ’source_type’, ’source_tracking_id’, ’source_lng’, ’source_lat’ - The information of the courier’s previous action.\n\n* ’target_lng’, ’target_lat’ - The geographical information of the target.\n\n* ’grid_distance’ - The shortest traversable distance to the target provided by the GPS.\n\n* ’hour’ - The hour in the day.\n\n* ’urgency’ - Identifies how urgent the order is\n\n* ’action_type’ - The outcome variable in the classification task. This is masked in the test set.\n\n* ’expected_use_time’ - The outcome variable in the regression task. This is also masked in the test set\n\nFinally, we want to remind you that NOT ALL VARIABLES ARE NECESSARILY NEEDED. So, you should use your own judgment to decide what to use for your model.\n\n## Files\n\n*   **dataframe_train.csv** - the training set\n*   **dataframe_test.csv** - the test set\n*   **regression_example.csv** - a sample submission file in the correct format\n*   **questions_project.pdf** - Q&A",
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      "name": "Description",
      "content": "Within the context of Eleme delivery service, at every moment the command platform of “Smart Logistics” sends orders from customers to delivery-men for instant delivery. The decisions being made by the delivery-men are mainly two-fold: (i) pick-up from a store, and delivery to a customer. At any specific moment, a delivery-man may receive new orders assigned to him, while he has some unfinished orders which were previously assigned. In this situation, he needs to decide what to do next (to pick up a new order or to deliver an old one), based on his order status and geographical location.\n\nThere are two main tasks for this project, each counting 5% towards your final course grade. The first task is a classification problem, in which you are asked to build a model to predict whether the delivery-man’s next move is to pick up an order or to deliver an order. The second task is a regression problem, in which you are asked to build a model to predict the expected time of the delivery-man’s next action. For both tasks, you will use his historical decisions and current status as features. For each task, you need to submit a CSV file in the same format as the sample to the corresponding Kaggle competition.\n\nThe submission to the classification problem should be in 0-1 format, with 1 representing the ”DELIVERY” action. The data has been preprocessed, but you should decide your own approach to the problem: Which features to use, what models to try, how to deal with outliers, etc. \n\nBesides submitting your prediction results to Kaggle, you are also required to submit a PDF explaining your ideas and solution approach, together with the Jupyter Notebook with the computational results. This will be MORE important than your Kaggle score to determine your grade.",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is MAE-Score, see https://en.wikipedia.org/wiki/Mean_absolute_error\n\nSubmission Format:\nFor every item in the dataset, submission files should contain one column called ID, with a sequence from 0 to 25467. and a column of predicted expected time. The file should have a header, \"ID\" and \"expect_time\" for the two columns, and the file should have 22468 rows in total. The submission should be in CSV format",
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    {
      "name": "rules",
      "content": " - You are allowed to discuss with anyone in this world about this problem set.\n - You are also allowed to read your classmate’s solution. However, what you submit for credit have\nto be written entirely by yourself.\n - Have fun!",
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    {
      "name": "abstract",
      "content": "Have fun!",
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