{
  "fetched_at": "2026-10-19T10:48:10Z",
  "source_url": "https://www.kaggle.com/competitions/business-analytics-summer20-classification/data",
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      "name": "data-description",
      "content": "### Data Overview\n\nThe data was already pre-processed. Here's some brief overview of some of the attributes, the rest can be explored on your own.\n\n**'courier_id', 'wave_index', 'tracking_id', 'date', 'group', 'id',:**\n\nThese 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':**\n\nThese are the starting longitude and latitude of that wave of a certain courier\n\n**'level', 'speed', 'max_load':**\n\nThese 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':**\n\nThis is the weather condition of the order\n\n**'aoi_id':**\n\nThe id of the Area of Interest (i.e. the delivery destination)\n\n**'shop_id':**\n\nThe id of the shop\n      \n**'source_type', 'source_tracking_id', 'source_lng', 'source_lat':**\n\nThe information of the courier's previous action.\n\n**'target_lng', 'target_lat':**\n\nGeographical information of the target\n\n**'grid_distance':**\n\nThe shortest traversable distance to the target provided by the GPS\n\n**'hour':**\n\nThe hour in the day\n\n**'urgency'**\n\nIdentifies how urgent the order is\n\n**'action_type'**\n\nThe target variable in classification. This is masked from the test set\n\n**'expected_use_time':**\n\nThe target variable in regression. This is also masked from the test set\n\nNOT ALL VARIABLES ARE NECESSARILY NEEDED\n\nYou should use your own judgement as to what to use for your model.",
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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 decision being made by the delivery-men are mainly two-fold: pick-up in store, and delivery to customers. At one specific moment, the delivery-man may receive orders assigned to him, while he has unfinished orders which are previously assigned. He would need to make a decision as to what to do next, based on his order status and geographical location. The goal is to build a model to predict the delivery-man's next move, based on his historical decision and current status.",
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      "name": "Evaluation",
      "content": "<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.</p>\n<h2>Submission Format</h2>\nsubmission files should contain two columns: order and action_type_DELIVERY. action_type_DELIVERY should be a categorical variable: i.e. it would be 1 if action type is delivery, and 0 if otherwise.</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>order,action_type_DELIVERY<br>0,1<br><br>1,1<br><br>2,1<br></pre>\nrefer to the sample file for more information",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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