{ "fetched_at": "2026-10-20T09:31:52Z", "source_url": "https://www.kaggle.com/competitions/chinese-railways-delay-prediction-public/data", "pages": [ { "name": "data-description", "content": "English:​​\n\nThis competition challenges participants to predict ​the arrival delay (in minutes) at the final station​ of the high-speed train G77 service (\"G77\" is the train ID). The training data consists of historical journey records for this specific train, detailing its schedule and performance at each station along its route between Beijing and Guangzhou South. The test data provides the scheduled information for a future journey of the same train service (G77) but omits the delay information at the final station (Guangzhou South). Competitors must build a model using the training data to predict the delay minutes upon arrival at Guangzhou South in the test data.\n\n​中文 (Chinese):​​\n\n本次竞赛要求参赛者预测高铁 G77 次列车（\"G77\"为列车车次号）​在终点站（广州南）的到达延误时长（单位：分钟）​。训练数据包含该特定列车的历史运行记录，详细描述了其在北京至广州南沿途各车站的时刻表与实际运行情况。测试数据提供了同一列车（G77）未来一趟旅程的时刻表信息，但缺少终点站（广州南）的延误信息。参赛者必须利用训练数据构建模型，以预测测试数据中列车到达广州南时的延误分钟数。\n\nFiles / 文件\n​train.csv​ - The ​primary training data. Comprised of historical records for the ​G77 train​ service. Each row represents a scheduled stop (station) along a specific journey on a specific date. One file per journey date exists initially (e.g., 20250703_G77.csv), but ​competitors must merge all [YYYYMMDD]_G77.csvfiles into a single train.csvfile​ for model training. Contains the 延误分钟 (Delay Minutes)column.\n\n​主训练数据。包含 ​G77 次列车的历史运行记录。每一行代表特定日期、特定车次在沿途某一车站的时刻信息。初始状态为每个旅程日期一个文件（例如 20250703_G77.csv），但参赛者必须将所有 [YYYYMMDD]_G77.csv文件合并成一个 train.csv文件用于模型训练。包含 延误分钟 (Delay Minutes)列。\n\n​test.csv​ - The ​test set. Contains the ​scheduled information​ for one future journey (dated 2025-10-14) of the ​G77 train​ service from Beijing to Guangzhou South. The 延误分钟 (Delay Minutes)column is ​MISSING​ for the final station (广州南/ Guangzhou South) - this is the target variable to predict.\n\n​测试集。包含 ​G77 次列车​ 一趟未来旅程（日期 2025-10-14）从北京开往广州南的时刻表信息。终点站 (广州南/ Guangzhou South) 的 延误分钟 (Delay Minutes)列缺失——这正是需要预测的目标变量。\n\n​sample_submission.csv​ - A sample submission file demonstrating the required format. It will have one row corresponding to the final station (Guangzhou South/ 广州南) entry in test.csvwith the predicted 延误分钟 (Delay_Minutes).\n\n​样本提交文件。展示所需格式的示例文件。该文件将有一行，对应于 test.csv中终点站 (广州南/ Guangzhou South) 的记录，并包含预测的 延误分钟 (Delay_Minutes)值。\n\n​metaData.csv​ - (Optional/Recommended)A file providing supplemental information, such as:\n\nStation names and potentially latitude/longitude or region.\n\nExplanation of holidays/events during journey dates.\n\nWeather codes/stats for the journey dates/stations. Not provided in the initial sample.\n\n​元数据文件​ - (可选/推荐)提供补充信息的文件，例如：\n\n车站名称以及可能的经纬度或区域信息。\n\n运行日期期间节假日/特殊事件的说明。\n\n运行日期及车站的天气代码/统计数据。初始样本中未提供此文件。\n\nColumns / 数据列\n​车次ID (Train_ID): Unique identifier for the train service. ​Always G77in this dataset. Indicates data is solely for this specific train route.\n\n​列车车次标识符。该数据集中始终为 G77。表示所有数据仅针对这一特定列车运行路线。\n\nFormat:String (e.g., G77).\n\nImportance:Key identifier. All provided data belongs to this train service.\n\n​车站名 (Station_Name): Name of the train station. The route sequence is 北京(Beijing) -> 北京西(Beijing West) -> 石家庄(Shijiazhuang) -> 郑州东(Zhengzhou East) -> 信阳东(Xinyang East) -> 武汉(Wuhan) -> 长沙南(Changsha South) -> 衡阳东(Hengyang East) -> 广州南(Guangzhou South).\n\n​车站名称。列车停靠站点名称。运行路线顺序为：北京(Beijing) -> 北京西(Beijing West) -> 石家庄(Shijiazhuang) -> 郑州东(Zhengzhou East) -> 信阳东(Xinyang East) -> 武汉(Wuhan) -> 长沙南(Changsha South) -> 衡阳东(Hengyang East) -> 广州南(Guangzhou South)。\n\nFormat:String (e.g., 北京西, 广州南).\n\nImportance:Defines the sequence and location of stops. 广州南is the final station and prediction target location.\n\n​到达日期 (Arrival_Date): The date (year-month-day) the train was scheduled to arrive at this station.\n\n​到达日期。列车计划到达该车站的日期（年-月-日）。\n\nFormat:Date (YYYY-MM-DD).\n\n​到达时间 (Arrival_Time): The scheduled time of day (hour:minute) the train was planned to arrive at this station.\n\n​到达时间。列车计划到达该车站的时刻（时:分）。\n\nFormat:Time (HH:MM, 24-hour).\n\n​出发日期 (Departure_Date): The date (year-month-day) the train was scheduled to depart from this station. (Note: In the sample data, 到达日期 (Arrival_Date)and 出发日期 (Departure_Date)are always the same, as no journeys span midnight).\n\n​出发日期。列车计划从该车站出发的日期（年-月-日）。(注意：在样本数据中，到达日期 (Arrival_Date)和 出发日期 (Departureure_Date)总是相同，因为无跨午夜旅程）。\n\nFormat:Date (YYYY-MM-DD).\n\n​出发时间 (Departure_Time): The scheduled time of day (hour:minute) the train was planned to depart from this station.\n\n​出发时间。列车计划从该车站出发的时刻（时:分）。\n\nFormat:Time (HH:MM, 24-hour).\n\n​延误分钟 (Delay_Minutes): The ​target variable​ (present only in train.csv). The actual observed delay, in minutes, upon ​arrival​ at this station (e.g., 2= 2 minutes late). A value of 0indicates on-time arrival. ​For the test.csv, this value is MISSING for the final station (广州南/ Guangzhou South) and must be predicted by the model.​​ Note: The 出发时间 (Departure Time)may also be implicitly delayed if the train arrives late, but only the ​arrival delay at each station (especially the final station)​​ is explicitly recorded in this column and is the prediction target.\n\n​延误分钟​：​目标变量​（仅在 train.csv中存在）。列车到达该车站时的实际观察到的延误分钟数（例如，2表示晚点 2 分钟）。0表示准点到达。​在 test.csv中，终点站 (广州南/ Guangzhou South) 的这一值是缺失的，必须由模型预测。​​ 注意：出发时间 (Departure Time)可能会因列车到达晚点而隐性延误，但此列明确记录的是各站（尤其是终点站）的到达延误，且是预测目标。\n\nFormat:Integer (can be 0or positive, negative values unlikely but theoretically possible if early arrival).\n\nImportance:​This is the prediction target.​​\n\n​distance: The ​cumulative distance traveled​ (in kilometers) from the origin station (北京/ Beijing)to the current station. This value increases sequentially along the route.\n\n​距离​：从始发站 (北京/ Beijing) 到当前车站的累计行驶距离​（单位：公里）。该值沿路线依次增加。\n\nFormat:Integer (e.g., 0, 9, 290, 2253).\n\nImportance:Key feature indicating progress along the route.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "English:​​\n\nThis competition challenges participants to predict ​the arrival delay (in minutes) at the final station​ of the high-speed train G77 service (\"G77\" is the train ID). The training data consists of historical journey records for this specific train, detailing its schedule and performance at each station along its route between Beijing and Guangzhou South. The test data provides the scheduled information for a future journey of the same train service (G77) but omits the delay information at the final station (Guangzhou South). Competitors must build a model using the training data to predict the delay minutes upon arrival at Guangzhou South in the test data.\n\n​中文 (Chinese):​​\n\n本次竞赛要求参赛者预测高铁 G77 次列车（\"G77\"为列车车次号）​在终点站（广州南）的到达延误时长（单位：分钟）​。训练数据包含该特定列车的历史运行记录，详细描述了其在北京至广州南沿途各车站的时刻表与实际运行情况。测试数据提供了同一列车（G77）未来一趟旅程的时刻表信息，但缺少终点站（广州南）的延误信息。参赛者必须利用训练数据构建模型，以预测测试数据中列车到达广州南时的延误分钟数。\n\nFiles / 文件\n​train.csv​ - The ​primary training data. Comprised of historical records for the ​G77 train​ service. Each row represents a scheduled stop (station) along a specific journey on a specific date. One file per journey date exists initially (e.g., 20250703_G77.csv), but ​competitors must merge all [YYYYMMDD]_G77.csvfiles into a single train.csvfile​ for model training. Contains the 延误分钟 (Delay Minutes)column.\n\n​主训练数据。包含 ​G77 次列车的历史运行记录。每一行代表特定日期、特定车次在沿途某一车站的时刻信息。初始状态为每个旅程日期一个文件（例如 20250703_G77.csv），但参赛者必须将所有 [YYYYMMDD]_G77.csv文件合并成一个 train.csv文件用于模型训练。包含 延误分钟 (Delay Minutes)列。\n\n​test.csv​ - The ​test set. Contains the ​scheduled information​ for one future journey (dated 2025-10-14) of the ​G77 train​ service from Beijing to Guangzhou South. The 延误分钟 (Delay Minutes)column is ​MISSING​ for the final station (广州南/ Guangzhou South) - this is the target variable to predict.\n\n​测试集。包含 ​G77 次列车​ 一趟未来旅程（日期 2025-10-14）从北京开往广州南的时刻表信息。终点站 (广州南/ Guangzhou South) 的 延误分钟 (Delay Minutes)列缺失——这正是需要预测的目标变量。\n\n​sample_submission.csv​ - A sample submission file demonstrating the required format. It will have one row corresponding to the final station (Guangzhou South/ 广州南) entry in test.csvwith the predicted 延误分钟 (Delay_Minutes).\n\n​样本提交文件。展示所需格式的示例文件。该文件将有一行，对应于 test.csv中终点站 (广州南/ Guangzhou South) 的记录，并包含预测的 延误分钟 (Delay_Minutes)值。\n\n​metaData.csv​ - (Optional/Recommended)A file providing supplemental information, such as:\n\nStation names and potentially latitude/longitude or region.\n\nExplanation of holidays/events during journey dates.\n\nWeather codes/stats for the journey dates/stations. Not provided in the initial sample.\n\n​元数据文件​ - (可选/推荐)提供补充信息的文件，例如：\n\n车站名称以及可能的经纬度或区域信息。\n\n运行日期期间节假日/特殊事件的说明。\n\n运行日期及车站的天气代码/统计数据。初始样本中未提供此文件。\n\nColumns / 数据列\n​车次ID (Train_ID): Unique identifier for the train service. ​Always G77in this dataset. Indicates data is solely for this specific train route.\n\n​列车车次标识符。该数据集中始终为 G77。表示所有数据仅针对这一特定列车运行路线。\n\nFormat:String (e.g., G77).\n\nImportance:Key identifier. All provided data belongs to this train service.\n\n​车站名 (Station_Name): Name of the train station. The route sequence is 北京(Beijing) -> 北京西(Beijing West) -> 石家庄(Shijiazhuang) -> 郑州东(Zhengzhou East) -> 信阳东(Xinyang East) -> 武汉(Wuhan) -> 长沙南(Changsha South) -> 衡阳东(Hengyang East) -> 广州南(Guangzhou South).\n\n​车站名称。列车停靠站点名称。运行路线顺序为：北京(Beijing) -> 北京西(Beijing West) -> 石家庄(Shijiazhuang) -> 郑州东(Zhengzhou East) -> 信阳东(Xinyang East) -> 武汉(Wuhan) -> 长沙南(Changsha South) -> 衡阳东(Hengyang East) -> 广州南(Guangzhou South)。\n\nFormat:String (e.g., 北京西, 广州南).\n\nImportance:Defines the sequence and location of stops. 广州南is the final station and prediction target location.\n\n​到达日期 (Arrival_Date): The date (year-month-day) the train was scheduled to arrive at this station.\n\n​到达日期。列车计划到达该车站的日期（年-月-日）。\n\nFormat:Date (YYYY-MM-DD).\n\n​到达时间 (Arrival_Time): The scheduled time of day (hour:minute) the train was planned to arrive at this station.\n\n​到达时间。列车计划到达该车站的时刻（时:分）。\n\nFormat:Time (HH:MM, 24-hour).\n\n​出发日期 (Departure_Date): The date (year-month-day) the train was scheduled to depart from this station. (Note: In the sample data, 到达日期 (Arrival_Date)and 出发日期 (Departure_Date)are always the same, as no journeys span midnight).\n\n​出发日期。列车计划从该车站出发的日期（年-月-日）。(注意：在样本数据中，到达日期 (Arrival_Date)和 出发日期 (Departureure_Date)总是相同，因为无跨午夜旅程）。\n\nFormat:Date (YYYY-MM-DD).\n\n​出发时间 (Departure_Time): The scheduled time of day (hour:minute) the train was planned to depart from this station.\n\n​出发时间。列车计划从该车站出发的时刻（时:分）。\n\nFormat:Time (HH:MM, 24-hour).\n\n​延误分钟 (Delay_Minutes): The ​target variable​ (present only in train.csv). The actual observed delay, in minutes, upon ​arrival​ at this station (e.g., 2= 2 minutes late). A value of 0indicates on-time arrival. ​For the test.csv, this value is MISSING for the final station (广州南/ Guangzhou South) and must be predicted by the model.​​ Note: The 出发时间 (Departure Time)may also be implicitly delayed if the train arrives late, but only the ​arrival delay at each station (especially the final station)​​ is explicitly recorded in this column and is the prediction target.\n\n​延误分钟​：​目标变量​（仅在 train.csv中存在）。列车到达该车站时的实际观察到的延误分钟数（例如，2表示晚点 2 分钟）。0表示准点到达。​在 test.csv中，终点站 (广州南/ Guangzhou South) 的这一值是缺失的，必须由模型预测。​​ 注意：出发时间 (Departure Time)可能会因列车到达晚点而隐性延误，但此列明确记录的是各站（尤其是终点站）的到达延误，且是预测目标。\n\nFormat:Integer (can be 0or positive, negative values unlikely but theoretically possible if early arrival).\n\nImportance:​This is the prediction target.​​\n\n​distance: The ​cumulative distance traveled​ (in kilometers) from the origin station (北京/ Beijing)to the current station. This value increases sequentially along the route.\n\n​距离​：从始发站 (北京/ Beijing) 到当前车站的累计行驶距离​（单位：公里）。该值沿路线依次增加。\n\nFormat:Integer (e.g., 0, 9, 290, 2253).\n\nImportance:Key feature indicating progress along the route.\n\n​Note on G77Column:​​ This column appears redundant as 车次ID (Train_ID)already identifies the train. Double-check if the first column is indeed named 车次IDand the G77column is a separate artifact from reading the file. If so, it can likely be ​ignored or dropped, as the 车次ID (Train_ID)column (G77) provides the necessary identification.\n\n​关于 G77列的说明：​​ 该列似乎与 车次ID (Train_ID)列内容重复。请确认第一列名称确实是 车次ID，且 G77列是读取文件时产生的冗余列。如果确实如此，则可以忽略或删除该列，因为 车次ID (Train_ID)列（包含 G77）已提供必要的标识信息。", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The evaluation section describes how submissions will be scored and how participants should format their submissions. You don't need a sub-title at the top; the page title appears above. Below is an example of a typical evaluation page.\n\n---\n\nSubmissions are evaluated on [area under the ROC curve](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) between the predicted probability and the observed target.\n\n\n## Submission File\nFor each ID in the test set, you must predict a probability for the TARGET variable. The file should contain a header and have the following format:\n\n station_name,predict_delay_time\n 北京,0\n 北京西,0\n 石家庄,0\n ...\n\n etc.", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "##ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.\n\n**[See Section 3.18 for defined terms](rules#18.-terms)**\n\n*The Competition named below is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. To enter the Competition, you must agree to these Official Competition Rules, which incorporate by reference the provisions and content of the Competition Website and any Specific Competition Rules herein (collectively, the \"Rules\"). Please read these Rules carefully before entry to ensure you understand and agree. You further agree that Submission in the Competition constitutes agreement to these Rules. You may not submit to the Competition and are not eligible to receive the prizes associated with this Competition unless you agree to these Rules. These Rules form a binding legal agreement between you and the Competition Sponsor with respect to the Competition. Your competition Submissions must conform to the requirements stated on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes, if any, will be awarded to Participants with the best scores, based on the merits of the data science models submitted. See below for the complete Competition Rules.*\n\n**You cannot sign up to Kaggle from multiple accounts and therefore you cannot enter or submit from multiple accounts.**\n\n

1. COMPETITION-SPECIFIC TERMS

\n

1. COMPETITION TITLE

[INSERT]\n

2. COMPETITION SPONSOR

[INSERT]\n

3. COMPETITION SPONSOR ADDRESS

[INSERT]\n

4. COMPETITION WEBSITE

https://www.kaggle.com/competitions/[INSERT]\n[INSERT NON-MONETARY PRIZES AS APPLICABLE]\n

6. WINNER LICENSE TYPE

[INSERT]\n

7. DATA ACCESS AND USE

[INSERT]\n\n\n###2. COMPETITION-SPECIFIC RULES \nIn addition to the provisions of the General Competition Rules below, you understand and agree to these Competition-Specific Rules required by the Competition Sponsor:\n\n####1. TEAM LIMITS\na. The maximum Team size is five (5).\nb. Team mergers are allowed and can be performed by the Team leader. In order to merge, the combined Team must have a total Submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of Submissions per day multiplied by the number of days the competition has been running.\n\n####2. SUBMISSION LIMITS\na. You may submit a maximum of five (5) Submissions per day.\nb. You may select up to two (2) Final Submissions for judging.\n\n####3. COMPETITION TIMELINE\na. Competition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition’s Overview > Timeline page.\n\n####4. COMPETITION DATA\n\na. Data Access and Use. [CHOOSE ONE OF THE 3 OPTIONS BELOW BASED ON WHAT THE SPONSOR CHOOSES IN THE SOW.]\n\n1. [*Competition Use only is checked*: You may access and use the Competition Data only for participating in the Competition and on Kaggle.com forums. The Competition Sponsor reserves the right to disqualify any Participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.]\n\n2. [*Competition Use and Non-Commercial & Academic Research is checked*: You may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any Participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.]\n\n3. [*Competition Use and Commercial are checked*: You may access and use the Competition Data for any purpose, whether commercial or non-commercial, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any Participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.]\n\n4. [*ADD IN THE FOLLOWING, IN ADDITION TO THE CHOSEN SECTION ABOVE, IF SPECIFIC LICENSE IS CHECKED*: The Competition Data is also subject to the following terms and conditions: [INSERT URL]. \n\nb. Data Security. \n\n1. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.\n\n####5. WINNER LICENSE [INSERT ONLY THE RELEVANT LICENSE LANGUAGE FOR THE COMPETITION.]\n\na. Under Section 2.8 (Winners Obligations) of the General Rules below, you hereby grant and will grant the Competition Sponsor the following license(s) with respect to your Submission if you are a Competition winner:\n\n1. [*Non-Exclusive*: You hereby grant and will grant to Competition Sponsor and its designees a worldwide, non-exclusive, sub-licensable, transferable, fully paid-up, royalty-free, perpetual, irrevocable right to use, reproduce, distribute, create derivative works of, publicly perform, publicly display, digitally perform, make, have made, sell, offer for sale and import your winning Submission and the source code used to generate the Submission, in any media now known or developed in the future, for any purpose whatsoever, commercial or otherwise, without further approval by or payment to you.]\n \n2. [*Open Source: You hereby license and will license your winning Submission and the source code used to generate the Submission under an Open Source Initiative-approved license (see [www.opensource.org] (http://www.opensource.org)) that in no event limits commercial use of such code or model containing or depending on such code. \n\n3. For generally commercially available software that you used to generate your Submission that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, you do not need to grant the license in the preceding Section for that software. \n\n4. In the event that input data or pretrained models with an incompatible license are used to generate your winning solution, you do not need to grant an open source license in the preceding Section for that data and/or model(s). \n\nb. You may be required by the Sponsor to provide a detailed description of how the winning Submission was generated, to the Competition Sponsor’s specifications, as outlined in Section 2.8, Winner’s Obligations. This may include a detailed description of methodology, where one must be able to reproduce the approach by reading the description, and includes a detailed explanation of the architecture, preprocessing, loss function, training details, hyper-parameters, etc. The description should also include a link to a code repository with complete and detailed instructions so that the results obtained can be reproduced.\n \n####6. EXTERNAL DATA AND TOOLS\n\na. You may use data other than the Competition Data (“External Data”) to develop and test your Submissions. However, you will ensure the External Data is either publicly available and equally accessible to use by all Participants of the Competition for purposes of the competition at no cost to the other Participants, or satisfies the Reasonableness criteria as outlined in Section 2.6.b below. The ability to use External Data under this Section does not limit your other obligations under these Competition Rules, including but not limited to Section 2.8 (Winners Obligations). \n\nb. The use of external data and models is acceptable unless specifically prohibited by the Host. Because of the potential costs or restrictions (e.g., “geo restrictions”) associated with obtaining rights to use external data or certain software and associated tools, their use must be “reasonably accessible to all” and of “minimal cost”. Also, regardless of the cost challenges as they might affect all Participants during the course of the competition, the costs of potentially procuring a license for software used to generate a Submission, must also be considered. The Host will employ an assessment of whether or not the following criteria can exclude the use of the particular LLM, data set(s), or tool(s):\n\n1. Are Participants being excluded from a competition because of the \"excessive\" costs for access to certain LLMs, external data, or tools that might be used by other Participants. The Host will assess the excessive cost concern by applying a “Reasonableness” standard (the “Reasonableness Standard”). The Reasonableness Standard will be determined and applied by the Host in light of things like cost thresholds and accessibility.\n\n2. By way of example only, a small subscription charge to use additional elements of a large language model such as Gemini Advanced are acceptable if meeting the Reasonableness Standard of Sec. 8.2. Purchasing a license to use a proprietary dataset that exceeds the cost of a prize in the competition would not be considered reasonable.\n\nc. Automated Machine Learning Tools (“AMLT”)\n\n1. Individual Participants and Teams may use automated machine learning tool(s) (“AMLT”) (e.g., Google toML, H2O Driverless AI, etc.) to create a Submission, provided that the Participant or Team ensures that they have an appropriate license to the AMLT such that they are able to comply with the Competition Rules. \n\n####7. ELIGIBILITY\n\na. Unless otherwise stated in the Competition-Specific Rules above or prohibited by internal policies of the Competition Entities, employees, interns, contractors, officers and directors of Competition Entities may enter and participate in the Competition, but are not eligible to win any Prizes. \"Competition Entities\" means the Competition Sponsor, Kaggle Inc., and their respective parent companies, subsidiaries and affiliates. If you are such a Participant from a Competition Entity, you are subject to all applicable internal policies of your employer with respect to your participation.\n\n####8. WINNER’S OBLIGATIONS\n\na. As a condition to being awarded a Prize, a Prize winner must fulfill the following obligations:\n\n1. Deliver to the Competition Sponsor the final model's software code as used to generate the winning Submission and associated documentation. The delivered software code should follow [these documentation guidelines][5], must be capable of generating the winning Submission, and contain a description of resources required to build and/or run the executable code successfully. For avoidance of doubt, delivered software code should include training code, inference code, and a description of the required computational environment. \n\na. To the extent that the final model’s software code includes generally commercially available software that is not owned by you, but that can be procured by the Competition Sponsor without undue expense, then instead of delivering the code for that software to the Competition Sponsor, you must identify that software, method for procuring it, and any parameters or other information necessary to replicate the winning Submission; Individual Participants and Teams who create a Submission using an AMLT may win a Prize. However, for clarity, the potential winner’s Submission must still meet the requirements of these Rules, including but not limited to Section 2.5 (Winners License), Section 2.8 (Winners Obligations), and Section 3.14 (Warranty, Indemnity, and Release).” \n\nb. Individual Participants and Teams who create a Submission using an AMLT may win a Prize. However, for clarity, the potential winner’s Submission must still meet the requirements of these Rules,\n\n2. Grant to the Competition Sponsor the license to the winning Submission stated in the Competition Specific Rules above, and represent that you have the unrestricted right to grant that license;\n\n3. Sign and return all Prize acceptance documents as may be required by Competition Sponsor or Kaggle, including without limitation: (a) eligibility certifications; (b) licenses, releases and other agreements required under the Rules; and (c) U.S. tax forms (such as IRS Form W-9 if U.S. resident, IRS Form W-8BEN if foreign resident, or future equivalents).\n\n####9. GOVERNING LAW\n\na. Unless otherwise provided in the Competition Specific Rules above, all claims arising out of or relating to these Rules will be governed by California law, excluding its conflict of laws rules, and will be litigated exclusively in the Federal or State courts of Santa Clara County, California, USA. The parties consent to personal jurisdiction in those courts. If any provision of these Rules is held to be invalid or unenforceable, all remaining provisions of the Rules will remain in full force and effect.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "abstract", "content": "English:​​\n\nThis competition challenges participants to predict ​the arrival delay (in minutes) at the final station​ of the high-speed train G77 service (\"G77\" is the train ID). The training data consists of historical journey records for this specific train, detailing its schedule and performance at each station along its route between Beijing and Guangzhou South. The test data provides the scheduled information for a future journey of the same train service (G77) but omits the delay information at the final station (Guangzhou South). Competitors must build a model using the training data to predict the delay minutes upon arrival at Guangzhou South in the test data.\n\n​中文 (Chinese):​​\n\n本次竞赛要求参赛者预测高铁 G77 次列车（\"G77\"为列车车次号）​在终点站（广州南）的到达延误时长（单位：分钟）​。训练数据包含该特定列车的历史运行记录，详细描述了其在北京至广州南沿途各车站的时刻表与实际运行情况。测试数据提供了同一列车（G77）未来一趟旅程的时刻表信息，但缺少终点站（广州南）的延误信息。参赛者必须利用训练数据构建模型，以预测测试数据中列车到达广州南时的延误分钟数。\n\nFiles / 文件\n​train.csv​ - The ​primary training data. Comprised of historical records for the ​G77 train​ service. Each row represents a scheduled stop (station) along a specific journey on a specific date. One file per journey date exists initially (e.g., 20250703_G77.csv), but ​competitors must merge all [YYYYMMDD]_G77.csvfiles into a single train.csvfile​ for model training. Contains the 延误分钟 (Delay Minutes)column.\n\n​主训练数据。包含 ​G77 次列车的历史运行记录。每一行代表特定日期、特定车次在沿途某一车站的时刻信息。初始状态为每个旅程日期一个文件（例如 20250703_G77.csv），但参赛者必须将所有 [YYYYMMDD]_G77.csv文件合并成一个 train.csv文件用于模型训练。包含 延误分钟 (Delay Minutes)列。\n\n​test.csv​ - The ​test set. Contains the ​scheduled information​ for one future journey (dated 2025-10-14) of the ​G77 train​ service from Beijing to Guangzhou South. The 延误分钟 (Delay Minutes)column is ​MISSING​ for the final station (广州南/ Guangzhou South) - this is the target variable to predict.\n\n​测试集。包含 ​G77 次列车​ 一趟未来旅程（日期 2025-10-14）从北京开往广州南的时刻表信息。终点站 (广州南/ Guangzhou South) 的 延误分钟 (Delay Minutes)列缺失——这正是需要预测的目标变量。\n\n​sample_submission.csv​ - A sample submission file demonstrating the required format. It will have one row corresponding to the final station (Guangzhou South/ 广州南) entry in test.csvwith the predicted 延误分钟 (Delay_Minutes).\n\n​样本提交文件。展示所需格式的示例文件。该文件将有一行，对应于 test.csv中终点站 (广州南/ Guangzhou South) 的记录，并包含预测的 延误分钟 (Delay_Minutes)值。\n\n​metaData.csv​ - (Optional/Recommended)A file providing supplemental information, such as:\n\nStation names and potentially latitude/longitude or region.\n\nExplanation of holidays/events during journey dates.\n\nWeather codes/stats for the journey dates/stations. Not provided in the initial sample.\n\n​元数据文件​ - (可选/推荐)提供补充信息的文件，例如：\n\n车站名称以及可能的经纬度或区域信息。\n\n运行日期期间节假日/特殊事件的说明。\n\n运行日期及车站的天气代码/统计数据。初始样本中未提供此文件。\n\nColumns / 数据列\n​车次ID (Train_ID): Unique identifier for the train service. ​Always G77in this dataset. Indicates data is solely for this specific train route.\n\n​列车车次标识符。该数据集中始终为 G77。表示所有数据仅针对这一特定列车运行路线。\n\nFormat:String (e.g., G77).\n\nImportance:Key identifier. All provided data belongs to this train service.\n\n​车站名 (Station_Name): Name of the train station. The route sequence is 北京(Beijing) -> 北京西(Beijing West) -> 石家庄(Shijiazhuang) -> 郑州东(Zhengzhou East) -> 信阳东(Xinyang East) -> 武汉(Wuhan) -> 长沙南(Changsha South) -> 衡阳东(Hengyang East) -> 广州南(Guangzhou South).\n\n​车站名称。列车停靠站点名称。运行路线顺序为：北京(Beijing) -> 北京西(Beijing West) -> 石家庄(Shijiazhuang) -> 郑州东(Zhengzhou East) -> 信阳东(Xinyang East) -> 武汉(Wuhan) -> 长沙南(Changsha South) -> 衡阳东(Hengyang East) -> 广州南(Guangzhou South)。\n\nFormat:String (e.g., 北京西, 广州南).\n\nImportance:Defines the sequence and location of stops. 广州南is the final station and prediction target location.\n\n​到达日期 (Arrival_Date): The date (year-month-day) the train was scheduled to arrive at this station.\n\n​到达日期。列车计划到达该车站的日期（年-月-日）。\n\nFormat:Date (YYYY-MM-DD).\n\n​到达时间 (Arrival_Time): The scheduled time of day (hour:minute) the train was planned to arrive at this station.\n\n​到达时间。列车计划到达该车站的时刻（时:分）。\n\nFormat:Time (HH:MM, 24-hour).\n\n​出发日期 (Departure_Date): The date (year-month-day) the train was scheduled to depart from this station. (Note: In the sample data, 到达日期 (Arrival_Date)and 出发日期 (Departure_Date)are always the same, as no journeys span midnight).\n\n​出发日期。列车计划从该车站出发的日期（年-月-日）。(注意：在样本数据中，到达日期 (Arrival_Date)和 出发日期 (Departureure_Date)总是相同，因为无跨午夜旅程）。\n\nFormat:Date (YYYY-MM-DD).\n\n​出发时间 (Departure_Time): The scheduled time of day (hour:minute) the train was planned to depart from this station.\n\n​出发时间。列车计划从该车站出发的时刻（时:分）。\n\nFormat:Time (HH:MM, 24-hour).\n\n​延误分钟 (Delay_Minutes): The ​target variable​ (present only in train.csv). The actual observed delay, in minutes, upon ​arrival​ at this station (e.g., 2= 2 minutes late). A value of 0indicates on-time arrival. ​For the test.csv, this value is MISSING for the final station (广州南/ Guangzhou South) and must be predicted by the model.​​ Note: The 出发时间 (Departure Time)may also be implicitly delayed if the train arrives late, but only the ​arrival delay at each station (especially the final station)​​ is explicitly recorded in this column and is the prediction target.\n\n​延误分钟​：​目标变量​（仅在 train.csv中存在）。列车到达该车站时的实际观察到的延误分钟数（例如，2表示晚点 2 分钟）。0表示准点到达。​在 test.csv中，终点站 (广州南/ Guangzhou South) 的这一值是缺失的，必须由模型预测。​​ 注意：出发时间 (Departure Time)可能会因列车到达晚点而隐性延误，但此列明确记录的是各站（尤其是终点站）的到达延误，且是预测目标。\n\nFormat:Integer (can be 0or positive, negative values unlikely but theoretically possible if early arrival).\n\nImportance:​This is the prediction target.​​\n\n​distance: The ​cumulative distance traveled​ (in kilometers) from the origin station (北京/ Beijing)to the current station. This value increases sequentially along the route.\n\n​距离​：从始发站 (北京/ Beijing) 到当前车站的累计行驶距离​（单位：公里）。该值沿路线依次增加。\n\nFormat:Integer (e.g., 0, 9, 290, 2253).\n\nImportance:Key feature indicating progress along the route.\n\n​Note on G77Column:​​ This column appears redundant as 车次ID (Train_ID)already identifies the train. Double-check if the first column is indeed named 车次IDand the G77column is a separate artifact from reading the file. If so, it can likely be ​ignored or dropped, as the 车次ID (Train_ID)column (G77) provides the necessary identification.\n\n​关于 G77列的说明：​​ 该列似乎与 车次ID (Train_ID)列内容重复。请确认第一列名称确实是 车次ID，且 G77列是读取文件时产生的冗余列。如果确实如此，则可以忽略或删除该列，因为 车次ID (Train_ID)列（包含 G77）已提供必要的标识信息。", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "foundational-rules", "content": "The following Kaggle Competition Foundational Rules (“ Foundational Rules ”) apply to every competition regardless of whether the Sponsor creates competition-specific rules. Any competition-specific rules provided by the Sponsor are in addition to these rules, and in the case of any conflict or inconsistency, these Foundational Rules control and nullify contrary competition-specific rules.\n###GENERAL COMPETITION RULES - BINDING AGREEMENT\n####1. ELIGIBILITY\na. To be eligible to enter the Competition, you must be:\n1. a registered account holder at Kaggle.com; \n2. the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Sponsor and appropriate parental/guardian consents have been obtained by Competition Sponsor); \n3. not a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, or North Korea; and\n4. not a person or representative of an entity under U.S. export controls or sanctions (see: [https://www.treasury.gov/resourcecenter/sanctions/Programs/Pages/Programs.aspx][1]).\n\nb. Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Host reserves the right to forego or award alternative Prizes where needed to comply with local laws. If a winner is located in a country where prizes cannot be awarded, then they are not eligible to receive a prize.\n\nc. If you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent or where you are an employee. If you are acting within the scope of your employment, or as an agent of another party, you warrant that such party or your employer has full knowledge of your actions and has consented thereto, including your potential receipt of a Prize. You further warrant that your actions do not violate your employer's or entity's policies and procedures. \n\nd. The Competition Sponsor reserves the right to verify eligibility and to adjudicate on any dispute at any time. If you provide any false information relating to the Competition concerning your identity, residency, mailing address, telephone number, email address, ownership of right, or information required for entering the Competition, you may be immediately disqualified from the Competition.\n\n####2. SPONSOR AND HOSTING PLATFORM\n\na. The Competition is sponsored by Competition Sponsor named above. The Competition is hosted on behalf of Competition Sponsor by Kaggle Inc. (\"Kaggle\"). Kaggle is an independent contractor of Competition Sponsor, and is not a party to this or any agreement between you and Competition Sponsor. You understand that Kaggle has no responsibility with respect to selecting the potential Competition winner(s) or awarding any Prizes. Kaggle will perform certain administrative functions relating to hosting the Competition, and you agree to abide by the provisions relating to Kaggle under these Rules. As a Kaggle.com account holder and user of the Kaggle competition platform, remember you have accepted and are subject to the Kaggle Terms of Service at [www.kaggle.com/terms][2] in addition to these Rules.\n\n####3. COMPETITION PERIOD\na. For the purposes of Prizes, the Competition will run from the Start Date and time to the Final Submission Deadline (such duration the “Competition Period”). The Competition Timeline is subject to change, and Competition Sponsor may introduce additional hurdle deadlines during the Competition Period. Any updated or additional deadlines will be publicized on the Competition Website. It is your responsibility to check the Competition Website regularly to stay informed of any deadline changes. YOU ARE RESPONSIBLE FOR DETERMINING THE CORRESPONDING TIME ZONE IN YOUR LOCATION.\n\n####4. COMPETITION ENTRY\na. NO PURCHASE NECESSARY TO ENTER OR WIN. To enter the Competition, you must register on the Competition Website prior to the Entry Deadline, and follow the instructions for developing and entering your Submission through the Competition Website. Your Submissions must be made in the manner and format, and in compliance with all other requirements, stated on the Competition Website (the \"Requirements\"). Submissions must be received before any Submission deadlines stated on the Competition Website. Submissions not received by the stated deadlines will not be eligible to receive a Prize.\nb. Submissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.\nc. If the Competition is a multi-stage competition with temporally separate training and/or test data, one or more valid Submissions may be required during each Competition stage in the manner described on the Competition Website in order for the Submissions to be Prize eligible.\nd. Submissions are void if they are in whole or part illegible, incomplete, damaged, altered, counterfeit, obtained through fraud, or late. Competition Sponsor reserves the right to disqualify any entrant who does not follow these Rules, including making a Submission that does not meet the Requirements. \n\n####5. INDIVIDUALS AND TEAMS\na. Individual Account. You may make Submissions only under one, unique Kaggle.com account. You will be disqualified if you make Submissions through more than one Kaggle account, or attempt to falsify an account to act as your proxy. You may submit up to the maximum number of Submissions per day as specified on the Competition Website. \nb. Teams. If permitted under the Competition Website guidelines, multiple individuals may collaborate as a Team; however, you may join or form only one Team. Each Team member must be a single individual with a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message sent to your Kaggle account. Team membership may not exceed the Maximum Team Size stated on the Competition Website.\nc. Team Merger. Teams may request to merge via the Competition Website. Team mergers may be allowed provided that: (i) the combined Team does not exceed the Maximum Team Size; (ii) the number of Submissions made by the merging Teams does not exceed the number of Submissions permissible for one Team at the date of the merger request; (iii) the merger is completed before the earlier of: any merger deadline or the Competition deadline; and (iv) the proposed combined Team otherwise meets all the requirements of these Rules. \nd. Private Sharing. No private sharing outside of Teams. Privately sharing code or data outside of Teams is not permitted. It's okay to share code if made available to all Participants on the forums.\n\n####6. SUBMISSION CODE REQUIREMENTS\na. Private Code Sharing. Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.\nb. Public Code Sharing. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or notebooks associated specifically with the Competition for the benefit of all competitors. By so sharing, you are deemed to have licensed the shared code under an Open Source Initiative-approved license (see [www.opensource.org][3]) that in no event limits commercial use of such Competition Code or model containing or depending on such Competition Code.\nc. Use of Open Source. Unless otherwise stated in the Specific Competition Rules above, if open source code is used in the model to generate the Submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see [www.opensource.org][4]) that in no event limits commercial use of such code or model containing or depending on such code.\n\n####7. DETERMINING WINNERS\na. Each Submission will be scored and ranked by the evaluation metric stated on the Competition Website. During the Competition Period, the current ranking will be visible on the Competition Website's Public Leaderboard. The potential winner(s) are determined solely by the leaderboard ranking on the Private Leaderboard, subject to compliance with these Rules. The Public Leaderboard will be based on the public test set and the Private Leaderboard will be based on the private test set.\nb. In the event of a tie, the Submission that was entered first to the Competition will be the winner. In the event a potential winner is disqualified for any reason, the Submission that received the next highest score rank will be chosen as the potential winner.\n\n####8. NOTIFICATION OF WINNERS & DISQUALIFICATION\na. The potential winner(s) will be notified by email. \nb. If a potential winner (i) does not respond to the notification attempt within one (1) week from the first notification attempt or (ii) notifies Kaggle within one week after the Final Submission Deadline that the potential winner does not want to be nominated as a winner or does not want to receive a Prize, then, in each case (i) and (ii) such potential winner will not receive any Prize, and an alternate potential winner will be selected from among all eligible entries received based on the Competition’s judging criteria.\nc. In case (i) and (ii) above Kaggle may disqualify the Participant. However, in case (ii) above, if requested by Kaggle, such potential winner may provide code and documentation to verify the Participant’s compliance with these Rules. If the potential winner provides code and documentation to the satisfaction of Kaggle, the Participant will not be disqualified pursuant to this paragraph.\nd. Competition Sponsor reserves the right to disqualify any Participant from the Competition if the Competition Sponsor reasonably believes that the Participant has attempted to undermine the legitimate operation of the Competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other Participants, Competition Sponsor or Kaggle.\ne. A disqualified Participant may be removed from the Competition leaderboard, at Kaggle's sole discretion. If a Participant is removed from the Competition Leaderboard, additional winning features associated with the Kaggle competition platform, for example Kaggle points or medals, may also not be awarded.\nf. The final leaderboard list will be publicly displayed at Kaggle.com. Determinations of Competition Sponsor are final and binding.\n\n####9. PRIZES\na. Prize(s) are as described on the Competition Website and are only available for winning during the time period described on the Competition Website. The odds of winning any Prize depends on the number of eligible Submissions received during the Competition Period and the skill of the Participants. \nb. All Prizes are subject to Competition Sponsor's review and verification of the Participant’s eligibility and compliance with these Rules, and the compliance of the winning Submissions with the Submissions Requirements. In the event that the Submission demonstrates non-compliance with these Competition Rules, Competition Sponsor may at its discretion take either of the following actions: (i) disqualify the Submission(s); or (ii) require the potential winner to remediate within one week after notice all issues identified in the Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).\nc. A potential winner may decline to be nominated as a Competition winner in accordance with Section 3.8.\nd. Potential winners must return all required Prize acceptance documents within two (2) weeks following notification of such required documents, or such potential winner will be deemed to have forfeited the prize and another potential winner will be selected. Prize(s) will be awarded within approximately thirty (30) days after receipt by Competition Sponsor or Kaggle of the required Prize acceptance documents. Transfer or assignment of a Prize is not allowed. \ne. You are not eligible to receive any Prize if you do not meet the Eligibility requirements in Section 2.7 and Section 3.1 above.\nf. If a Team wins a monetary Prize, the Prize money will be allocated in even shares between the eligible Team members, unless the Team unanimously opts for a different Prize split and notifies Kaggle before Prizes are issued.\n\n####10. TAXES\na. ALL TAXES IMPOSED ON PRIZES ARE THE SOLE RESPONSIBILITY OF THE WINNERS. Payments to potential winners are subject to the express requirement that they submit all documentation requested by Competition Sponsor or Kaggle for compliance with applicable state, federal, local and foreign (including provincial) tax reporting and withholding requirements. Prizes will be net of any taxes that Competition Sponsor is required by law to withhold. If a potential winner fails to provide any required documentation or comply with applicable laws, the Prize may be forfeited and Competition Sponsor may select an alternative potential winner. Any winners who are U.S. residents will receive an IRS Form-1099 in the amount of their Prize.\n\n####11. GENERAL CONDITIONS\na. All federal, state, provincial and local laws and regulations apply.\n\n####12. PUBLICITY\na. You agree that Competition Sponsor, Kaggle and its affiliates may use your name and likeness for advertising and promotional purposes without additional compensation, unless prohibited by law.\n\n####13. PRIVACY\na. You acknowledge and agree that Competition Sponsor and Kaggle may collect, store, share and otherwise use personally identifiable information provided by you during the Kaggle account registration process and the Competition, including but not limited to, name, mailing address, phone number, and email address (“Personal Information”). Kaggle acts as an independent controller with regard to its collection, storage, sharing, and other use of this Personal Information, and will use this Personal Information in accordance with its Privacy Policy <[www.kaggle.com/privacy][6]>, including for administering the Competition. As a Kaggle.com account holder, you have the right to request access to, review, rectification, portability or deletion of any personal data held by Kaggle about you by logging into your account and/or contacting Kaggle Support at <[www.kaggle.com/contact][7]>.\nb. As part of Competition Sponsor performing this contract between you and the Competition Sponsor, Kaggle will transfer your Personal Information to Competition Sponsor, which acts as an independent controller with regard to this Personal Information. As a controller of such Personal Information, Competition Sponsor agrees to comply with all U.S. and foreign data protection obligations with regard to your Personal Information. Kaggle will transfer your Personal Information to Competition Sponsor in the country specified in the Competition Sponsor Address listed above, which may be a country outside the country of your residence. Such country may not have privacy laws and regulations similar to those of the country of your residence.\n\n####14. WARRANTY, INDEMNITY AND RELEASE \na. You warrant that your Submission is your own original work and, as such, you are the sole and exclusive owner and rights holder of the Submission, and you have the right to make the Submission and grant all required licenses. You agree not to make any Submission that: (i) infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; or (ii) otherwise violates any applicable U.S. or foreign state or federal law.\nb. To the maximum extent permitted by law, you indemnify and agree to keep indemnified Competition Entities at all times from and against any liability, claims, demands, losses, damages, costs and expenses resulting from any of your acts, defaults or omissions and/or a breach of any warranty set forth herein. To the maximum extent permitted by law, you agree to defend, indemnify and hold harmless the Competition Entities from and against any and all claims, actions, suits or proceedings, as well as any and all losses, liabilities, damages, costs and expenses (including reasonable attorneys fees) arising out of or accruing from: (a) your Submission or other material uploaded or otherwise provided by you that infringes any third party proprietary rights, intellectual property rights, industrial property rights, personal or moral rights or any other rights, including without limitation, copyright, trademark, patent, trade secret, privacy, publicity or confidentiality obligations, or defames any person; (b) any misrepresentation made by you in connection with the Competition; (c) any non-compliance by you with these Rules or any applicable U.S. or foreign state or federal law; (d) claims brought by persons or entities other than the parties to these Rules arising from or related to your involvement with the Competition; and (e) your acceptance, possession, misuse or use of any Prize, or your participation in the Competition and any Competition-related activity.\nc. You hereby release Competition Entities from any liability associated with: (a) any malfunction or other problem with the Competition Website; (b) any error in the collection, processing, or retention of any Submission; or (c) any typographical or other error in the printing, offering or announcement of any Prize or winners.\n\n####15. INTERNET\na. Competition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a Participant’s ability to participate.\n\n####16. RIGHT TO CANCEL, MODIFY OR DISQUALIFY\na. If for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Sponsor reserves the right to cancel, terminate, modify or suspend the Competition. Competition Sponsor further reserves the right to disqualify any Participant who tampers with the submission process or any other part of the Competition or Competition Website. Any attempt by a Participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Sponsor and Kaggle each reserves the right to seek damages from any such Participant to the fullest extent of the applicable law.\n\n####17. NOT AN OFFER OR CONTRACT OF EMPLOYMENT\na. Under no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Sponsor or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Sponsor or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.\n\n####18. DEFINITIONS\na. \"Competition Data\" are the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to Participants. \nb. An “Entry” is when a Participant has joined, signed up, or accepted the rules of a competition. Entry is required to make a Submission to a competition.\nc. A “Final Submission” is the Submission selected by the user, or automatically selected by Kaggle in the event not selected by the user, that is/are used for final placement on the competition leaderboard.\nd. A “Participant” or “Participant User” is an individual who participates in a competition by entering the competition and making a Submission.\ne. The “Private Leaderboard” is a ranked display of Participants’ Submission scores against the private test set. The Private Leaderboard determines the final standing in the competition.\nf. The “Public Leaderboard” is a ranked display of Participants’ Submission scores against a representative sample of the test data. This leaderboard is visible throughout the competition.\ng. A “Sponsor” is responsible for hosting the competition, which includes but is not limited to providing the data for the competition, determining winners, and enforcing competition rules.\nh. A “Submission” is anything provided by the Participant to the Sponsor to be evaluated for competition purposes and determine leaderboard position. A Submission may be made as a model, notebook, prediction file, or other format as determined by the Sponsor.\ni. A “Team” is one or more Participants participating together in a Kaggle competition, by officially merging together as a Team within the competition platform.\n\n [1]: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx\n [2]: http://www.kaggle.com/terms\n [3]: http://www.opensource.org\n [4]: http://www.opensource.org\n [5]: https://www.kaggle.com/WinningModelDocumentationGuidelines\n [6]: http://www.kaggle.com/privacy\n [7]: http://www.kaggle.com/contact\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }