{
  "fetched_at": "2026-10-19T19:39:13Z",
  "source_url": "https://www.kaggle.com/competitions/aia-dnn-classification-pokemongo-bt101/data",
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    {
      "name": "data-description",
      "content": "詳見 Overview 說明\n\n## Brief file descriptions\n- train.csv - the training set\n- test.csv - the test set\n- sampleSubmission.csv - a sample submission file in the correct format",
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      "name": "Description",
      "content": "大家一起練習用 DNN 分類寶可夢吧！\n題目類型 - 分類問題：\nclass 總共有 6 種不同的寶可夢，分別為 class 0, 1, 2, 3, 4 or 5 ，其中\nclass 0 = Sandshrew\nNo.27\nclass 1 = Tentacool\nNo.72\nclass 2 = Slowpoke\nNo.79\nclass 3 = Magnemite\nNo.81\nclass 4 = Voltorb\nNo.100\nclass 5 = Exeggcute\nNo.102\n\n資料包括之欄位如下:\nappearedTimeOfDay - time of the day of a sighting (night, evening, afternoon, morning)\n\nappearedHour/appearedMinute - local hour/minute of a sighting (numeric)\n\nterrainType - terrain where pokemon appeared described with help of GLCF Modis Land Cover (numeric)\n\ncloseToWater - did pokemon appear close (100m or less) to water (Boolean, same source as above)\n\ncity - the city of a sighting (nominal)\n\ncontinent (not always parsed right) - the continent of a sighting (nominal)\n\nweather - weather type during a sighting (Foggy Clear, PartlyCloudy, MostlyCloudy, Overcast, *Rain, BreezyandOvercast, LightRain, Drizzle, BreezyandPartlyCloudy, HeavyRain, ** BreezyandMostlyCloudy, Breezy, Windy, WindyandFoggy, Humid, Dry, WindyandPartlyCloudy, DryandMostlyCloudy, DryandPartlyCloudy, DrizzleandBreezy, LightRainandBreezy, HumidandPartlyCloudy, HumidandOvercast, RainandWindy)\n\ntemperature - temperature in celsius at the location of a sighting (numeric)\n\nwindSpeed - speed of the wind in km/h at the location of a sighting (numeric)\n\npressure - atmospheric pressure in bar at the location of a sighting (numeric)\n\nweatherIcon - a compact representation of the weather at the location of a sighting (fog, clear-night, partly-cloudy-night, partly-cloudy-day, cloudy, clear-day, rain, wind)\n\npopulation density - what is the population density per square km of a sighting (numeric, Source)\n\nurban-rural - how urban is location where pokemon appeared (Boolean, built on Population density, =200 and =400 and 800 for urban)\n\ngymDistanceKm, pokestopDistanceKm - how far is the nearest gym/pokestop in km from a sighting? (numeric, extracted from this dataset)\n\ngymIn100m-pokestopIn5000m - is there a gym/pokestop in 100/200/etc meters? (Boolean)\n\ncooc 1-cooc 151 - co-occurrence with any other pokemon (pokemon ids range between 1 and 151) within 100m distance and within the last 24 hours (Boolean) List of Pokémon by National Pokédex number\n\n* class - says which pokemon it is, to be predicted.\n問題：\n預測每筆 data 的寶可夢類別 (0, 1, 2, 3, 4 or 5)\n\n練習方向：\nData preprocessing and feature engineering\nDNN model construction and hyper-parameter tuning\nMetrics: Accuracy\n\n通過條件： 超越 Public baseline\n上傳次數：\n一天可以上傳20次，上傳時，Team Name請以您的學號命名，如BT101999",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is Accuracy-Score.\n\n\n<p>The file should contain a header and have the following format:</p>\n<pre>ID,class<br>MTA5MTEwOTYxMzM0NzA2NDEzNzM=,1<br></pre>",
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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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