{
  "fetched_at": "2026-10-20T05:36:21Z",
  "source_url": "https://www.kaggle.com/competitions/grad-bunker-learning-hub-data-science-competition/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This dataset is from a ride sharing app data. It has two parts, train data (3559, 13) and test data (1526, 12). In this competition, you will be dealing with a regression problem for the given dataset.\n\n**Rules:**\n•\tJoin the competition when it opens\n•\tGet the dataset and submission templates\n•\tClean the dataset, pre-process the dataset and perform EDA where it is necessary\n•\tProvide data visualization from EDA and put note on each of them\n•\tTrain your models on train set and predict on test set\n•\tYou can use multiple regression models if necessary\n•\tSelect the final model and provide performance metrics.\n•\tSubmit the submission file in Kaggle competition page\n\n\n**Files**\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **metaData.csv** - supplemental information about the data\n\n",
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    {
      "name": "Description",
      "content": "**Welcome to Grad Bunker Learning Hub Data Science Competition. **\n\nThis dataset is from ride-sharing app data. It has two parts, train data (3559, 13) and test data (1526, 12). In this competition, you will be dealing with a regression problem for the given dataset.\n\n\n**Prize: **\n1. Winner will get full free scholarship on our upcoming Data Science Course, which has a value of around 15000 Taka\n2. First Runner-up will get 90% scholarship on our upcoming Data Science Course\n3. Second Runner-up will get 80% scholarship on our upcoming Data Science Course\n\nMay the force be with you.\n\n**Team GB**\n",
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    },
    {
      "name": "Evaluation",
      "content": "**We will evaluate your model by the following criteria:**\n\n1. How much explanation you can make from EDA\n2. Your data visualization techniques and explanations\n3. Your model accuracy and performance metrics (r2 score, ROC curve, MSE, RMSE etc)",
      "isPublished": false,
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      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "**Rules:**\n\n•\tJoin the competition when it opens\n•\tGet the dataset and submission templates\n•\tClean the dataset, pre-process the dataset and perform EDA where it is necessary\n•\tProvide data visualization from EDA and put note on each of them\n•\tTrain your models on train set and predict on test set\n•\tYou can use multiple regression models if necessary\n•\tSelect the final model and provide performance metrics.\n•\tSubmit the submission file in Kaggle competition page\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
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    }
  ]
}