{
  "fetched_at": "2026-10-24T19:00:28Z",
  "source_url": "https://www.kaggle.com/competitions/ai-hackathon-foobar-60-q2/data",
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      "name": "data-description",
      "content": "What files do I need? \n\nAns: stock_train.csv and stock_test.csv\n\nUse stock_train.csv to study and train your data\n\nUse stock_test.csv to test your data  \n\n----------------------------------------------------------------------------------------------------------------------------------\nWhat should I expect the data format to be?  \nAns: The data is in CSV format\n\n----------------------------------------------------------------------------------------------------------------------------------\nWhat should I do once the training and testing is done?\n\nAns: Find the accuracy of your model using R2 score metric\n\n## Files\n\n*   **stock_train.csv** - the training set\n*   **stock_test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n● ID - The id value of that row\n● open - the opening value of the stock\n● close - the closing value of the stock\n● high - the highest value the stock climbed to\n● low - the lowest value the stock dropped to",
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    {
      "name": "Description",
      "content": "A stock market, equity market, or share market is the aggregation of buyers and sellers of stocks (also called shares), which represent ownership claims on businesses; these may include securities listed on a public stock exchange, as well as stock that is only traded privately, such as shares of private companies which are sold to investors through equity crowdfunding platforms. Investment is usually made with an investment strategy in mind.\n\nUse *Neural networks* to predict the future prices of the stock.",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [R2 score](https://en.wikipedia.org/wiki/Coefficient_of_determination)\n\nPrecision is the ratio of true positives \\(\\text{tp}\\) to all predicted positives \\(\\text{tp} + \\text{fp}\\). Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\nIn statistics, the coefficient of determination, denoted R2 or r2 and pronounced \"R squared\", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).\n\nIt is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.[1][2][3]\n\nThere are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. When only an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the observed outcomes and the observed predictor values.[4] If additional regressors are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination normally ranges from 0 to 1.\n\n## Submission Format\n\nFor every row in the given dataset, the submission file should contain two columns: `ID` and `close`. The `ID` column should be numbered from 0 to 261 and the `close` column should contain your prediction for the closing price.\nThe file should contain a header and have the following format:\n```\nID,close\n0,153.07007\n1,149.57\n....\n....\n261,169.119995\n```",
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    {
      "name": "rules",
      "content": "# Rules\n\n- Team of 2 or 3\n- References allowed\n- No cheating",
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