{
  "fetched_at": "2026-10-20T00:26:36Z",
  "source_url": "https://www.kaggle.com/competitions/become-a-kaggle-master-hw2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "We provide four files\n\n## Files\n\n*   **train.csv** - the training set. It contains features (columns 1 to 22 corresponding to the return of the 22 factors) + the expected return (column called FundReturn)\n*   **test.csv** - the test set containing only the returns of the 22 factors\n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **column_description.csv** - a file giving the description of each column",
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    {
      "name": "Description",
      "content": "As there has been a data leak in the previous competition, please go to \nhttps://www.kaggle.com/competitions/become-a-kaggle-master-iasd-hw2/\n",
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    {
      "name": "Evaluation",
      "content": "As there has been a data leak in the previous competition, please go to \nhttps://www.kaggle.com/competitions/become-a-kaggle-master-iasd-hw2/",
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    {
      "name": "rules",
      "content": "You cannot use external data.",
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    {
      "name": "abstract",
      "content": "# Context:\nThis is homework 2 of the course 'Become a Kaggle Master'.\nThis course is part of the <a href=\"https://dauphine.psl.eu/en/training/masters-degrees/computer-science/m2-artificial-intelligence-systems-data\">IASD Master 2 in Machine Learning, Artificial Intelligence, and Data Science</a> program, jointly run by <a href=\"https://dauphine.psl.eu/en/\">Université Paris Dauphine</a>, <a href=\"https://www.ens.psl.eu/en\"> ENS</a>  and <a href=\"https://mines-paristech.eu/\"> Mines ParisTech</a>.\n\n#Goal:\nThis latest Kaggle competition aims at predicting a crucial financial time series! The objective is to leverage machine learning techniques to accurately forecast a financial variable, essential for making precise trading predictions. Although the dataset is intentionally anonymized, it encompasses approximately 133 features covering diverse aspects of financial markets. Your task is to develop a robust model capable of predicting the target variable based on the provided features.\n\n#Who Can Participate:\nThis competition is open to all students currently enrolled in our course. It serves as an invaluable practical exercise in machine learning, particularly focusing on time series prediction. Whether you're a novice or an experienced data scientist, this challenge offers a unique opportunity to sharpen your skills and apply theoretical knowledge to real-world scenarios.\n\n#Key Considerations:\nGiven the complexity of financial markets, participants are strongly encouraged to guard against overfitting, as it holds significant weight in the final ranking. The ability to generalize your model's predictions beyond the training data is paramount for success in this competition. As you fine-tune your algorithms, keep in mind the overarching goal of producing robust and reliable forecasts that can withstand the dynamic nature of financial markets.",
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