{
  "fetched_at": "2026-10-19T16:44:28Z",
  "source_url": "https://www.kaggle.com/competitions/developers-day-2023-data-science-competition/data",
  "pages": [
    {
      "name": "data-description",
      "content": "You should download the train.csv file and perform EDA on it. You are required to predict Credit_Score column in the data.\n\nThe metric kept for ML score will be F1 accuracy.\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",
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    {
      "name": "Description",
      "content": "Hello  participants! Welcome to the data science competition of **Developer’s Day’23**, It's great to have you here. Get ready to showcase your skills. We hope you enjoy the challenge.\n**Best of luck for the competition!**\n\n",
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      "name": "Evaluation",
      "content": "In this task, you are given two files: a train dataset and a test dataset. Your goal is to train your model on the train dataset and use it to generate predictions for the **Credit_Score** column in the test dataset. You are expected to submit a notebook with your code and the results of your predictions.\nTo accomplish this, you will need to perform **exploratory data analysis (EDA)** on the train dataset to gain insights into the underlying patterns and relationships in the data. This will help you select appropriate features and preprocessing techniques for your model. Once you have trained your model on the train dataset, you can use it to predict the **Credit_Score** for the test dataset.\nEach submission will be scored on the basis of **EDA** and **prediction**.\nRemember to evaluate your model's performance on the test dataset and report your results accurately. \n\n**Good luck with the task!**\n\n",
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    {
      "name": "rules",
      "content": "\n - Have fun!",
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