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  "fetched_at": "2026-10-19T12:29:58Z",
  "source_url": "https://www.kaggle.com/competitions/2025-2-data-solution-p-02/data",
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      "content": "## Dataset Description\nIn this competition, you will predict the probability that an auto insurance policy holder files a claim.\n\nIn the train and test data, features that belong to similar groupings are tagged as such in the feature names (e.g., ind, reg, car, calc). In addition, feature names include the postfix bin to indicate binary features and cat to indicate categorical features. Features without these designations are either continuous or ordinal. Values of -1 indicate that the feature was missing from the observation. The target columns signifies whether or not a claim was filed for that policy holder.\n\n## File descriptions\n- train.csv contains the training data, where each row corresponds to a policy holder, and the target columns signifies that a claim was filed.\n- test.csv contains the test data.\n- sample_submission.csv is submission file showing the correct format.",
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      "name": "Description",
      "content": "## Chapter 8 - [경진대회] 포르투 세구로 안전 운전자 예측 경진대회\n2025학년도 2학기 데이터사이언스학과\n데이터문제해결및실습1\n\n- 난이도 : ★★☆\n- 경진대회명 : 포르투 세구로 안전 운전자 예측 경진대회\n- 미션 : 포르투 세구로 보험사에서 제공한 고객 데이터를 활용해 운전자가 보험을 청구할 확률 예측\n- 문제 유형 : 이진분류\n- 평가지표 : 정규화된 지니계수\n- 데이터 크기 : 288.7MB\n- 참가팀 수 : 5,156팀\n- 제출 시 사용한 모델 : LightGBM와 XGBoost의 앙상블",
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      "name": "Evaluation",
      "content": "# Scoring Metric\nSubmissions are evaluated using the *Normalized Gini Coefficient.* \n\nDuring scoring, observations are sorted from the largest to the smallest predictions. Predictions are only used for ordering observations; therefore, the relative magnitude of the predictions are not used during scoring. The scoring algorithm then compares the cumulative proportion of positive class observations to a theoretical uniform proportion. \n\nThe Gini Coefficient ranges from approximately 0 for random guessing, to approximately 0.5 for a perfect score. The theoretical maximum for the discrete calculation is $$ (1 - frac_pos) / 2. $$\n\nThe Normalized Gini Coefficient adjusts the score by the theoretical maximum so that the maximum score is 1. The code to calculate Normalized Gini Coefficient in a number of different languages can be found in this forum thread. \n\n# Submission File \nFor each id in the test set, you must predict a probability of an insurance claim in the target column. The file should contain a header and have the following format: ``` id,target 0,0.1 1,0.9 2,1.0 etc. ```",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "abstract",
      "content": "## Chapter 8 - [경진대회] 포르투 세구로 안전 운전자 예측 경진대회\n2023학년도 2학기 데이터사이언스학과\n데이터문제해결및실습1",
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