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  "fetched_at": "2026-10-19T09:26:40Z",
  "source_url": "https://www.kaggle.com/competitions/anyas-gojo-revival-bondman/data",
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      "name": "data-description",
      "content": "**Data Description for Anya's Gojo Revival: Detective Bondman**\n\n**Files**\n- `trainData.csv`: The training dataset with 23 columns, including the 'id' and the 'is_fraud' target column.\n- `testData.csv`: The test dataset with 22 columns, missing the 'is_fraud' target column.\n- `sample_submission.csv`: A sample submission file in the correct format, including 'id' and predicted 'is_fraud' values.\n\n**Columns**\n- `id`: Unique identifier for each entry.\n- `trans_date_trans_time`: The date and time of the transaction.\n- `cc_num`: Credit card number.\n- `merchant`: Merchant receiving payment.\n- `category`: Area of merchant's business.\n- `amt`: Amount in American Dollars.\n- `first`: First name of the cardholder.\n- `last`: Last name of the cardholder.\n- `gender`: Gender of the cardholder (Male/Female).\n- `street`: Street of cardholder's residence.\n- `city`: City of cardholder's residence.\n- `state`: State of cardholder's residence.\n- `zip`: ZIP code of cardholder's residence.\n- `lat`: Latitude of the cardholder.\n- `long`: Longitude of the cardholder.\n- `city_pop`: Population of the city.\n- `job`: Occupation of the cardholder.\n- `dob`: Date of birth of the cardholder.\n- `trans_num`: Transaction ID.\n- `unix_time`: Unix time (time since 1970).\n- `merch_lat`: Latitude of the merchant.\n- `merch_long`: Longitude of the merchant.\n- `is_fraud`: Binary flag indicating whether the transaction is fraud (1) or not (0).\n\n**Data Format**\n- The datasets are provided in CSV format, with each row representing a data point, and columns containing the feature values.\n\n**Prediction Task**\n- Your task is to predict whether each transaction in `testData.csv` is fraudulent (1) or not fraudulent (0) based on patterns and relationships observed in `trainData.csv`.\n\n**Acknowledgments**\n- This dataset is fictional and purely for simulation purposes. Any resemblance to real-life cases is coincidental.\n\nParticipants should be able to understand the provided data, its format, the task at hand, and the role of each file after reading this data description.",
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      "name": "Evaluation",
      "content": "Submissions will be evaluated based on their Receiver Operating Characteristic Area Under the Curve ([ROC AUC](http://en.wikipedia.org/wiki/Receiver_operating_characteristic)) score. ROC AUC measures the model's ability to distinguish between genuine and fraudulent transactions.\n\n**Scoring:** A higher ROC AUC score indicates superior model performance in detecting fraud. The final leaderboard will rank participants according to their ROC AUC score, as well as their code quality, data preprocessing, visualization, and explanation of their approach.\n\n## Submission File\nFor each 'id' in the test set, you must predict whether the transaction is fraudulent (1) or not fraudulent (0). The file should contain a header and have the following format:\n\n    `id`,`is_fraud`\n    2,0\n    5,1\n    6,0\n    etc.\n\n**Good Luck!** We wish all participants the best of luck in the challenge. May the sharpest detectives help Anya in her quest to revive her cosmic companion, Gojo, by identifying fraudulent transactions.",
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      "name": "rules",
      "content": "Abide by the following rules throughout the competition:-\n\n1. This competition is part of Logical Rhythm 2k23. There are four Competitions in total, and this is the second one in the series.\n2. This is a team event with 1-3 members.\n3. All problem statements will be marked individually, and scores will be accumulated.\n4. Any form of plagiarism will not be tolerated.\n5. While showing the final submission, always use save and run all (commit) versions rather than quick save of the notebooks.\n6. The final leaderboard will rank participants according to their Kaggle Leaderboard Score (error metrics) and their code styles, data preprocessing, data visualization, and explanation of the reasoning behind their approach.\n7. Dont post your code publicly before the competition is over.\n\nHave fun!",
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    {
      "name": "abstract",
      "content": "Anya, still on her quest to revive Gojo, found herself engrossed in her favorite show, \"Bondman.\" While watching, she made a curious connection—the mysterious sheets from her earlier adventure held information about a YouTube channel. It was a revelation that seemed to have a cosmic link.\n\nWith her newfound knowledge, identifying the channel's owner was a breeze. All she had to do was pull her ace card—threatening Itaru Hashida of the Future Gadget Lab. Itaru, a wizard in hacking, obliged, and they soon uncovered the owner's identity. However, there was a twist in this cosmic tale.\n\nThe target, it seemed, was involved in numerous transactions, but something about them was awry. A sense of fishiness surrounded the financial dealings. To unravel the mystery and get one step closer to reviving Gojo, Anya needed to identify which of these transactions were fraudulent.\n\nJoin Anya in her new adventure as \"Detective Bondman.\" Help her detect fraudulent transactions and unlock the secrets hidden within, as the fate of the cosmos hangs in the balance.",
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    {
      "name": "Logical Rhythm 2k23",
      "content": "This is one of the four problems for Logical Rhythm 2k23. \n**Anya's Gojo Revival**\n1. [Pilot](https://www.kaggle.com/competitions/anyas-gojo-revival)\n2. [Detective Bondman](https://www.kaggle.com/competitions/anyas-gojo-revival-bondman)\n3. [The Aerospace Enigma](https://www.kaggle.com/competitions/anyas-gojo-revival-aerospace-enigma)\n4. [Space shop](https://www.kaggle.com/competitions/anyas-gojo-revival-space-shop)",
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