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  "source_url": "https://www.kaggle.com/competitions/1056lab-credit-card-fraud-detection/data",
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      "name": "rules",
      "content": " - Don't cheat!\n - Help each other!\n - Have fun!",
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      "content": "#File descriptions\n* **train.csv** - the training set, which includes quality.\n* **test.csv** - the test set, which does *not* include quality.\n* **sampleSubmission.csv**  - a sample submission file in the correct format.\n\n# Data Fields\nThere are id and 12 characteristics of wine: color, pH, alcohol, etc.\n\n* `ID` transaction-id\n* `Time` the seconds elapsed between each transaction and the first transaction in the dataset\n* `V1`, `V2`, ..., `V28` the principal components obtained with PCA, the only features which have not been transformed with PCA\n* `Amount` the transaction Amount\n* `Class` it takes value 1 in case of fraud and 0 otherwise",
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      "name": "Description",
      "content": "# Context\nFraud detection in financial transactions is one of the most important problems in financial companies.\nWe have already had a competition on fraud detection at [the 5th 1056Lab Data Analytics Competition](https://www.kaggle.com/c/1056lab-flaud-detect) which is the first our competition at Kaggle in 2018.\n\n# Content\nThe original dataset is in [Kaggle Datasets](https://www.kaggle.com/mlg-ulb/creditcardfraud).\n\nThis data is about fraud detection in credit card transactions. The data was made by credit cards in September 2013 by European cardholders.  The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n\nIt contains only numerical input variables which are the result of a PCA transformation. The original features and more background information about the data is not provided.\n\nThe dataset contains 284,807 instances, 492 instances are fraudulent, the remaining 284,315 instances are genuine.\n\n\n# Training Dat and Test Data\nThe training data set is the former 198,365 (70%) instances, 383 represents fraud transactions, 197,982 transactions are genuine.\n\nThe test data is the later 86,442 (30%) transactions.\n\n\n# License\nThe original data is licensed by [Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/dbcl/1.0/).\n\n\n# Notice\nThis is a private competition in [1056Lab](https://1056lab.org/), a data mining laboratory at [Chubu University](https://www3.chubu.ac.jp/main/english/).\n\n#Reference\n- [Machine Learning Group](http://mlg.ulb.ac.be) - ULB: [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud), Kaggle Datasets\n- Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. [Calibrating Probability with Undersampling for Unbalanced Classification](https://www.researchgate.net/publication/283349138_Calibrating_Probability_with_Undersampling_for_Unbalanced_Classification). In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n- Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. [Learned lessons in credit card fraud detection from a practitioner perspective](https://www.researchgate.net/publication/260837261_Learned_lessons_in_credit_card_fraud_detection_from_a_practitioner_perspective), Expert systems with applications,41,10,4915-4928,2014, Pergamon\n- Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. [Credit card fraud detection: a realistic modeling and a novel learning strategy](https://www.researchgate.net/publication/319867396_Credit_Card_Fraud_Detection_A_Realistic_Modeling_and_a_Novel_Learning_Strategy), IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n- Dal Pozzolo, Andrea. [Adaptive Machine learning for credit card fraud detection](http://di.ulb.ac.be/map/adalpozz/pdf/Dalpozzolo2015PhD.pdf), ULB MLG PhD thesis (supervised by G. Bontempi)\n- Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. [Scarff: a scalable framework for streaming credit card fraud detection with Spark](https://www.researchgate.net/publication/319616537_SCARFF_a_Scalable_Framework_for_Streaming_Credit_Card_Fraud_Detection_with_Spark), Information fusion,41, 182-194,2018,Elsevier\n- Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. [Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization](https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection), International Journal of Data Science and Analytics, 5,4,285-300,2018, Springer International Publishing\n- Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi. [Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection](https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection), INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019\n- Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi. [Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection](https://www.researchgate.net/publication/333143698_Combining_Unsupervised_and_Supervised_Learning_in_Credit_Card_Fraud_Detection), Information Sciences, 2019",
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    {
      "name": "Evaluation",
      "content": "#Area Under the ROC Curve (AUC)\nThe evaluation metric for this competition is [AUC (Area Under the Curve) ROC (Receiver Operating Characteristics) curve](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc).\n\n#Submission Format\nSubmission files should contain two columns: `ID` and `Class`.\n\nThe file should contain a header and have the following format:\n<blockquote>\nID,Class<br>\n198366,1<br>\n198367,1<br>\n198368,1<br>\netc.\n</blockquote>",
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    {
      "name": "Timeline",
      "content": "* Start Date: December 6, 2019\n* Submission Deadline: **January 6, 2020, 11:59, JST**\n* Presentation: January 6, 2020, 17:05, JST and January 7, 2020, 15:20, JST",
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    {
      "name": "Prizes",
      "content": "* **1st place** - **Gold medal** and **10 points**\n* **2nd place** - **Silver medal** and **8 points**\n* **3rd place** - **Bronze medal** and **6 points**\n* **4th place** - **4 points**\n* **5th place** - **2 points**\n\nIf the score is within 5%, the same points minus 1 are given.\nIf you could not submit until the deadline, 4 points are deducted.\nIf you could not make your final score Notebook public after the deadline, you are disqualified and deducted 2 points.",
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    {
      "name": "Notebook Requirements",
      "content": "You **have to use** Kaggle Notebook and **make your final score Notebook public** after the deadline.",
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