{ "fetched_at": "2026-10-19T10:52:12Z", "source_url": "https://www.kaggle.com/competitions/fi-2020-q2-kaggle-competition/data", "pages": [ { "name": "rules", "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "

Data: The train dataset contains transactions made by credit cards over two days in September 2013 by european cardholders. The dataset is highly unbalanced, the positive class (frauds) account for roughly 0.17% of all transactions.

\n\n

The data contains only numerical input variables which are the result of a PCA transformation. Due to confidentiality issues, we cannot provide the original features and background information about the data (this is common in many neural network problems). Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

\n\n

The test dataset is the data that you will score using your model. Submit your predictions for the test dataset for evaluation.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "

The Model Domain is hosting a Model Competition/Training using Kaggle. You are asked to build a neural network model to predict fraudulent credit card transactions. The winner will be selected based on the model’s performance on an out-of-sample test dataset.

\n\n

Objective: The goal of this competition is to gain additional practice building neural networks.

\n\n

Scoring Metric: Given the class imbalance ratio, submissions will be evaluated using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

\n\n

Deadline: Entries can be submitted at any time, and you can track your progress on the public leaderboard. We encourage you to make multiple submissions to try to improve your score. Final scoring using the private leaderboard will be conducted on June 30, 2020.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "

The evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:

\n

\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]

\n

The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.

\n

Submission Format

\n

For every record in the test dataset, submission files should contain two columns: Id and Predicted.

\n

The file should contain a header and have the following format:

\n
Id,Predicted
0,0
1,0
2,0
3,1
etc.
",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}