{ "fetched_at": "2026-10-19T18:55:46Z", "source_url": "https://www.kaggle.com/competitions/datms/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": "

The data below is a subset of the data provided during the Lending Club Competition. \n\n

For convenience, you have been provided both a training and a testing dataset.

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The testing dataset does not include the target variable. The columns names should be self explanatory. The purpose of this competition is to accurately predict if a loan will default.

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Please review the sample submission file for the format in which your result submissions need to be in. You will be graded on the accuracy of your predictions. .

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This competition is for the 9/23 cohort of the Morgan Stanley Part Time Data Science Course

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Purpose

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You will be using a dataset derived from the Lending Club competition and trying to determine if you can accurately predict if a loan is going to default or not:

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Use any modeling, ensembling , feature engineering and selection techniques to try and maximize your prediction accuracy
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The target is the Account Loan Status column
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Work individually or in teams of 2/3, you can make a maximum of 5 submissions a day. Make sure to include the names of the participants in your time. Check the leaderboard to see who is winning
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Note: Make sure that the submission file format is the same as the example provided. Use the Id column as the index

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Note: You can make a total of 5 submissions a day

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The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:

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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:

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

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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.

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Submission Format

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For every author in the dataset, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).

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

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AuthorId,DuplicateAuthorIds
1,1
8,8
9,9 10
10,10 9
etc.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }