{ "fetched_at": "2026-10-20T05:14:47Z", "source_url": "https://www.kaggle.com/competitions/dscmeetup4/data", "pages": [ { "name": "rules", "content": "This challenge will be guided by DSC rules which are summarized below:\n\n- Don't cheat!\n- Apply yourself!\n- Have fun!\n- One account per participant\n\nYou may submit as many entries as the platform allows within the competition time. (The Kaggle Platform allows up to 20 submissions within a 24 hour period but for this challenge, we’ve set it to 10 within a 24 hour period.)\n\n- Please make all submissions before the deadline.\n- You will be judged on your best-performing solution.\n- A team with a maximum of three (3) members is allowed.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "### Description of columns in the dataset:\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3998144%2Fd0865e71a448f9fe1d841cc0c0ffa19b%2Fkgrules.PNG?generation=1582050607808394&alt=media)\n\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "**Description of the challenge**\n\nRecently, there has been an increase in the number of building collapse in Lagos and major cities in Nigeria. Affinity Insurance Company offers a building insurance policy that protects buildings against damages that could be caused by a fire or vandalism, by a flood or storm.\n\nYou have been appointed as the Lead Data Analyst to build a predictive model to determine if a building will have an insurance claim during a certain period or not. You will have to predict the probability of having at least one claim over the insured period of the building.\n\nThe model will be based on the building characteristics. The target variable, Claim, is a:\n- 1 if the building has at least a claim over the insured period.\n- 0 if the building doesn’t have a claim over the insured period.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "

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 } ] }