{
  "fetched_at": "2026-10-20T04:56:49Z",
  "source_url": "https://www.kaggle.com/competitions/data-science-and-machine-learning-ism-415-2021/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>The dataset contains these variables as explained below:</p>\n- CustomerId: System-generated unique customer's ID\n- Surname: Customer's Surname\n- CreditScore: System generated scores\n- Geography: Customer's Country\n- Gender\n- Age\n- Tenure: How long he/she has been a customer\n- Balance: Available Balance\n- NumOfProducts: Number of products bought\n- HasCrCard: Does customer has a credit card\n- IsActiveMember: States if the customer is active or not\n- EstimatedSalary: Customer's estimated salary",
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    {
      "name": "Description",
      "content": "<h1> ISM 415 InClass Machine Learning Competition</h1>\n<p><p>This Kaggle is a project from the course ISM 415: Special Topics at Pan Atlantic University in Nigeria. All students must create a Kaggle account and submit a solution.</p>\n\n<p><b>CASE STUDY: ZOC GROUP OF COMPANIES - CUSTOMER CHURN PREDICTION</b></p>\n<p> Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customers to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn.</p>\n<p>You have been called in by ZOC GROUP OF COMPANIES  to use your machine learning skills to study the pattern of customers churn (exit). With this insight, they can understand the important features among available features that can be used to predict the customers that are likely to churn out of their service in the future.</p>\n\n<p>The dataset contains these variables as explained below:</p>\n- CustomerId: System-generated unique customer's ID\n- Surname: Customer's Surname\n- CreditScore: System generated scores\n- Geography: Customer's Country\n- Gender\n- Age\n- Tenure: How long he/she has been a customer\n- Balance: Available Balance\n- NumOfProducts: Number of products bought\n- HasCrCard: Does customer has a credit card\n- IsActiveMember: States if the customer is active or not\n- EstimatedSalary: Customer's estimated salary",
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    {
      "name": "Evaluation",
      "content": "<p>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:</p>\n<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. 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:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>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.</p>\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, 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.&nbsp;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).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br>1,1<br></pre>",
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    },
    {
      "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!",
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      "hasIsPublished": false,
      "postTitle": "",
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}