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  "fetched_at": "2026-10-25T00:38:40Z",
  "source_url": "https://www.kaggle.com/competitions/big-data-adelaide-bootcamp/data",
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      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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      "content": "# Overview\nThe data has been split into two groups:\n\n- training set (audit_train.csv)\n- test set (audit_test.csv)\n\nThe training set should be used to build your machine learning models. For the training set, we provide the target outcome (also known as the “ground truth”) for each individual. Your model will be based on “features” like individual's occupation or income. You can also use feature engineering to create new features.\n\nThe test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each individual. \n\n## Data Dictionary\n\nA data frame. In line with data mining terminology we refer to the rows of the data frame (or the observations) as entities. The columns are refered to as variables. The entities represent people in this case. We describe the variables here: \n\n    ID         - This is a unique identifier for each person.\n    Age        - The age.\n    Employment - The type of employment.\n    Education  - The highest level of education.\n    Marital    - Current marital status.\n    Occupation - The type of occupation.\n    Income     - The amount of income declared.\n    Gender     - The persons gender.\n    Deductions - Total amount of expenses that a person claims in their financial statement.\n    Hours      - The average hours worked on a weekly basis.\n    Accounts   - The main country in which the person has most of their money banked.\n    Adjustment - This variable records the monetary amount of any adjustment to the person's financial claims as a result of a productive audit.\n    Target     - The target variable for modelling (generally for classification modelling).\n\n",
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      "name": "Description",
      "content": "\n## Description\nThe audit dataset is an artificially constructed dataset that has some of the characteristics of a true financial audit dataset for modelling productive and non-productive audits of a person's financial statement. \n\n## Expected Outcomes\nThe audit dataset is used to illustrate binary classification. The target variable is identified as **Target**.\n\n- A **productive audit** is one which identifies errors or inaccuracies in the information provided by a client. \n- A **non-productive audit** is usually an audit which found all supplied information to be in order.\n\n> Your task is to predict if an individual has provided inaccuracies in their information\n\nThe dataset itself is derived from publicly available data (which has nothing to do with audits).",
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      "name": "Evaluation",
      "content": "### Goal\nIt is your job to predict if individuals financial affairs are fraudulent or not.\nFor each Id in the test set, you must predict a 0 or 1 value for the Target variable.\n\n### Metric\nYour score is the percentage of individuals you correctly predict. This is known simply as \"accuracy”.\n\n### Submission File Format\nYou should submit a csv file with exactly 9,676 entries plus a header row. Your submission will show an error if you have extra columns (beyond Id and Target) or rows.\n\nThe file should have exactly 2 columns:\n- Id (sorted in any order)\n- Target (contains your binary predictions: 1 for productive-audit, 0 for non-productive.)\n\n### Sample Submission\n\n    \"ID\",\"Target\"\n    1000466,0\n    1001107,0\n    1001779,0\n    1002020,1\n    1002080,1\n\n",
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