{
  "fetched_at": "2026-10-20T10:10:22Z",
  "source_url": "https://www.kaggle.com/competitions/creditcardcompetitioncs6501/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!",
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    {
      "name": "data-description",
      "content": "Files\n\n1.  train.csv\n2. test.csv\n3. Sample_submission.csv\n\nEach row in the train/test file is a different person.\n\nColumns are:\nMale\tAge\tDebt\tMarried\tBankCustomer\tEducationLevel\tEthnicity\tYearsEmployed\tPriorDefault\tEmployed\tCreditScore\tCitizen\tIncome\n\n\n- **Id**: id of the applicant;\n- **Male**: If applicant is male or not\n- **Age**: Age of the applicant;\n- **Married**\n- **BankCustomer**\n-**EducationLevel**\n-**Ethnicity**\n-**YearsEmployed**\n-**PriorDefault**\n-**Employed**\n-**CreditScore**\n-**Citizen**\n-**Income**\n\n- **Category**: value=1 means approved, value=0 means denied\n\nThe **Category** column is only present in the train.csv file. \nYou must output this column for the test set.\n\nWe have also uploaded a sample submission file example. \n\n",
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    {
      "name": "Description",
      "content": "Business Problem: Banks receives so many applications for credit card (CC) request. Going through each request manually can be very time consuming, also prone to human errors. However, if we can use the historical data to build a model which can shortlist the candidates for approval that save time.\n\nThe provided training dataset contains past applications for CC. Each row in the dataset includes information on the applicant  (sex,age,Debt, Married etc.)  and the output is the decision (1: approved, 0: denied) \n\n**Goal**\nBy using any ML classifier,  you must build a predictor model that predicts if a CC should be approved for a person that has certain characteristics (sex,age,Debt, Married etc.)",
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    {
      "name": "Evaluation",
      "content": "The prediction of your classifier will be evaluated by using *accuracy*:\n\n\n<p>\\[\nAcc = \\frac{1}{n}\\sum_{i=1}^{n}(y_i==\\hat{y}_i)\n\\]</p>\n where n is the number of instances.\n\n<h2>Submission Format</h2>\nThe submission file should be in text format (.csv). The submission file must have the following format:\n<p>\\[\n\\begin{array}{cc}\nId & Category\\\\\n0 & 1\\\\\n1 & 0\\\\\n2 & 1\\\\\n3 & 1\\\\\n4 & 0\\\\\n... & ...\\\\\n\\end{array}\n\\]</p>\n\nwhere *Id* is the Id of the person (the first column of the dataset) and Category is your prediction for that person (1: approved, 0:denied).  Be sure that  the values in the Id and Category column are integers (not float).\n",
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