{
  "fetched_at": "2026-10-20T05:07:37Z",
  "source_url": "https://www.kaggle.com/competitions/cia1-mtech/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\n*   **Customer Churn train.csv** - the training set\n*   **Customer Churn test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format.\n\n## Other details\nYou have to perform necessary EDA before building the models. All the columns are self explanatory.",
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    {
      "name": "Evaluation",
      "content": "Submissions are evaluated on the accuracy score between the actual data and the predicted data.",
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    {
      "name": "rules",
      "content": "Have fun!",
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    {
      "name": "abstract",
      "content": "Welcome to the Great Kirigalan .. **End semester lab exam..** \n\n**Your Goal**: To predict whether a customer continues with their account or closes it (e.g., churns). You are expected to perform the necessary preprocessings and hyperparameter tuning and submit the file as given in the sample submission.\n\n**NOTE1**: you can make only 3 final submissions to this competition, however, you can modify the code locally any number of times. Your procedure and approach will also be considered towards final scoring apart from the accuracy that your model gets.\n\n**NOTE2** : The probability that 2 people get the same accuracy is 0% (because of the preprocessing.) If 2 or more people get the same accuracy upto 4th digit, it means you have copied from each other. Hence, those scores will be discarded and you will be penalized.\n\nAll the best!!😄",
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    {
      "name": "Submission File",
      "content": "For each `customer_id` in the test dataset, you must predict the target label for `churn`. The submission file should contain only 2 columns - `customer_id` and `churn` as shown below:\n\n| customer_id| churn |\n| 165034 | 0 |\n| 165035 | 1  |\n",
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