{
  "fetched_at": "2026-10-19T22:05:03Z",
  "source_url": "https://www.kaggle.com/competitions/cis-data-mining/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\n#### **In this competition, you will predict the `Response` You are provided with a dataset containing 12 features including the target variable.**\n\n# Files descriptions:\n*   **dataset_description.txt** - the dataset columns description.\n*   **train.csv** - the training set.\n*   **test.csv** - the test set.\n*   **sample_submission.csv** - a sample submission file in the correct format.\n\n\n",
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    {
      "name": "Description",
      "content": "\n## Problem description\n**Insurance companies that sell life, health, property, and casualty insurance are using machine learning (ML) to drive improvements in customer service, fraud detection, and operational efficiency.**\n\n**CMB company provides Health Insurance to its customers. We can build a model to predict whether the customers from past year will also be interested in Vehicle Insurance provided by the company.**\n\n**In this problem,  we invite you to help CMB company predict the binary class Responses.**\n\n\n## Instruction: \n\n1. If you don't have Kaggle account, create yours using the link:\nhttps://www.kaggle.com/account/login?phase=startRegisterTab&returnUrl=%2F\n\n2. Download the train.csv, test.csv, sample solution, and dataset_description.txt.\n\n\n3. Read the dataset_description.txt to understand the data you're dealing with.\n\n4. train.csv is the data you will use to build your model.\n\n5. Build your model. You may use any classification model, for example, a Decision tree or Nearest Neighbors. See labs #4 and #5 on elearning.\n\n6. Read test.csv, and predict the data using the model you built in step 5.\n\n7. Create a data frame with two columns `id` and `price_range`. The `price_range` is the predictions done in step 6.\n\n8. Save the data frame (done in step 7) in the `submission.csv` file.\n\n9. Submit `submission.csv`, and see your score.\n",
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    {
      "name": "Evaluation",
      "content": "#### **Submissions are evaluated on area under the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) between the predicted probability and the observed target.**\n\n\n## Submission Format\n\nFor each Id in the test set, you must make a binary prediction of the Response variable. The file should contain a header and have the following format:\n```\nid, Response\n0, 0\n1, 1\n2, 1\netc.\n\n```",
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    {
      "name": "rules",
      "content": "## **One account per participant**\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## **No private sharing**\nPrivately sharing code or data is not permitted. It's okay to share code if made available to all participants on the forums.\n\n",
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