{
  "fetched_at": "2026-10-24T09:21:02Z",
  "source_url": "https://www.kaggle.com/competitions/datawiz-2022-r1/data",
  "pages": [
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      "name": "data-description",
      "content": "In the dataset provided, you have been given all the data needed for assessing a patient's condition.\nYou will be required to predict the **Tumor Stage** of a particular patient.\n\n## File Descriptions\n\n*   **r1_train.csv** - the training set\n*   **r1_test.csv** - the test set\n*   **sample_solution.csv** - a sample submission file in the correct format\n\n## Data Fields\n\n*   Patient ID: A unique identification number assigned to every patient.\n*   Sample ID: The identification for samples collected from a particular patient.\n*   Tumor Stage: The stage of cancer the tumor is in.\n*   Type of Cancer: The type of cancer which the patient is suffering from.\n*   Verbose Type of Cancer: The type of cancer, including any subtypes, which the patient is suffering from.\n*   Sample Coverage: A measure of reliability of the sample taken.\n*   Patient Age: At what age the patient was diagnosed with cancer. \n*   Number of Samples per Patient: The number of samples that were taken from a patient.\n*   DNA Input: A metric calculated by sequencing the DNA of the patient and using research-based results to assess the severity of the cancer.\n*   Mutation Count: The number of Mutations observed in the patient’s cancer.\n*   Sample Type: The type of sample taken from the patient.\n*   Gender: Gender of the patient.\n*   Alcohol/Tobacco Consumption: Whether the patient consumed alcohol or tobacco.\n*   Specimen Preservation Type: The kind of preservation used to store the sample.\n*   Specimen Type: The type of specimen taken from the patient.\n*   TMB (nonsynonymous): The tumor mutation burden is the number of mutations in the cancer determined by next-*   generation gene-sequencing techniques.\n*   Anti Raster Probability: A measure of probability of response to given treatment.\n*   Oligo-Bertrand Symptoms: Set of symptoms for cancer stages as stated by Dr. Bertrand et al. in their research paper of 2014.\n*   Treatment: The form of treatment the patient is undergoing.\n*   Tumor Purity: The purity of the tumor present in the patient.\n*   OLPG Rate: The measure of coherence to Oligo-Bertrand Symptoms.\n",
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      "name": "Description",
      "content": "As a part of an internship in the UUPC Trust for cancer patients across India, you were hired as a data scientist to help the organization to identify patients in the more severe stages of the disease. The Chennai-based NGO has surveyed over 10,000 patient reports in India and has compiled all of the data in the given dataset. \nWith this dataset, you have to utilize machine learning algorithms and identify the patients’ cancer stage in order to aid the NGO to support the patients who have a greater need of healthcare.\n\nP.S: Head over to [credenz.in](https://credenz.in/) to register for Credenz if you haven't already!",
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    {
      "name": "Evaluation",
      "content": "#**Evaluation Metric**\nThe Evaluation Metric for this competition is **categorization accuracy**.",
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    {
      "name": "rules",
      "content": "Registered users are eligible for prizes only.  [register](https://credenz.in/)<br>\n1)   Each team of students may consist of a maximum of 3 participants.<br>\n2)  One account per participant.<br>\n3)  Signing In on Kaggle through Multiple accounts is prohibited.<br>\n4)  No private sharing outside teams.<br>\n5)  Privately sharing code or data outside of teams is not permitted.<br>\n6)  Results should be reproducible to be eligible for prizes.<br>\n\nAlso:) -<br>\n - Don't cheat!<br>\n - Apply yourself!<br>\n - Have fun!<br>",
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