{
  "fetched_at": "2026-10-19T07:21:29Z",
  "source_url": "https://www.kaggle.com/competitions/consumer-finance-complaints-prediction/data",
  "pages": [
    {
      "name": "data-description",
      "content": "There are 18 features in total. 'Product' feature is used as a target class.\n\n## Files\n\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## Attribute Information\n\n* `Date received` - The date the CFPB received the complaint.\n* `Product` - The type of product the consumer identified in the complaint.\n* `Sub-product` - The type of sub-product the consumer identified in the complaint.\n* `Issue` - The issue the consumer identified in the complaint.\n* `Sub-issue` - The sub-issue the consumer identified in the complaint.\n* `Consumer complaint narrative` - Consumer complaint narrative is the consumer-submitted description of \"what happened\" from the complaint. Consumers must opt-in to share their narrative. We will not publish the narrative unless the consumer consents, and consumers can opt-out at any time. The CFPB takes reasonable steps to scrub personal information from each complaint that could be used to identify the consumer.\n* `Company public response` - The company's optional, public-facing response to a consumer's complaint. Companies can choose to select a response from a pre-set list of options that will be posted on the public database. For example, \"Company believes the complaint is the result of an isolated error.\"\n* `Company` - The complaint is about this company.\n* `State` - The state of the mailing address provided by the consumer.\n* `ZIP code` - The mailing ZIP code provided by the consumer.\n* `Tags` - Data that supports easier searching and sorting of complaints submitted by or on behalf of consumers. \nFor example, complaints where the submitter reports the age of the consumer as 62 years or older are tagged, ‘Older American.’ Complaints submitted by or on behalf of a servicemember or the spouse or dependent of a servicemember are tagged, ‘Servicemember.’ Servicemember includes anyone who is active duty, National Guard, or Reservist, as well as anyone who previously served and is a Veteran or retiree.\n* `Consumer consent provided?` - Identifies whether the consumer opted in to publish their complaint narrative. We do not publish the narrative unless the consumer consents and consumers can opt-out at any time.\n* `Submitted via` - How the complaint was submitted to the CFPB.\n* `Date sent to company` - The date the CFPB sent the complaint to the company.\n* `Company response to consumer` - This is how the company responded. For example, \"Closed with explanation.\"\n* `Timely response?` - Whether the company gave a timely response.\n* `Consumer disputed?` - Whether the consumer disputed the company’s response.\n* `Complaint ID` - The unique identification number for a complaint.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "## Overview and Problem Statement\n\nThese are real world complaints received about financial products and services. Each complaint has been labeled with a specific product; therefore, this is a supervised text classification problem. With the aim to classify future complaints based on its content, we used different machine learning algorithms can make more accurate predictions (i.e., classify the complaint in one of the product categories)\n\nThe dataset contains different information of complaints that customers have made about multiple products and services in the financial sector, such as Credit Reports, Student Loans, Money Transfer, etc.\nThe date of each complaint ranges from November 2011 to May 2019. Can you reliably predict what type of product has complaints?\n\n## Goal:\n\nThe participant has to develop a classifier model which classifies the ‘Product' as, \n* 'Personal consumer reports'\n* 'Debt collection'\n* 'Mortgage'\n* 'Credit reporting'\n* 'Checking or savings account'\n* 'Credit card'\n* 'Bank account or service'\n* 'Student loan'\n* 'Money service'\n* 'Vehicle loan or lease'\n* 'Consumer Loan'\n* 'Personal loan'\n* 'Payday loan'\n* 'Money transfers'\n* 'Prepaid card'\n* 'Other financial service'\n* 'Virtual currency'",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "## Evaluation:\n\nYour score will be calculated using the [Mean F score](https://en.wikipedia.org/wiki/F-score) on your submission file.\n\n## Submission File:\n\nYou should submit a csv file with exactly entries plus a header row. Your submission will show an error if you have extra columns (beyond Complaint ID and Product) or rows.\n\nThe file should have exactly 2 columns:\n* Complaint ID: (order same as the test set)\n* Product: (contains your predictions)\n```\nComplaint ID,Product\n1,Credit card\n5,Mortgage\n13,Credit card\nEtc.\n```\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "## One account per participant\n\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## No private sharing outside teams\n\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n## Team Mergers\n\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\n## Team Limits\n\nThere can be 2 to 5 members in a team.\n\n## Submission Limits\n\nYou may submit a maximum of 1 entry only.\n\nYou may select 1 final submissions for judging.\n\n## Competition Timeline\n\nStart Date: 14/10/2022 12:00 AM IST\n\nMerger Deadline: None\n\nEntry Deadline: 13/10/2022 11:59 PM IST\n\nEnd Date: 16/10/2022 11:59 PM IST\n\n* We ask that you respect the spirit of the competition and do not cheat. You should not submit entries based on test-set answers or train your model on the test set. Hand labeling is also forbidden.\n* Your model should only use information which was available prior to the time for which it is forecasting.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}