{
  "fetched_at": "2026-10-19T13:35:25Z",
  "source_url": "https://www.kaggle.com/competitions/ais117v-2022-s1/data",
  "pages": [
    {
      "name": "data-description",
      "content": "The data provided by STEG is composed of two files. The first one is comprised of client data and the second one contains billing history since 2005.\nFiles available for download\n\nThere are 4 files for download:\n    \n    client_train_v2.csv - Client information in the train population\n\n    invoice_train_v2.csv - Clients invoice in the train set\n\n    client_test_v2.csv - Client information for the test population\n\n    invoice_test_v2.csv - Clients invoice in the test set\n\n\nVariable definitions\n\nClient:\n\n    Client_id: Unique id for client\n    District: District where the client is\n    Client_catg: Category client belongs to\n    Region: Area where the client is\n    Creation_date: Date client joined\n    Target: fraud:1 , not fraud: 0\n\nInvoice data\n\n    Client_id: Unique id for the client\n    Invoice_date: Date of the invoice\n    Tarif_type: Type of tax\n    Counter_number:\n    Counter_statue: takes up to 5 values such as working fine, not working, on hold statue, ect\n    Counter_code:\n    Reading_remarque: notes that the STEG agent takes during his visit to the client (e.g: If the counter shows something wrong, the agent gives a bad score)\n    Counter_coefficient: An additional coefficient to be added when standard consumption is exceeded\n    Consommation_level_1: Consumption_level_1\n    Consommation_level_2: Consumption_level_2\n    Consommation_level_3: Consumption_level_3\n    Consommation_level_4: Consumption_level_4\n    Old_index: Old index\n    New_index: New index\n    Months_number: Month number\n    Counter_type: Type of counter\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "The Tunisian Company of Electricity and Gas (STEG) is a public and a non-administrative company, it is responsible for delivering electricity and gas across Tunisia. The company suffered tremendous losses in the order of 200 million Tunisian Dinars due to fraudulent manipulations of meters by consumers.\n\nUsing the client’s billing history, the aim of the challenge is to detect and recognize clients involved in fraudulent activities.\n\nThe solution will enhance the company’s revenues and reduce the losses caused by such fraudulent activities. \n\n# Rules\n\n## Teams and collaboration\n\nYou may participate in this competition as an individual or in a team of up to 2 people. \n\nMultiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.\n\nCode must not be shared privately outside of a team. \n\n## Datasets and packages\n\nThe solution must use publicly-available, open-source packages only. \n\nYou may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.\n\nThe data used in this competition is the sole property of Zindi. You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.\n\nYour solution must not infringe the rights of any third party.\n\n## Submissions and winning\n\nYou may make a maximum of 15 submissions per day.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top; the page title appears above. For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have included below:\n\n---\n\nThe evaluation metric for this competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\( \\text{p} \\) and recall \\( \\text{r} \\).\n\nPrecision is the ratio of true positives \\(\\text{tp}\\) to all predicted positives \\(\\text{tp} + \\text{fp}\\). Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\n\\[ \\text{F1} = 2\\frac{\\text{p} \\cdot \\text{r}}{\\text{p}+\\text{r}}\\ \\ \\mathrm{where}\\ \\ \\text{p} = \\frac{\\text{tp}}{\\text{tp}+\\text{fp}},\\ \\ \\text{r} = \\frac{\\text{tp}}{\\text{tp}+\\text{fn}} \\]\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\n## Submission Format\n\n**For every author in the dataset**, submission files should contain two columns: `client` and `target`. `target` should be 0 or 1.\n\n\nThe file should contain a header and have the following format:\n```\nclient,target\n30423,0\n21031,1\n43535,0\n44854,0\n23604,0\n\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}