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An application of a classification problem - Fraud Detection

Helping Nigeria
TBD
Challenge completed over 2 years ago
Classification
67 joined
28 active
Starti
Jun 01, 23
Closei
Jun 10, 23
Reveali
Jun 10, 23
About

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.

Client:

  • Client_id: Unique id for client
  • District: District where the client is
  • Client_catg: Category client belongs to
  • Region: Area where the client is
  • Creation_date: Date client joined
  • Target: fraud:1 , not fraud: 0

Invoice data

  • Client_id: Unique id for the client
  • Invoice_date: Date of the invoice
  • Tarif_type: Type of tax
  • Counter_number:
  • Counter_statue: takes up to 5 values such as working fine, not working, on hold statue, ect
  • Counter_code:
  • 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)
  • Counter_coefficient: An additional coefficient to be added when standard consumption is exceeded
  • Consommation_level_1: Consumption_level_1
  • Consommation_level_2: Consumption_level_2
  • Consommation_level_3: Consumption_level_3
  • Consommation_level_4: Consumption_level_4
  • Old_index: Old index
  • New_index: New index
  • Months_number: Month number
  • Counter_type: Type of counter
Files
Description
Files
You will use these files to train your model.
You will use these files to test your model.
Is an example of what your submission file should look like. The order of the rows does not matter, but the names of the IDs must be correct. The column "target" is your prediction.