Need help in ascertaining the right ML technique



One of the client requirements that we have is to find out if the vehicle or accessory configuration being done for the current year ( a large set of attributes ) is wrong based on previous year configurations.

All of the attributes are textual in nature ( vehicle type information, vehicle color , specific vehicle parameters etc) . Can you please help suggest whether ML would help solve this.

Basically the ML output should prompt the user for any anomalies in the configuration being done.

All the required data can be imported as a textual file for the yeara analyzed


Few questions to understand your requirement.

  1. are you comparing the previous year configuration with current year configuration and trying to find out whether configuration is correct or not?
  2. are you trying to find whether each parameter is configured correctly or not
  3. are configuration parameters different for each vehicle or accessories
  4. what are the specific vehicle parameters
  5. In your data, do you have attribute which mentions whether configuration was correct or not

Also can you paste some sample data?

Based on the information provided by you, it looks like the problem here is…
to check whether each parameter is set correctly or not, if that’s true then, i think it’s just configuration set up problem.