I have a customers table with 2 columns for the name (firstname, lastname) and it contains around 100k records.
I have a scenario where I have to import new customers but their names come as a single column. Most names are simple (first and last), but some names are double names (with a space or hyphen), double surnames (with a space or hyphen) or even both.
Does using a ML.NET classification algorithm make sense to split the fullname based on a trained model from the 100k records?
I think it would be unnecessary to use machine learning methods for such a problem. You should try a rule-based method here.
Assuming the data comes in 1 column: For example: After splitting the Text by space, is the length of the word count equal to 2? If equal, the 1st word is the name and the 2nd word is the surname.
Example 2: Does the text contain hyphen or not? If yes, what should I do? How can I determine my name and surname?
1-) What you need to do here is to create a training, validation and test set for yourself.
2-) Doing a coding with the rules you extracted from the data in the train set. (Here you need to make clever deductions by examining the data)
3-) You need to determine the most ideal rules with validation data. Finally, you should evaluate your work by getting results on the test set with the rule you find most ideal.
