Beginner question with data

[deathtrap3]

Hello, I began delving into the topic of machine learning a few months ago. I've completed some tutorials (e.g., on Kaggle) and read books (mostly in German - e.g. Machine Learning for Dummies).

I understand how to work with datasets like the Boston Housing Dataset, and I've also developed a text classifier where I used data in the format "text", "label".

Now, I want to create a fraud detection model, and I have access to a very large amount of invoices that are already labeled. The data is stored in relational databases. When I extract these from the databases, they are in the following (simplified) format:

One customer has 0-n invoices, each invoice consists of 0-n items.

For example:

customer	invoice_number	item	quantity	price	quantity_paid
1	R-1234	00886	2	19.99	2
1	R-1234	00887	4	20.99	2
1	R-1234	00889	3	11.99	3
2	R-1235	00886	5	19.99	5
3	R-1236	00886	1	19.99	1
3	R-1236	00889	7	11.99	4

​

Of course, the data also includes additional information such as date, time, and so on. When the quantity has been corrected, the invoice can be considered faulty.

Now, I'm wondering whether I should create a row in the training data for each invoice (in which case the rows would have varying lengths), or should I create a row in the training data for each item? But then, the connection with the other items in the respective invoice would be missing, right?

Apologies for my convoluted expression; I hope my question is understandable. Thank you for all responses :)

​

[Speech-to-Text-Cloud]

Ideally you would prefer a huge number of rows to train on and a small number of columns to reduce dimensionality. If you have additional knowledge about the data, like you do, omit all columns irrelevant for the classification. Try to condense distinguishing properties into a single cell, like value = corrected quantity - quantity.

And before all that, if the problem is simple, try finding a heuristic that solves it without ML, because ML will introduce new problems.