I have unlabeled credit card transaction data that has the following columns:
Transaction_ID Frequency Amount Fees 192831 21 829 23 382912 14 920 24 483921 839 24059 87
Eventually, I'd like to build a deep learning model(e.g. LSTM) that can tell me whether a transaction(row) has a "high", "moderate", or "low" risk. However, since the data is unlabeled, I believe I need to label the data first before I feed the data into the deep learning model.
For example, transactions that have small frequency and amount values like the first two rows need to be labeled as "low (0)" while transactions that have large frequency and amount like the last row should be labeled as "high (2)". If both frequency and amount have moderate values, the row will be labeled as "moderate(1)".
I wonder if it is okay to use other machine learning techniques such as K-Means clustering to label the data before I feed the data into the deep learning model. Is it okay to use one Machine Learning algorithm (K-means) to label the data and feed the same labeled data into another Deep Learning model (LSTM)? Or is it a bad practice? For example, if the first model (K-means) is biased, will that bias(error) be carried over from the first model to the second model (LSTM)?
If it is a bad practice to use two different ML technologies, what else can I do to label the data?